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Federal Reserve Bank of Chicago

Technology Shocks Matter
Jonas D. M. Fisher

WP 2002-14

Technology Shocks Matter
Jonas D.M. Fisher∗
Federal Reserve Bank of Chicago
Current Draft: December, 2003

Abstract
I use the neoclassical growth model to identify the effects of technology shocks on the
US business cycle. The model includes two sources of technology shocks: neutral, which
affect the production of all goods homogeneously, and investment-specific. Investmentspecific shocks are the unique source of the secular trend in the real price of investment
goods, while both shocks are the only factors which affect labor productivity in the
long run. Consistent with previous empirical work which considers only neutral shocks,
the results suggest these shocks account for little, about 6 percent, of the business cycle
variation in hours worked. In contrast, investment-specific shocks account for about 50
percent, a new finding which suggests that technology shocks are an important source
of the business cycle.

∗

Thanks to Lisa Barrow, Jeffrey Campbell, Lawrence Christiano, Martin Eichenbaum, Charles Evans,
John Fernald, Jordi Galí, Martin Gervais and Lars Hansen for helpful conversations. Thanks also to Jason
Cummins and Gianluca Violante for their data. Any views expressed herein do not necessarily reflect the
views of the Federal Reserve Bank of Chicago or the Federal Reserve System.

1. Introduction
This paper shows that investment-specific technology shocks account for a significant fraction
of the business cycle. In doing so it overturns recent conclusions, based on research which
focuses on neutral technological change, that technology shocks are unimportant for the
business cycle.
Traditional estimates of the technology-driven business cycle are based on transitory neutral technology shocks derived from Solow residuals. Many researchers view these estimates
to be implausibly large since Solow residuals are an error-ridden measure of neutral technology over short horizons. If transitory shocks are implausible, and one only considers
neutral technological change, then permanent neutral shocks are the remaining source for a
technology-driven business cycle. Permanent neutral shocks can be identified without relying on Solow residuals, under the assumption that they are the only source of permanent
shocks to labor productivity. Galí (1999) and a growing literature use this approach and
find that technology shocks account for very little of the business cycle. The robustness of
this finding and the likely irrelevance of transitory shocks suggest that technology shocks are
unimportant for the business cycle.1
The contribution of this paper is to demonstrate that the data strongly suggest otherwise. Permanent technology shocks matter after all, if one also considers investment-specific
technological change. The findings in Greenwood, Hercowitz and Krusell (1997) motivate
considering this additional source of technological change. Using evidence on the secular
decline in the real investment price, these authors show that investment-specific, not neutral, technological change, is the major source of economic growth. From the real business
cycle perspective, if investment-specific technological change is important for growth, it could
also be important for the business cycle.
1
For a recent examination of the difficulties involved with measuring transitory fluctuations in neutral
technology, see Basu and Fernald (2001, 2002). Figure 6, p. 268 is the clearest statement of the finding that
technology shocks do not matter in Gali (1999). Recent papers by Francis and Ramey (2001) and Christiano,
Eichenbaum and Vigfusson (2002), Gali (2003) confirm the finding. This research builds on Blanchard and
Quah (1989), King, Plosser, Stock and Watson (1991), and Shapiro and Watson (1988).

1

To assess this possibility, I adapt the framework that has been used to estimate the contribution of permanent neutral technology shocks to the business cycle. First, consistent with
the neoclassical growth model, I modify the standard assumption that neutral technological
change is the only source of long run changes in labor productivity to include the possibility
that investment-specific change also has an effect. Second, I impose the additional restriction, also implied by the growth model, that investment-specific technological change is the
unique source of the secular trend in the real price of investment goods. These assumptions
are sufficient to identify the business cycle effects of both kinds of technological change.
When I impose these assumptions on US data I reproduce the standard result that neutral
technological change is relatively unimportant for explaining the business cycle, accounting
for less than 10 percent of business cycle variation in hours worked. In contrast, I also find
that investment-specific technological change accounts for about 50 percent of this variation.
Therefore, in total, technology shocks account for a significant fraction of the business cycle
variation in hours worked. I show that this basic finding is robust to many perturbations of
the analysis, including alternative empirical specifications and sample periods. In addition,
I show that the shocks I identify are unrelated to other variables, such as capital taxes, that
might have affected labor productivity and the real investment price.
The remainder of the paper is as follows. In the next section I use the neoclassical growth
model to derive the identification assumptions at the heart of the analysis and show how
they can be used to identify the affects of technology shocks. After this I discuss the data
used in the analysis, present the main empirical findings and evaluate the robustness of these
findings. Finally, I summarize and suggest directions for future research.

2. Empirical Framework
In this section I describe how the identifying assumptions exploited in the empirical analysis
can be derived from a neoclassical growth model. The first sub-section describes the economic
model. This model is deliberately stripped down to make the discussion as transparent as
possible. The second sub-section describes how the identifying assumptions derived from
2

the model can be implemented econometrically. The econometric strategy is consistent with
a broad class of models which share the same balanced growth properties as the model
considered here.
2.1. Economic Model
The model is adapted from the competitive equilibrium growth model of Greenwood, et. al.
(1997). In this model the welfare theorems hold so it is sufficient to explain the problem of
the social planner. The planner chooses consumption, Ct , investment, Xt , hours worked, Ht
and next period’s capital stock, Kt+1 to solve

max E0

∞
X

β t U(Ct , Ht )

(1)

t=0

subject to
Ct + Xt ≤ At Ktα Ht1−α , α ∈ (0, 1)

(2)

Kt+1 ≤ (1 − δ)Kt + Vt Xt , K0 given, δ ∈ (0, 1)

(3)

and
At = exp(γ + Ca (L)εat )At−1 , γ ≥ 0
Vt = exp(ν + Cv (L)εvt )Vt−1 , ν ≥ 0

(4)

[εat , εvt ]0 ∼ N(0, D), D diagonal
Here E0 is the expectations operator conditional on time t = 0 information, U(·, ·) is a utility
function consistent with balanced growth, β is the planner’s discount factor, At is the level
of neutral technology, Vt is the level of investment-specific technology and Ca (L) and Cv (L)
are square summable polynomials in the lag operator L.
The differences with Greenwood, et. al. (1997) are (i) there is only one capital good
and (ii) exogenous technology has a stochastic instead of a deterministic trend. The first
difference is not crucial to the argument. Difference (ii) is substantial because it is the
reason why technology shocks have permanent effects in this model. Many real business
cycle models assume a stochastic trend in technology, but much of the emphasis in this
3

research is on persistent, but transitory technology shocks. The econometric identification
used below, which follows the standard methodology, does not consider transitory technology
shocks.
Galí (1999) argues that the stochastic trend assumption is natural when thinking about
purely technological disturbances. More substantially Alvarez and Jermann (2002) present
compelling evidence that without a unit root, it is difficult to resolve data on asset prices
with economic theory. Yet, a plausible interpretation regarding the neutral term is that
it represents “productivity” shocks, disturbances to production possibilities more generally
conceived and not necessarily due to technological change, such as taxes, regulation and
market structure. These disturbances may very well be transitory. By ignoring transitory
effects I am presenting a lower bound on the contribution to business cycles of technology
shocks more generally conceived.
The long run implications of the model are found by considering its balanced growth
properties. The balanced growth path is derived from the unique transformation of the
endogenous variables which renders them stationary. It is straightforward to confirm that
along a balanced growth path the following variables are stationary:

Yt /Zt Ct /Zt , Xt /Zt , Kt+1 /(Zt Vt ), (Yt /Ht )/Zt and Ht ,
1/(1−α)

where Yt = Ct + Xt and Zt = At

α/(1−α)

Vt

(5)

. Therefore, along a balanced growth path,

output, consumption, investment and labor productivity (all measured in consumption units)
on average each grow at the rate (γ + αν)/(1 − α). In addition, the capital stock grows at
the rate (γ + ν)/(1 − α), and hours worked is stationary.
From (5) it is immediate that a positive innovation to neutral or investment-specific
technology leads to permanent increases in output, consumption, investment, the capital
stock, and labor productivity, but has no effect on hours worked in the long run. Most
important for what follows is the fact that both sources of technological change influence

4

labor productivity in the long run. That is, at any time t
∂ ln Yt+j /Ht+j
α
∂ ln Yt+j /Ht+j
1
=
> 0 and lim
=
> 0.
j→∞
j→∞
∂ευt
1−α
∂εat
1−α
lim

(6)

This is different from a model without investment-specific technological change or if investmentspecific shocks are stationary about a deterministic trend, because in such a model only
neutral technological change influences labor productivity in the long run.
Notice from (2) and (3) that the number of consumption units that must be given up
to get an additional unit of capital is 1/Vt . That is, in the competitive equilibrium of this
economy, the real price of investment, Pt , is 1/Vt . Obviously, the only shock in this economy
that has a permanent effect on Pt is an innovation to Vt . Neutral technological change has no
impact on the technical rate of transformation between consumption goods and investment
goods and therefore on the real price of investment. Mathematically,
∂ ln Pt+j
∂ ln Pt+j
= −1 < 0 and lim
= 0.
j→∞
j→∞
∂ευt
∂εat
lim

(7)

The simple growth model implies that the real investment price is completely determined
by the level of investment-specific technology. In a more general (and realistic) model with
curvature in the transformation frontier of producing investment and consumption goods, for
example the two-sector model studied by Boldrin, Christiano and Fisher (2001), this would
not be true. However, such a model, if it is consistent with balanced growth, would continue
to have the property that the only factor influencing the real price in the long run would be
investment-specific technological change.2
It is straightforward to extend the simple growth model in other ways to incorporate many
alternative propagation mechanisms, including models with money and sticky prices and
2

Greenwood, et. al. (1997) consider and reject several alternative mechanisms which in principle might
account for the secular trend in the real investment price instead of investment-specific technological change.
One important example they consider is different factor shares for the investment and consumption good
sectors in a two-sector model. If these shares are of the right magnitude then there could be a secular trend
in the real price without one in investment-specific technology. Greenwood, et. al. (1997) argued that this
hypothesis requires assuming implausible parameter values.

5

wages. These models can also have other exogenous shocks, as long as they have transitory
effects only. That is as long as,
∂ ln Yt+j /Ht+j
∂ ln Pt+j
= 0 and lim
= 0,
j→∞
j→∞
∂εxt
∂εxt
lim

(8)

for all other shocks x. In these alternative models there may be other endogenous variables,
but the balanced growth properties given in (5) will be the same, as long as technological
change is neutral and investment-specific.
At this stage of the analysis one could specify a parameterized theoretical model which
matched several interesting unconditional moments in the data and use this model to assess
the contribution of technology shocks to the business cycle. The earliest paper to do this
with investment-specific shocks was Greenwood, Hercowitz and Huffman (1988). Other
papers which have considered the business cycle implications of investment-specific shocks
are Campbell (1998), Christiano and Fisher (1998), Fisher (1997), and Greenwood, Hercowitz
and Krusell (2000). As we have learned from the literature on neutral technology shocks, the
contribution of technology shocks to the business cycle derived from quantitative business
cycle models may not correspond with that derived from an econometric analysis which takes
a weaker stand on the nature of the propagation mechanism. This helps to motivate using
the method described in the next section to identify the importance of technology shocks for
the business cycle.
2.2. Econometric Strategy
Two basic assumptions summarize the class of models for which (6)-(8) hold. In words, these
assumptions are:
Assumption A1. Only shocks to exogenous investment-specific technology affect the real
investment price in the long run;
Assumption A2. Only shocks to exogenous neutral or investment-specific technology affect
labor productivity in the long run.
6

In addition to these assumptions, I define a “shock” to be a statistically independent innovation. I now describe how these assumptions can be used to identify the dynamic responses of
variables to exogenous neutral and investment-specific technology shocks within the standard
econometric framework.
The econometric identification strategy in which I incorporate A1 and A2 is a simple
adaptation of Shapiro and Watson (1988).3 To begin, the equilibrium of the economic model
(and its admissible generalizations) has a moving average representation,

yt = Φ(L)εt

(9)

where yt is an n × 1 vector of states and controls and εt is a vector of fundamental shocks
with εvt and εat as the first two elements, Eεt ε0t = Ω, where Ω is a diagonal matrix, Φ(L)
is a matrix of polynomials in the lag operator L. The elements of yt are [∆pt , ∆at , ht , qt ]0 ,
where pt is the log of the real investment price, at is the log of labor productivity, ht is the
log of per capita hours worked, qt is a vector of other endogenous variables in the model,
and ∆ = 1 − L.
Under the assumption that Φ(L) is invertible (typically the case in real business cycle
models) we may recover the exogenous shocks εvt and εat with two instrumental variables
(IV) regressions. First, consider

∆pt = Γpp (L)∆pt−1 + Γpa (L)∆at + Γph (L)ht + Γpq (L)qt + εvt ,

(10)

where the Γxy (L)’s here and below are lag polynomials derived by inverting Φ(L). This
equation relates changes in the real investment price to current and lagged values of the
endogenous variables. The contemporaneous effects of all non-εvt shocks effect the current
∆pt through ∆at , ht and qt . By assumption A1, it follows that the long run multipliers from
3

See Basu and Fernald (1998) for a different identification strategy which does not rely on long run
restrictions. At this stage it is unclear what the implications of investment-specific technological change are
for their approach.

7

these variables to the real price are zero. Imposing this restriction is the same as imposing
a unit root in each of the lag polynomials associated with ∆at , ht and qt . That is,

Γpj (L) = Γ̃pj (L)(1 − L), j = a, h, q .
As long as Γ̃pj (1) 6= 0, j = a, h, q, it follows under assumption A1 that (10) becomes
∆pt = Γpp (L)∆pt−1 + Γ̃pa (L)∆2 at + Γ̃ph (L)∆ht + Γ̃pq (L)∆qt + εvt .

(11)

Innovations to the real investment price may affect the contemporaneous values of ∆at , ht
and qt . Consequently this equation cannot be estimated by ordinary least squares. However,
given that εvt is exogenous, this shock is orthogonal to all variables dated t − 1 and earlier.
Hence we can estimate (11) by IV, using lagged values of yt as instruments. The residuals
of this estimation are my estimates of εvt , ε̂vt .4
The equation used to identify the neutral shock is

∆at = Γap (L)∆pt−1 + Γaa (L)∆at−1 + Γah (L)ht + Γaq (L)qt + θεvt + εat .

(12)

Here contemporaneous non-εat shocks influence ∆at indirectly through ht , qt and directly
through εvt . By a similar argument to before, assumption A2 implies the long run multipliers
from ht and qt to ∆at are zero, but that the long run multiplier from εvt is non-zero. When
these assumptions are incorporated into (12),

∆at = Γap (L)∆pt−1 + Γaa (L)∆at−1 + Γ̃ah (L)∆ht + Γ̃pq (L)∆qt + θεvt + εat .

(13)

Notice that (13) implies εvt can have a long run impact on the level of at if neutral technological change has a long run impact. Similar to (11) this equation can be estimated by
IV using lagged values of yt as instruments. The residuals from (13), ε̂at are my estimates
4

Notice that the identification of the investment-specific shock does not rely on the orthogonality of this
shock with the contemporaneous neutral shock.

8

of εat . Including ε̂vt in (13) ensures ε̂at is orthogonal to the investment-specific shock within
the sample period.
These steps for estimating the neutral shock differ from the existing literature because
the real price and the investment-specific shock are included in (13). This difference is
entirely due to the difference of assumption A2 from the usual identifying assumption that
only neutral technological change has a long run impact on labor productivity. By omitting
∆pt−1 and εvt from (13), estimates of the coefficients on ∆at−1 , ∆ht and ∆qt are likely biased.
The ultimate impact of this bias on the estimates of εat and estimated dynamic responses
of variables to this shock is unclear at this stage. However, even if previous estimates of εat
are valid and therefore the finding that these shocks are unimportant for the business cycle
is correct, there is still scope for technology shocks to impact the business cycle through εvt .
Estimating (11) and (13) by IV yields estimates of the shocks, ε̂vt and ε̂at . I am interested
in identifying the dynamic responses of the variables in yt to each shock and decomposing
the sample path of any element of yt into parts due to the two shocks. This is done efficiently
by estimating the reduced form vector-autoregression,
yt = A(L)yt−1 + ut , E0 ut u0t = Σ,

(14)

where A(L) is a square-summable matrix lag polynomial. The reduced form residuals are
linearly related to the fundamental shocks εt in the linear approximation to a given real
business cycle model through a matrix B,
ut = Bεt , BB 0 = Σ.

The first two elements of εt are ε̂vt and ε̂at . Therefore, to derive the dynamic responses of
interest we need only estimate the first two columns of B. Since the first two elements of
εt are orthogonal to the other elements in the vector we can do this by applying ordinary
least squares to the n equations with each residual from (14) on the left hand side of the
equation and the two estimated shocks on the right hand side. Finally, to derive the response
9

of any variable in yt to either of the two technology shocks we can simulate (14) using the
appropriate elements of B. We can also use these estimates to decompose the sample path
of any variable in yt into parts due to the two identified shocks.5

3. The Data
This section describes the data used in the empirical analysis. I begin by describing the
real investment price data, which is a crucial input into the empirical analysis. After this
I discuss the specific variables included in the application of the econometric model to US
data.
3.1. The Real Price of Investment
As is standard, I measure the real price of investment as the ratio of a suitably chosen
investment deflator and a deflator for consumption derived from the National Income and
Product Accounts (NIPA). The consumption deflator I use corresponds to nondurable and
service consumption, the service flow from consumer durables and government consumption.
The NIPA investment deflators are well-known to be poorly measured, so finding a suitable
investment deflator is the main challenge to constructing a real investment price. In this
subsection I describe the choices underlying my chosen deflator and then I describe the
resulting real price.
Poor measurement is one reason why the importance of investment-specific technological
5

This procedure yields identical results to the following adaptation of the methodology in Blanchard and
Quah (1989), as long as the number of lags in (10) and (12) are identical to the lags in (14). The long run
effects of the system (14) are summarized by
yt = [I − A(1)]−1 Bεt = Φ(1)Bεt .
The preceding discussion implies that the first two rows of Φ(1)B have zeros everywhere except in the
first element of the first row and first two elements of the second row. It follows that to estimate B
we can follow four simple steps. First, estimate the unconstrained vector-autoregression (14). Second,
use the estimated coefficients to compute A(1). Third, compute the lower triangular Choleski matrix, C,
so that CC 0 = Φ(1)ΣΦ(1)0 . Fourth, use the fact that Φ(1)B is a factor of Φ(1)−1 ΣΦ(1)−10 to solve for
B = Φ(1)−1 C. Using the first two columns of B one can compute the dynamic response functions and
historical decompositions of interest.

10

change has taken so long to be recognized. Gordon (1989) argued forcefully that the NIPA
producer durable equipment deflator was seriously miss-measured because of its treatment of
quality change. Through exhaustive use of primary sources he formulated a quality adjusted
deflator. However, it was not until Greenwood, et. al. (1997) that the full import of these
adjustments were recognized. They showed that the real price of producer durable equipment
derived using Gordon’s deflator has a pronounced downward secular trend. Based on a model
similar to the one in section 2.1 they showed that the technological change implied by this
trend accounts for about 58 percent of output growth between 1954 and 1990.
To arrive at their estimates Greenwood, et. al. (1997) extended Gordon’s original sample,
which ends in 1983, with a rough bias adjustment to the NIPA data. Cummins and Violante
(2002) derived a more systematic update of the Gordon data. They estimated econometric models of the bias adjustment implicit in the individual deflators underlying Gordon’s
equipment deflator and used these models to bias adjust the NIPA data for the period 19842000. Their findings confirm the Greenwood, et. al. (1997) result that investment-specific
technological change is a major source of growth.
I initially consider two investment deflators based on the Gordon-Cummins-Violante
(GCV) equipment deflator. The first measure is “equipment” and is derived directly from
the GCV equipment deflator. The second is “total investment” which is a broader measure
but still includes the GCV deflator along with other NIPA deflators. This is designed to
correspond to the investment measures typically used in real business cycle studies.
To be used in the econometric analysis, these deflators need to be quarterly series.
Since the GCV series is an annual series it must be interpolated. There is no generally agreed on method of interpolation. Here I use the popular approach due to Denton
(1971). As shown by Fernandez (1981), this method fits within the generalized least squares
interpolation-by-related-series class of interpolation schemes introduced by Chow and Lin
(1971). Interpolation-by-related-series uses information in a higher frequency indicator variable to interpolate a better quality but lower frequency variable. Denton’s version of this
method minimizes the squared differences of successive ratios of the interpolated to the in11

dicator series subject to the constraint that the sum or average of the interpolated series
equals the value in the annual series.6
My equipment-specific deflator is the annual GCV deflator interpolated with the NIPA
equipment deflator under the assumption that the average price for the year must equal the
GCV annual deflator. My total investment deflator is derived by using the appropriate chainweighting formula to combine my equipment-specific deflator with NIPA based deflators
for non-residential structures, residential structures, consumer durables, and government
investment.7 The Denton method retains the low frequency information in the higher quality
series and so any error in the interpolation underlying these deflators should have little
consequence for the identification of the technology shocks, which relies on the low frequency
part of the data.
Figure 1 displays the two investment deflators and the associated real investment prices,
along with series based only on the NIPA accounts for the sample period 1955:I-2000:IV.8
In each plot the solid line corresponds to the GCV based measure and the dashed line
corresponds to the NIPA measures, both in logs. This figure is helpful for making three
basic points. First, the quality bias in the NIPA deflators is large making them unsuitable
for constructing real prices. Second, consistent with Cummins and Violante (2002) and
Greenwood, et. al. (1997), equipment-specific technological change has been substantial
since 1955. The data indicate a 200 percent drop in the real equipment price. Third, the
6

Denton’s method is used by the IMF in their official statistics. When the related series is a good indicator
the practical differences among the available methods are small. An extensive discussion of alternative
interpolation methods can be found in Handbook of Quarterly National Accounts Compliation. This can be
currently viewed at www.imf.org/external/pubs/ft/qna/2000/Textbook/index.htm
7
Gordon estimates an annual quality adjusted consumer durables deflator which also indicates considerable quality bias in the corresponding NIPA deflator. For the consumer durable deflator used to construct
the total investment deflator I interpolate the annual Gordon consumer durable deflator using the NIPA
deflator as the related series for the period 1947-1983 and splice this to the NIPA deflator for the remaining
years of my sample.
8
I exclude data before 1955 for three main reasons. First, it is common in the real business cycle literature
to focus on the post-Korean war era (see for example, Prescott 1986). Second, I am more concerned about
the quality of my interpolations before 1955 because I believe the quality bias in the NIPA data may be
stronger than later in the sample. Third, estimates of the magnitude of the impact of neutral technology
shocks on the business cycle appear to be sensitive to including variables associated with monetary policy.
For empirical studies associated with US monetary policy it is customary to select dates for the beginning
of the sample period of analysis that are several years after the Treasury Accord of 1951.

12

real total investment price declines by much less, but still has a secular trend. The weaker
trend in this price may be due to a slower rate of quality change in non-equipment investment
or it may be due to the fact that the deflators for these investment goods have not been
quality adjusted.9
It is useful to briefly consider why the dynamics of the real price and quantity data
strongly suggests investment-specific technological change is important for understanding
both business cycles and growth, even prior to any econometric analysis. Figure 2 shows the
long run and short run association between real investment prices and quantities (similar
figures appear in Greenwood, et. al. 1997). The left column reproduces the GCV based
real prices from figure 1 along with ratios of the quantity of investment to output with the
latter in consumption units. These plots show that the real price declines coincide with large
increases in the relative quantity of investment goods produced, illustrating the importance
of investment-specific technological change for capital accumulation and hence growth.
The derivation in section 2.1 is based on the balanced growth hypothesis. The consistency
of the two investment series for this hypothesis can be assessed by observing that the sum
of the real price and real share series must be stationary under balanced growth. By this
test equipment is not a good measure of the object called investment in the growth model,
because the sum of the two series in the top left column has an upward trend. The presence
of this trend can be rendered consistent with a neoclassical growth model if we extend the
notion of balanced growth as in Kongsamut, Rebelo and Xie (2001). Notice that while the
evidence on equipment investment is inconsistent with the growth model as written down in
section 2.1, total investment is consistent, since the sum of the two series in the lower left
column is stationary.
The right column displays the business cycle components of real investment prices and
quantities. These plots confirm the observation of Greenwood, et. al. (1997, 2000) that, even
in the short run, investment-specific supply shocks seem to be closely related to investment.
9

Gort, Greenwood, and Rupert (1999) estimate significant quality bias in the NIPA deflators for nonresidential structures.

13

In figure 2, the business cycle components are derived using the Baxter and King (1999)
band-pass filter which excludes frequencies higher than one and a half years and frequencies
lower than eight years.10 The top right plot shows a negative relationship between the
investment prices and quantities, strongly suggesting a role for cost-reducing technology
shocks. This negative relationship is apparent throughout the sample. The unconditional
correlation is -0.67. The relationship with total investment is not as strong (the correlation
is -0.41). Some of the difference may be due to measurement error, but the likelihood that
residential investment has a strong “demand”-driven component probably plays a role as
well. Still, overall the data suggest that investment-specific technology shocks play a key
role in the business cycle.
3.2. Variables in the Econometric Application
Figure 3 displays the variables underlying the main results presented in the next section.
Implementing the econometric model in section 2.2 requires only the growth rate of the real
investment price, the growth rate of average labor productivity, and per capita hours worked.
The variables used to measure these objects are plotted in the left column of figure 3. The
equipment price is my measure of the real price because it is presumed to be more precisely
measured than the total investment price.11 Results based on the total investment price are
similar. Labor productivity is the non-farm business measure published by the Bureau of
Labor Statistics (BLS). Per capita hours (in logs) is the BLS hours worked (not hours paid)
measure corresponding to the productivity measure, divided by the working age population.
To retain consistency with the growth model I express labor productivity in consumption
units per hour using my consumption deflator. The first two variables, real price growth and
productivity growth are clearly stationary. However, the stationarity of per capita hours is
10

To implement the Baxter and King filter I must select the number of lags (leads) in the filter. I use their
suggested value of 12 quarters.
11
The sharp changes in the real equipment price in 1973-74 are an artifact of the impact of the Nixon wage
and price controls on the construction of the equipment deflator (see Cummins and Violante 2002). Since
this is a transitory phenomenon it should not affect the identification of the investment-specific technology
shocks.

14

a matter of controversy in the literature and so some discussion of this is necessary.
Francis and Ramey (2001) argue that, since standard tests of the null hypothesis of a unit
root (e.g. Perron and Phillips 1988) in per capita hours are not rejected, per capita hours
should be first differenced prior to the analysis. Consistent with Francis and Ramey (2001),
tests of a unit root in per capita hours as plotted in figure 3 are not rejected at conventional
significance levels. Of course non-rejection of a null hypothesis does not mean the alternative
hypothesis, in this case level stationarity, is rejected. Indeed, the Kwiatkowski, Phillips,
Schmidt and Shin (1992) test of the null of level stationarity against the alternative of a unit
root is not rejected for this series at conventional significance levels either.12 Therefore, as
in Christiano, et. al. (2002), neither level nor difference stationarity of per capita hours can
be rejected by classical statistical criteria. Christiano, et. al. (2002) use Bayesian methods
to argue that the preponderance of the evidence points toward level stationarity as the most
plausible assumption.13 Finally, the purpose of this paper is to examine the technologydriven business cycle hypothesis and standard versions of this hypothesis imply per capita
hours are level stationary. These considerations suggest it is reasonable to work with per
capita hours in levels. I discuss alternative ways of incorporating per capita hours into the
analysis below.
In addition to considering the minimal set of variables needed for identification, Galí
(1999) and Christiano, et. al. (2002) also consider adding variables to assess the robustness
12

Kwiatkowski, et. al. (1992) demonstrate that the asymptotic critical values of their test imply over
rejection of the null when the data is autocorrelated. For example, in their Table 2, p. 171 if the true
data generating process is an AR(1) with autoregressive coefficient of 0.8 then the true size of a test with
nominal size of 5% is between 24% and 30%, depending on the sample size. Therefore, following Christiano,
et. al. (2002), I base my conclusions regarding level stationarity on small sample critical values derived
using Monte Carlo methods. I assumed the data generating mechanism was an AR(1), used 10,000 Monte
Carlo draws of sample sizes of length 188, and used the recommended lag truncation parameter (8) for the
test. The 5 percent critical values for autocorrelation coefficients 0.85, 0.9 and 0.95 are 0.85, 1.03 and 1.40,
respectively. The corresponding test statistic for per capita hours worked is 0.47. Test statistics exceeding
the critical values lead to rejection of the null of level stationarity. Therefore the test is not rejected at the
5% significance level. Note that the asymptotic 5% critical value from Table 1, p. 166 of Kwiatkowski, et.
al. (1992) is 0.46.
13
The debate over level versus difference stationarity is controversial because, as Christiano, et. al. (2002)
show, the sign of the estimated response of hours to a neutral technology shock depends on which is assumed.
Galí’s (1999) finding that neutral technology shocks are irrelevant to the business cycle is not sensitive to
the level versus difference stationarity choice.

15

of their findings. Christiano, et. al. (2002) argue that the contribution to business cycles of
neutral technological change may be overstated by excluding certain key variables. Motivated
by their findings, I include the four variables plotted in the right column in the analysis as
well. As in that paper inflation is measured by the GDP deflator and the nominal interest
rate is the 3-month Treasury Bill rate. I also include the nominal ratios of consumption
and investment to output, in logs. These variables are suggested by the balanced growth
implications of the growth model and may be important for correctly identifying technology
shocks.
To retain consistency with the measure of labor productivity I use, my nominal output
measure is the corresponding BLS output measure. I use the consumption measure corresponding to the consumption deflator used to deflate the nominal equipment price. The
investment measure is total investment as defined above. As in the case of the three variables
in the left column, one cannot reject the hypothesis of level stationarity using the test of
Kwiatkowski, et. al. (1992) for any of the four variables in the right column.14

4. Main Findings
The standard one-technology-shock identification scheme can be implemented with just two
variables, productivity growth and hours. The two-technology-shock identification scheme
proposed in this paper can be implemented with just three variables, productivity growth,
hours and real investment price growth. In the first sub-section I describe my findings for
these minimal systems using the real equipment price as the measure of the real investment
price. In the second subsection I describe my findings for larger models which add variables
to the minimal systems. Regardless of the size of the empirical models estimated, the results
strongly suggest an important role for technology shocks in the business cycle with the largest
14

See footnote 12 for details of how I conducted these tests. In addition to implying the nominal expenditure shares are stationary, the growth model implies the real price, labor productivity, consumption and
investment are integrated of order 1. Standard tests do not reject this hypothesis for each of these variables
at standard levels of significance. Tests of the null hypothesis of trend stationarity are strongly rejected for
the price but not the other variables.

16

contribution coming from investment-specific technology shocks.
4.1. Models with a Minimal Number of Variables
I estimated the minimal systems using four lags, which is consistent with the literature. The
complete estimated dynamic responses (out to 64 quarters) of the inverse real investment
price (1/P ), labor productivity (Y /H), hours (H) and output (Y ) are displayed in figure
4. The responses to investment-specific shocks are labelled “I-Shock Responses” and the
responses to neutral shocks are labelled “N-Shock Responses.” I use “one-shock model”
to describe the econometric model estimated under the one-technology-shock hypothesis
and “two-shock model” to describe the model estimated under the two-technology-shock
hypothesis. All responses are to one-standard deviation positive innovations to technology
and are plotted relative to the standard deviation of the shock.
Consider the N-shock responses in the one-shock model in the right-most plot. The
permanent and positive response of labor productivity was used to identify these responses
and the other responses are conditional on this outcome. With hours included in the model
in levels, hours responds positively about a quarter of a percent before declining slowly back
to zero. This dynamic response is consistent with the one reported recently by Christiano,
et. al. (2002) who include hours in the same way. Given the similarity of the productivity
responses as well, the response of output (the sum of labor-productivity and hours) is also
consistent with this other work.
Now consider the N-shock responses estimated using the two-shock model, in the middle
plot. The responses of productivity, hours and output are strikingly similar to the oneshock model. The main difference is that the response of hours, and therefore also output,
is dampened slightly in the two-shock model. Including real investment price growth in
the one-shock model has little effect on the estimated responses of the one-shock model
(not shown).15 It follows that the two identification strategies are essentially identifying the
15

This finding suggests the real equipment price does not add much new information for forecasting
productivity that is not already contained in productivity and hours.

17

same shock. The two-shock identification has the advantage of yielding the response of the
(inverse) equipment price to an N-shock. Interestingly the equipment price rises (its inverse
falls) after a positive N-shock. This is consistent with the predictions of real business cycle
models with a rising short-run supply price of capital, such as in the two-sector model in
Christiano and Fisher (1998). This latter finding adds support to the interpretation of the
N-shock responses as being genuine neutral technology shocks.
The I-shock responses in the left-most plot are conditional on the permanent and (positive) negative response of the (inverse) real equipment price. The peak response of hours
is a little more than one percent. This is four times greater than in the hours response to
an N-shock in the same model. The positive response of hours to an I-shock is consistent
with the real business cycle models studied by Christiano and Fisher (1998) and Greenwood,
Hercowitz and Krusell (2000). After an initial increase, the response of productivity declines
below zero before rising to it long run positive value. Note that while theory predicts the
productivity response to be positive in the long run, this is not imposed on the estimation.
The initial decline in productivity is also reasonable from the perspective of theory. This is
because investment takes time to have an impact on the capital stock and so productivity
can be driven by hours in the aftermath of an I-shock if hours worked responds strongly.
Given the relatively small movements in productivity, output responds similarly to hours.
Output’s response to an I-shock is similar to that for an N-shock in the short run, but it
declines more rapidly and converges to a lower long run value.
One’s confidence in the interpretation of the identified shocks depends in part on whether
theory can explain the dynamic responses to them. It is also important to assess the degree
of sampling uncertainty in the estimates. Information about this is presented in figure 5
which displays the responses of hours, productivity and the equipment price to the identified
shocks over a shorter horizon (32 quarters) and with equal-tailed, point-by-point 95 percent
confidence bands (dashed lines).16 Several points are worth noting here. First, the hours
16

These bands are computed using a standard bootstrap procedure combined with the Hall (1992) method
of constructing confidence intervals. See Killian (1999) for a discussion of bootstrap confidence intervals in a
relevant context. He finds that Hall’s confidence interval has relatively good classical coverage probabilities

18

responses to neutral shocks in both models are not significantly different from zero, but the
response to an I-shock is significant. Second, the increase in the equipment price following
an N-shock in the two-shock model is significant, at lease in the first few periods after a
shock. Third, the productivity response to an I-shock is not statistically significant.
The dynamic responses of hours worked are broadly consistent with theory, but are
they of the kind which can account for observed hours? The weak responses of hours to
N-shocks suggests these technology shocks are unimportant for this variable. On the other
hand, the strong and statistically significant responses to an I-shock suggests these shocks are
important. This can be assessed by examining figure 6 which shows historical decompositions
of hours due to investment-specific, neutral and both technology shocks along with the actual
path of hours.17 The first row shows the investment-specific shock accounts for a large part
of the variation in hours worked, particularly around recessions. In striking contrast, neutral
technology shocks seem almost unrelated to the business cycle (the decomposition for the
one-shock model, not shown, is similar). The combined effects of the two shocks track actual
hours quite closely.
Although it seems clear from figure 6 that the investment-specific technology-shocks
account for a large part of the business cycle and neutral shocks account for much less, hours
worked has some high and low frequency variation which may confound the interpretation of
such a figure. I now assess the contributions of the identified technology shocks to business
cycles more precisely using figure 7 and table 1. The figure shows plots of the business
cycle component of actual hours (solid lines) and hours as predicted by the empirical models
(dashed lines) where the business cycle components have been calculated using the same
compared to other bootstrap confidence intervals. To the extent that a point estimate lies closer to the
lower (upper) bound of the confidence interval then this is indicative of downward (upward) bias in the point
estimates. In figure 5 and below there is evidence of bias in the point estimates. This appears to be due
to non-linearity in the mapping from the regression coefficients to the dynamic response functions, and not
due to biased coefficient estimates. Consequently I do not employ the Killian (1998) “bias-correction” here.
A Baysesian analysis of the liklihood of different responses, as suggested by Sims and Zha (1999), is left to
future research.
17
The predicted time path of hours for a given model and shock is based on simulating the estimated
vector-autoregression underlying the dynamic responses in figures 4 and 5 using the estimated shocks and
the actual data in the first four periods of the sample to initialize the simulation.

19

procedure as in figure 2. The table shows summary statistics useful for quantifying the
extent to which technology shocks account for business cycle dynamics. The table includes
evidence for both hours and output and also a case (right-most column) where the one-shock
model is appended to include the linearly detrended real equipment price. This latter case
is discussed below.
The business cycle dynamics of hours worked derived from the N-shocks in the two models
is displayed in the lower row of figure 8, with the two-shock model on the left. Consistent
with the dynamic responses of hours shown in figures 4 and 5, the path of hours is similar
across the two identifications. In both cases, the variation in hours seems small, and the
predicted path does not seem to co-move strongly with actual hours. This is consistent with
Galí (1999) and Christiano, et. al. (2002) who have shown N-shocks to be unimportant
for the business cycle. In contrast, the I-shock in the minimal two-shock model seems to
generate volatility in hours near that of actual hours and there seems to be much closer
conformity with actual hours than for N-shocks. The overall effect of technology shocks is
to generate fluctuations in hours worked that are quite close to actual hours. If anything,
technology shocks seem to account for too much of the variation in hours. For example, the
recession in the early 1980s is widely believed to have been due in large part to monetary
policy, but here the recession is attributed almost entirely to I-shocks.
The sense of I-shocks generating a significant fraction of business cycle variation in hours
worked is confirmed by the findings reported in table 1. Panel A of this table shows the
relative volatility of the technology components of the two models, σ 2H m /σ 2H d , and the correlation of these variables with actual hours, ρ(H m , H d ). The numbers in parenthesis are
nominal equal-tailed 95 percent confidence intervals. The nominal confidence intervals for
the variance ratios sometimes include inadmissible values for this statistic (less than zero
or greater than 1). In these cases the bound of the interval is set to the nearest admissible
value for the statistic.
Consider the two-shock model first. Consistent with figure 7 investment—specific shocks
generate 54 percent of the business cycle volatility of hours and we have more than 95
20

percent confidence that this percentage exceeds 20. Furthermore the correlation coefficient
is large, 0.79, and statistically significant. These results strongly suggest a major role for
investment-specific shocks in generating the business cycle. On the other hand, the impact
of neutral shocks in the two-shock model is similar to the one-shock model, that is small.
The confidence intervals suggest that it is unlikely that these shocks generate more than 8
percent of the hours worked variation of interest and are relatively weakly correlated with
actual hours. Finally, consistent with the strong impact of investment-specific shocks, the
combined effect of the technology shocks in the two-shock model is strikingly large and highly
correlated with actual hours.18
Panel B of table 1 shows similar statistics for output, including the variance ratio
σ 2Y m /σ 2Y d and the correlation ρ(Y m , Y d ). These confirm the impression that investmentspecific shocks are important for the business cycle. Consistent with the pattern of responses
of productivity to neutral shocks, these shocks are more important for explaining output.
Still, the investment—specific shocks are the most important of the two technology shocks.
4.2. Larger Models
We now consider the findings based on estimating the larger systems. As discussed in section
3.2, these systems consist of the variables in the minimal models plus the nominal ratios of
investment to output, consumption to output, inflation and a short-term nominal interest
rate. Figure 8 displays the complete dynamic responses for the same variables considered
above in the top row and consumption (C), investment (X) and the ex post real interest
rate (R) in the bottom row. To facilitate comparisons across the two identification schemes,
investment is plotted in consumption units for the N-shock cases. Investment is in units of
equipment in the I-shock case.
The N-shock responses in the right two columns are similar in magnitude to the compa18

The total contribution of technology shocks would be exactly equal to the sum of the contributions of
the two shocks if the estimated shocks were exactly orthogonal to the each other at all leads and lags. The
estimation procedure only guarantees that the two shocks are orthogonal contemporaneously. In practice
there are slight correlations at various leads and lags of the shocks. Differences between the sum of the
contributions and entries in the first column of table 1 (and table 2, below) reflect these slight correlations.

21

rable responses in figure 4. The main difference is that output and hours rise more slowly
to their peaks and that output, hours and productivity all take longer to attain their long
run values. In the two-shock case, the response of the equipment price is somewhat stronger
than with the corresponding minimal system, but qualitatively quite similar. In the larger
systems we can assess the plausibility of the identification schemes in terms of the responses
of consumption, investment and the real interest rate. Given the positive long run responses
of consumption and investment and the rise in the real interest rate, these responses are
broadly consistent with neoclassical theory. Finally, as with the minimal systems the two
identification strategies seem to be identifying the same neutral shock.
Including the additional variables has more of an impact on the responses to I-shocks.
The responses that are comparable to those in figure 4 are displayed in the top plot of the
left-most column. First, the initial response of the inverse equipment price is weaker - the
initial peak of the price response in figure 4 is nearly 1.5 percent, while here the response is
about half as large as that. However, the inverse price converges to a larger positive value
here (3 percent versus 1.8 percent). The initial response of hours is also somewhat smaller
than before (0.8 percent versus 1.1 percent before). More interestingly there is now a second
hump in the hours response. As with the comparable minimal system, the overall response
of hours is much stronger than in the N-shock case. Productivity is much more persistent
in its negative response compared to the minimal case. Now it takes over 150 quarters for
the productivity response to turn positive compare to about 20 before (not shown). The
behavior of productivity means that, after an initial burst, output declines, before turning
positive.19
Now consider the responses in the lower left-most plot. Notice that investment initially
rises quite strongly, then declines just as strongly, before sustained growth toward the new
long run level sets in. This response seems to correspond quite closely to the hours response.
In particular, the initial rise and fall of hours coincides with the boom and decline in invest19

Recall that output is measured in consumption units. If it were measured in “output units,” that is
using a chain-weighting formula that accomodated changes in the consumption and investment deflators,
then the response would be mostly positive.

22

ment, while the second hump in hours coincides with the resumption of positive investment
growth. Consumption’s response seems quite weak. However, while not clear from the figure,
it does converge to a positive value. Campbell (1998) describes an investment-specific shock
driven vintage capital model which generates similar responses to these. Finally, the real
interest rate response is quite similar to the N-shock case. In sum, while somewhat different
from the responses estimated in the minimal two-shock system, the I-shock responses in the
larger two-shock model are broadly consistent with each other and with theory.
Some indication of the sampling uncertainty underlying the responses in figure 8 are
shown in figures 9 and 10. Several observations are worth making here. First, as in the
minimal case, the response of hours to an I-shock is significant. Interestingly, the responses
of hours in the N-shock cases are significant after several quarters when in the minimal
systems they were not. Second, the positive response of the equipment price to an N-shock
continues to be significant. Finally, the responses of consumption, investment, output and
the real interest rate in figure 10 are significant for at least a couple of quarters.
We now consider how important the identified technology shocks are for the business
cycle in the larger systems. Figures 11 and 12 table 2, the analogues of figures 6 and 7 and
table 1, confirm the main findings from the minimal systems. Figure 11 is somewhat different
from figure 6, with investment-specific hours less closely tracking actual hours and hours due
to neutral shocks more obviously cyclical. Still, the combined effect of the shocks seems large
and investment-specific shocks seem much more important. One notable difference with the
minimal system is that here there seems more room for monetary policy during the Paul
Volker period. This impression is confirmed in figure 12 where neutral shocks still seem to be
little related to the business cycle dynamics of hours, while investment-specific shocks seem
to be a major part of these dynamics. As before, the two identification strategies deliver
similar results for the neutral shocks.
Panel A of table 2 shows that the investment-specific shocks account for 48 percent of the
business cycle variation in hours and the implied path of hours is strongly correlated with
actual hours. Consistent with previous work, neutral shocks account for only 6 percent of
23

the variation in hours in the two-shock model and 4 percent in the one-shock model. In both
of these cases the correlation of the implied hours paths with actual hours is quite weak.
Overall, technology shocks account for 52 percent of the business cycle variation in hours.
Moreover, according to the indicated confidence interval for this statistic, it is unlikely this
percentage is below 30. The path of hours generated by the combined effects of the two
technology shocks is highly correlated with actual hours. In sum, when compared with the
results in table 1, including additional variables in the analysis has little impact on the
findings for hours. A similar conclusion holds for the effect of technology shocks on output,
as shown in panel B of table 2. As before, technology shocks are important for explaining
the business cycle dynamics of output, and investment-specific shocks are more important
than neutral shocks.

5. Robustness
The previous section demonstrated that technology shocks account for a significant fraction
of the business cycle variation in hours worked and that investment-specific technology shocks
are the most important of the two technology shocks considered. In this section I consider the
robustness of these findings to various perturbations of the analysis. In addition, I consider
the possibility that the shocks I have identified are not technology shocks, but reflect changes
to other variables. From this analysis I conclude that the main findings are robust and that
the shocks I have identified are unrelated to leading candidates for variables that may affect
productivity and the real investment price in the long run.
5.1. Alternative Ways of Including Per Capita Hours in the Analysis
As I described in section 3.2, the appropriate way to include per capita hours worked into the
analysis is a matter of some controversy. Here I consider two often suggested alternatives,
first differencing and quadratic detrending, and focus on the larger two shock model. These
alternative ways of including per capita hours imply somewhat smaller, but still large, effects
of technology shocks on hours worked. Specifically, first differencing implies technology
24

shocks account for 30% of business cycle variation in hours worked and that the largest
contribution comes from I-shocks. Similarly, quadratic detrending per capita hours implies
technology shocks account for 36% of business cycle variation in hours worked with I-shocks
again the main contributor.20
While taken at face value these results do not seem to overturn the main findings, it
is unclear how they should be interpreted. This is because the response of productivity to
a positive I-shock is not consistent with theory under these alternative ways of including
per capita hours since in both cases productivity converges to a negative value. There are
other reasons to prefer the level specification. Certainly, the arguments of Christiano, et.
al. (2002) point toward the level specification. In addition, the quadratic trend assumption
seems questionable. First, when the hours series excludes farm hours, as it does here, the dip
down in per capita hours in the middle of the sample is much less pronounced. This suggests
that much of the downward trend in the early sample in the data which includes farm hours,
as used by Francis and Ramey (2001), is due to the secular decline in farm hours. Excluding
farm hours instead of quadratically detrending is a reasonable way of dealing with this trend.
Second, while non-farm per capita hours do dip somewhat in the middle part of the sample,
this period of relatively low hours worked coincides with higher inflation and nominal interest
rates in the middle of the sample. It is not obvious that quadratically detrending hours is
the best way to accommodate this co-movement. Indeed, it is possible that proceeding in
that way may lead to specification error.
5.2. Assuming a Structural Break
Galí, López-Salido and Vallés (2001) have argued that the response of hours to neutral
technology shocks before Paul Volker’s chairmanship at the Federal Reserve was different
from during and after that time. Using linearly detrended total hours worked, they estimate
20

The impact of these changes on the responses of hours to the technology shocks is similar. For I-shocks
hours continue to respond positively over the first 32 periods. The response of hours to an N-shock, however,
is negative over this horizon. That the response of hours is negative following an N-shock when per-capita
hours are first-differenced is consistent with the findings of Christiano, et. al (2002) and Francis and Ramey
(2002).

25

hours to fall in the short run after a neutral technology shock in the pre-Volker period,
before 1979:II, but to rise in the Volker-Greenspan period, after 1982:III. These findings are
interpreted as arising due to an increased emphasis on price stability by the Federal Open
Market Committee in the Volker-Greenspan period. Given the plausibility of the structural
break hypothesis, it seems reasonable to ask whether the main findings reported here are
somehow distorted by assuming structural stability throughout the sample period.
Unfortunately, the limited size of the two sub-samples suggested by Galí, et. al. (2002)
presents some problems with assessing the impact of a structural break. Since the Galí,
et. al. (2002) hypothesis is fundamentally about the conduct of monetary policy, it seems
sensible to assess its effects by considering the larger two-shock model which includes the
nominal interest rate and inflation. However, the two sub-samples are just too short to
estimate this seven variable model with any reliability (Galí, et. al. (2002) estimated a
model with only four variables, productivity, hours, inflation and a nominal interest rate.) I
address this problem by studying a smaller five variable version of the larger two-shock model
which excludes the two nominal expenditure share variables and is essentially the system
estimated in Galí, et. al. (2002) appended to include the equipment price. The second
problem is that the method used to extract the business cycle component of per capita
hours becomes unreliable with short samples. I address this problem by considering forecast
error decompositions associated with the estimated models. By comparing these with those
derived from the larger model estimated over the full sample I can evaluate whether assuming
a structural break matters for the main findings.
In figure 13 I display estimates of the short-run responses of the real price, labor productivity and hours for the two sub-samples. While not shown in the figure, the long run
responses of the real price and productivity for the two shocks over the two sub-samples are
consistent with theory. Differences in the short-run dynamics of these variables across the
two sub-samples as shown in the figure does suggest the possibility of a structural break.
Differences are also apparent across sub-samples for the response of hours. In contrast to the
findings of Galí, et. al. (2002) there is little to choose statistically between the response of
26

hours to N-shocks across the two sub-samples. However, the differences in the shape of the
responses of hours to an I-shock are broadly consistent with the hypothesis of Galí, et. al.
(2002). In particular, hours fall in the immediate aftermath of an I-shock before eventually
rising, in the early period. In the later period the response of hours is uniformly positive.
Differences in the hours responses does not rule out the possibility that technology shocks
account for a large fraction of the variation in hours in both sub-samples. This issue is addressed in table 3 where I display forecast error decompositions for various forecast horizons
(in quarters) for the larger model estimated over the full sample (Panel A), and the smaller
two-shock model over the sub-samples 1955:I-1979:II (Panel B) and 1982:III-2000:IV (Panel
C). The results for the full sample confirm the findings reported in Table 2 for hours: technology shocks account for a significant fraction of the forecast error in hours over all the
horizons and the contribution of I-shocks is large compared to the N-shocks. The findings
for the two sub-samples are quite similar to the full sample results. The combined effects
of the technology shocks is somewhat smaller in the sub-samples, especially at the shorter
horizons, but the effects are still large. In addition the I-shocks continue to be the most
important of the two shocks. I conclude that if one takes seriously the structural-break
hypothesis then it leads to the same basic conclusions. Still, the evidence in favor of a structural break is not conclusive (e.g. Christiano, et. al. 2002 and Rudebusch 2002) and so
the full sample results based on the larger seven variable system may be preferred if the five
variable model has omitted variables and in fact there is sample period stability.
5.3. Trend Stationary Equipment Prices
In the model described in section 2.1 the standard one-shock identification scheme is valid
if investment-specific technological change is deterministic. An obvious first test of whether
the deterministic growth assumption is plausible is to test for trend stationarity. In the
preliminary data analysis in section 3.2, standard statistical tests of the null of a unit root
in the equipment price against the alternative of trend stationarity do not reject a unit
root. Moreover, the Kwiatkowski, et. al. (1992) test of the null of trend stationarity
27

versus the alternative of a unit root is strongly rejected. Questions of the veracity of the
available statistical tests aside, this evidence is supportive of the approach taken in this
paper. Nevertheless, the statistical tests are not perfect and it is worthwhile to examine the
implications of assuming a deterministic trend in the level of investment specific technology.
I do this by adding the linearly detrended equipment price to the minimal and larger
versions of the one-shock model, maintaining the one-shock assumption that only innovations
to neutral technology have a long run effect on productivity. With these modified models
the identified effects of technology shocks become a mixture of the responses to I-shocks and
N-shocks identified with the two-shock models (not shown). One indication of this is that
the contribution of technology shocks to the business cycle under the one-shock assumption
resembles that found with the two-shock models (see the right-most columns of tables 1 and
2).
This result has two possible interpretations. First, it may be the case that investmentspecific technological change is deterministic and that these modified one-shock models are
accurately capturing the effects of neutral shocks. The difficulty with this interpretation is
that the real equipment price is strongly negatively related to movements in investment and
output in response to the estimated technology shock. That is, the real investment price
falls in a boom driven by the supposed neutral shocks. One way this could happen would
be if there are increasing returns to producing investment goods (see, for example, Murphy,
Shleifer and Vishny (1989)). While this may be true, the degree of increasing returns required
to reproduce the large price responses (they are similar to the price response to I-shocks over
the first 32 quarters in figures 4 and 8) would seem to be implausibly large. The second,
and in my view, more plausible interpretation, is that the estimated responses under the
trend stationary assumption confound the effects of the two shocks and are not an accurate
reflection of the true effects of neutral technology shocks.

28

5.4. Other Variables in the Estimated Systems
Now consider the effects of including additional variables in the estimated econometric models. There are any number of variables that might be considered. Here I consider the growth
rate of M2, the growth rate of a measure of commodity prices (this is the Conference Board’s
index of sensitive materials prices deflated by my consumption deflator), and manufacturing
capacity utilization. When these variables are added individually to the larger two-shock
model and the responses and historical decompositions recalculated, the findings are not
substantively altered. In all cases the contribution of technology shocks to hours variability
exceeds 40 percent and investment-specific shocks remain, by far, the most important of the
two technology shocks.
5.5. Are the Shocks “Technology”?
A common criticism of the identification strategy I have used in this paper is that changes in
variables other than neutral and investment specific technology have had long run effects on
productivity and the real investment price. If this is true, then one would need to broaden
the interpretation of the shocks beyond that which I have given in this paper. However, one
would still conclude that permanent shocks to the efficiency of producing consumption and
investment goods are important for the business cycle.
Still, the interpretation of the shocks may influence one’s view of the business cycle.
As such it is interesting to assess whether these shocks are related to other non-technology
variables that might influence the efficiency of producing consumption and investment goods.
Leading candidates include capital tax rates, capital depreciation rates, and labor union
membership.21 Measures of these variables have secular trends and, to the extent that these
21

It is straightforward to show that permanent changes in capital income taxes and the rate of capital
depreciation affect aggregate labor productivity in the model presented section 2. In a two-sector version
of this model with different factor share across consumption and investment good sectors, then changes in
capital taxes and the rate of capital depreciation also affect the real price of investment goods. It is less
clear how changes in unionization rates should affect labor productivity and the real investment price. Still,
some authors, including Schmitz (2001), have argued that unions often bargain for work rules which have
the result of lowering labor productivity. To the extent that unionization rates have fallen over my sample
period and fallen by more in durable goods producing industries, it is reasonable to imagine that these have

29

trends reflect stochastic permanent components, then the shocks I have identified could be
confounded with these other variables.22
Table 4 presents evidence which suggests that the shocks that I identify do not reflect
shocks to these other variables. This table reports correlations of the identified shocks from
the seven variable model with the growth rates of capital taxes, depreciation rates and
union membership as a fraction of the labor force. Since the variables are only available
on an annual basis the correlations are estimated using annual averages for the identified
technology shocks. As Table 4 indicates, in all cases the correlations are small and not
significantly different from zero. So, while in theory it is possible that the shocks I have
identified may confound other shocks, in practice this does not seem to be a problem.

6. Conclusion
In this paper I have shown that when a standard procedure for identifying the effects of
technology shocks is modified to take into account investment-specific technological change,
then previous findings which suggest technology shocks are unimportant for business cycles
are overturned. Results based on the sample period 1955-2000 suggest investment-specific
technology shocks account for about 50 percent of the variation in hours worked and about
40 percent of the variation in output. At the same time, neutral technology shocks, the
focus of the real business cycle literature, account for less than 10 percent of either output
or hours variation.
Since these results are based on a procedure which abstracts from orthogonal transitory
technology shocks, the findings may be viewed as representing a lower bound on the overall
contribution of technology shocks to business cycles. Therefore, the results strongly suggest
that technology shocks, or more generally, shocks to the efficiency of producing goods, are
influenced labor productivity and the real equipment price.
22
My sources are as follows. For the capital tax I use an updated version of the series used by McGrattan
(1994). For the depreciation rate I use the Bureau of Economic Analysis series on the rate of depreciation
of the net stock of fixed assets and consumer durables. For union membership I use the series available from
the Bureau of Economic Analysis. These data are only available annually.

30

important for understanding business cycles. Some investigation of the robustness of the
findings was presented here, but of course more needs to be done. Still it does seem that
business cycle research could benefit from being directed toward studying investment-specific
technological change or other factors which influence the efficiency of producing investment
goods but not consumption goods. The vanguard of research cited in this paper is a good
foundation, but more work needs to be done here as well.

31

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34

Table 1. The Effects of Technology Shocks in the Minimal Models
Two-Shocks
One-Shock
All
w/o Price
w Price
Statistic
Technology Investment
Neutral
Neutral
Neutral
Panel A: Hours Worked
σ 2H m /σ 2H d
0.64
0.54
0.03
0.04
0.58
(0.35, 1)
(0.20, 1)
(0, 0.04)
(0, 0.08)
(0.33, 1)
ρ(H m , H d )
0.80
0.79
0.34
0.32
0.80
(0.35, 0.96) (0.45, 0.97) (0.04, 0.65) (−0.33, 0.59) (0.61, 0.97)
Panel B: Output
0.76
0.44
0.22
0.28
0.61
σ 2Y m /σ 2Y d
(0.52, 1)
(0.12, 0.86)
(0, 0.40)
(0, 0.55)
(0.38, 1)
ρ(Y m , Y d )
0.86
0.73
0.55
0.60
0.83
(0.34, 0.97) (0.50, 0.95) (0.04, 0.80) (−0.40, 0.86) (0.72, 0.98)

Table 2. The Effects of Technology Shocks in the Larger Models
Two-Shocks
One-Shock
All
w/o Price
w Price
Statistic
Technology Investment
Neutral
Neutral
Neutral
Panel A: Hours
σ 2H m /σ 2H d
0.52
0.48
0.06
0.04
0.38
(0.31, 0.95) (0.35, 0.93)
(0, 0.11)
(0, 0.05)
(0.28, 0.75)
ρ(H m , H d )
0.82
0.74
0.31
0.11
0.61
(0.82, 0.97) (0.68, 0.95) (−0.10, 0.60) (−0.59, 0.19) (0.46, 0.88)
Panel B: Output
2
2
σ Y m /σ Y d
0.40
0.34
0.09
0.09
0.33
(0.16, 0.69) (0.17, 0.64)
(0, 0.15)
(0, 0.15)
(0.22, 0.63)
0.71
0.65
0.24
0.16
0.57
ρ(Y m , Y d )
(0.60, 0.90) (0.55, 0.90) (−0.23, 0.41) (−0.42, 0.28) (0.42, 0.85)

35

Table 3: Forecast Error Decompositions for Hours
All
Horizon Technology Investment
Neutral
Panel A: 1955:I-2000:IV
1
46.3
44.6
1.7
4
59.3
58.6
0.7
8
57.8
57.3
0.5
12
54.4
51.6
2.8
32
61.3
43.3
18.8
Panel B: 1955:1-1979:II
1
21.2
20.7
1.2
4
31.4
28.7
2.7
8
27.9
25.1
2.8
12
37.0
33.3
3.7
32
58.8
54.1
4.7
Panel C: 1982:III-2000:IV
1
19.1
14.1
5.1
4
20.5
18.8
1.7
8
42.6
40.2
2.4
12
57.9
56.0
1.9
32
58.6
57.3
1.3

Table 4: Correlations of Alternative Shocks with Measured Technology Shocks
Capital Unionization Depreciation
Tax
Rate
Rate

Correlation
Standard Error
P-value

Neutral Technology Shocks
0.06
0.003
-0.02
0.14
0.14
0.18
0.66
0.98
0.91

Investment-Specific Technology Shocks
Correlation
-0.20
-0.07
0.03
Standard Error
0.18
0.14
0.12
P-value
0.27
0.61
0.82
Note: The sample period for the capital tax correlations is 1955-1997 and for the other
correlations is 1955-2000.

36

Figure 1: Nominal and Real Prices of Investment

0.4

Equipment Deflators

Total Investment Deflators

0.4

0.2

0.2

-0.0

-0.0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

-1.0

-1.0

-1.2

-1.2

-1.4

-1.4
1955 1961 1967 1973 1979 1985 1991 1997

GCV

1.75

1955 1961 1967 1973 1979 1985 1991 1997

NIPA

GCV

Real Equipment Prices

1.75

1.50

1.50

1.25

1.25

1.00

1.00

0.75

0.75

0.50

0.50

0.25

0.25

0.00

0.00

-0.25

-0.25

-0.50

NIPA

Real Total Investment Prices

-0.50
1955 1961 1967 1973 1979 1985 1991 1997

GCV

1955 1961 1967 1973 1979 1985 1991 1997

NIPA

GCV

NIPA

Figure 2: Investment Quantities and Prices

-2.0

Equipment in the Long Run

1.75

5.0

1.50

-2.4

1.25

-2.8

2.5

1.00

-3.2

0.75
-3.6

0.0

0.50

-4.0

0.25

-4.4

-2.5

0.00

-4.8

-0.25

-5.0

1958 1964 1970 1976 1982 1988 1994

Real Share

-1.2

Equipment over the Business Cycle

1958 1963 1968 1973 1978 1983 1988 1993

Price

Total Investment in the Long Run

-1.3

Quantity

0.72

5.0

Price

Total Investment over the Business Cycle

0.60

-1.4

2.5

0.48

-1.5
-1.6

0.36

-1.7

0.24

0.0

-1.8

0.12

-1.9

-2.5

0.00

-2.0
-2.1

-0.12

-5.0

1958 1964 1970 1976 1982 1988 1994

Real Share

1958 1963 1968 1973 1978 1983 1988 1993

Price

Quantity

37

Price

Figure 3: Variables in the Econometric Models

Real Investment Price Growth

0.04

Consumption Share of Nominal Output

-0.600

0.03

-0.625

0.02

-0.650

0.01

-0.675

0.00

-0.700

-0.01

-0.725

-0.02

-0.750

-0.03

-0.775

-0.04

-0.800
1955 1961 1967 1973 1979 1985 1991 1997

0.03

1955 1961 1967 1973 1979 1985 1991 1997

Labor Productivity Growth in Consumption Units

Investment Share of Nominal Output

-1.65

0.02

-1.70

0.01

-1.75

0.00

-1.80

-0.01

-1.85

-0.02

-1.90
1955 1961 1967 1973 1979 1985 1991 1997

1955 1961 1967 1973 1979 1985 1991 1997

Per Capita Hours Worked

-7.40

0.150

GDP Deflator Inflation and 3-Month T-Bill Rate

0.150

-7.45

0.125

-7.50

0.100

-7.55

0.075

0.075

-7.60

0.050

0.050

-7.65

0.025

-7.70

0.125

Interest Rate

0.100

Inflation

0.025

0.000

0.000

1955 1961 1967 1973 1979 1985 1991 1997

1956 1963 1970 1977 1984 1991 1998

Figure 4: Long Run Responses in the Minimal Systems

2.0

Two-Shock Model
I-Shock Responses

2.0

Two-Shock Model
N-Shock Responses

2.0

One-Shock Model
N-Shock Responses

1/P
1.5

1.5

1.5

Y
1.0

H

1.0

0.5

Y

0.5

Y

1.0

Y/H

Y/H
0.5

H
H
0.0

0.0

Y/H

0.0

1/P

-0.5

-0.5
16

32

48

64

-0.5
16

32

38

48

64

16

32

48

64

Figure 5: Short Run Responses in the Minimal Systems

2.5

Two-Shock Model

Two-Shock Model

One-Shock Model

Effect of I-Shock on H

Effect of N-Shock on H

Effect of N-Shock on H

2.5

2.5

2.0

2.0

2.0

1.5

1.5

1.5

1.0

1.0

1.0

0.5

0.5

0.5

0.0

0.0

0.0

-0.5

-0.5

-0.5

-1.0

-1.0
5

10

15

20

25

5

Effect of I-Shock on Y/H

1.50

-1.0

30

1.50

10

15

20

25

30

5

Effect of N-Shock on Y/H

1.50

1.25

1.25

1.25

1.00

1.00

1.00

0.75

0.75

0.75

0.50

0.50

0.50

0.25

0.25

0.25

0.00

0.00

0.00

-0.25

-0.25

-0.25

-0.50

-0.50
5

10

15

20

25

1.0

5

20

25

30

10

15

20

25

30

25

30

5

10

15

20

25

30

Effect of N-Shock on P

1.0

0.5

15

-0.50

30

Effect of I-Shock on P

10

Effect of N-Shock on Y/H

0.5

0.0

0.0

-0.5

-0.5

-1.0

-1.0

-1.5

-1.5

-2.0

-2.0

-2.5

-2.5

-3.0

-3.0
5

10

15

20

25

30

5

10

15

20

Figure 6: Actual and Technology-Driven Hours in the Minimal Two-Shock Model

Actual and Investment-Specific Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

1989

1992

1995

1998

1989

1992

1995

1998

Actual and Neutral Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

Actual and All Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

39

1980

1983

1986

Figure 7: Effect of Technology on Hours in the Minimal Systems

Effect of All Technology Shocks

Effect of Investment-Specific Shocks

in the Two-Shock Model

0.050

in the Two-Shock Model

0.050

0.025

0.025

0.000

0.000

-0.025

-0.025

-0.050

-0.050
1959

1965

1971

1977

1983

1989

1995

1959

Effect of Neutral Shocks

1971

1977

1983

1989

1995

1989

1995

Effect of Neutral Shocks

in the Two-Shock Model

0.050

1965

in the One-Shock Model

0.050

0.025

0.025

0.000

0.000

-0.025

-0.025

-0.050

-0.050
1959

1965

1971

1977

1983

1989

1995

1959

1965

1971

1977

1983

Figure 8: Long Run Responses in the Larger Systems

2.5

Two-Shock Model

Two-Shock Model

One-Shock Model

I-Shock Responses

N-Shock Responses

N-Shock Responses

2.5

2.0

2.5

2.0
1/P

1.5

2.0

1.5

1.0

1.5
Y

1.0
H

0.5

1.0

0.5

0.0

H

0.0

Y

Y/H

0.5

Y/H
H

-0.5

Y

0.0
1/P

-0.5

-0.5

Y/H

-1.0

-1.0
16

32

48

I-Shock Responses

2.0

32

48

64

N-Shock Responses

1.0

0.5

X

C

0.0

-0.5

-1.0

-0.5

-1.0
32

N-Shock Responses

X

-0.5

16

64

R

0.0

C

48

0.5
R

0.0

32

1.5
C

1.0
R

16

2.0

1.5

1.0
0.5

16

2.0
X

1.5

-1.0

64

48

64

-1.0
16

32

40

48

64

16

32

48

64

Figure 9: Short Run Responses in the Larger Systems

2.0

Two-Shock Model

Two-Shock Model

One-Shock Model

Effect of I-Shock on H

Effect of N-Shock on H

Effect of N-Shock on H

2.0

2.0

1.5

1.5

1.5

1.0

1.0

1.0

0.5

0.5

0.5

0.0

0.0

0.0

-0.5

-0.5

-0.5

-1.0

-1.0
5

2.5

10

15

20

25

-1.0

30

Effect of I-Shock on Y/H

5

2.5

10

15

20

25

30

Effect of N-Shock on Y/H

5

2.5

2.0

2.0

2.0

1.5

1.5

1.5

1.0

1.0

1.0

0.5

0.5

0.5

0.0

0.0

0.0

-0.5

-0.5

-0.5

-1.0

-1.0

-1.5
10

15

20

25

2.0

5

1.5

1.0

1.0

0.5

0.5

0.0

0.0

-0.5

-0.5

-1.0

-1.0

-1.5

-1.5

-2.0

25

30

10

15

20

25

30

5

10

15

20

25

30

Effect of N-Shock on P

2.0

1.5

20

-1.5

30

Effect of I-Shock on P

15

-1.0

-1.5
5

10

Effect of N-Shock on Y/H

-2.0

-2.5

-2.5
5

10

15

20

25

30

5

10

15

20

25

30

Figure 10: Short Run Responses in the Larger Systems
Two-Shock Model

Two-Shock Model

Effect of I-Shock on Y

3.5

One-Shock Model

Effect of N-Shock on Y

3.5

2.8

2.8

2.8

2.1

2.1

2.1

1.4

1.4

1.4

0.7

0.7

0.0

0.0

0.0

-0.7

-0.7

-0.7

-1.4

-1.4

-1.4

5

10

15

20

25

3.5

0.7

30

Effect of I-Shock on C

5

10

15

20

25

30

Effect of N-Shock on C

3.5

5

3.0

3.0

2.5

2.5

2.5

2.0

2.0

2.0

1.5

1.5

1.5

1.0

1.0

1.0

0.5

0.5

0.0

0.0

0.0

-0.5

-0.5

-0.5

10

15

20

25

5

10

15

20

25

30

Effect of N-Shock on X

4

5

3

3

2

2

2

1

1

1

0

0

0

-1

-1

-1

-2
5

10

15

20

25

30

Effect of N-Shock on R

2.0

10

15

20

25

30

Effect of N-Shock on R

5

1.5

1.5

1.0

1.0

1.0

0.5

0.5

0.5

0.0

0.0

15

20

25

30

15

20

25

30

10

15

20

25

30

25

30

0.0

-0.5
10

10

Effect of N-Shock on R

2.0

1.5

5

30

-2
5

2.0

-0.5

25

Effect of N-Shock on X

4

3

-2

20

-1.0

30

Effect of I-Shock on X

4

15

0.5

-1.0
5

10

Effect of N-Shock on C

3.5

3.0

-1.0

Effect of N-Shock on Y

3.5

-0.5
5

10

15

41

20

25

30

5

10

15

20

Figure 11: Actual and Technology-Driven Hours in the Larger Two-Shock Model

Actual and Investment-Specific Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

1989

1992

1995

1998

1989

1992

1995

1998

Actual and Neutral Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

Actual and All Technology-Driven Hours

-7.450
-7.475
-7.500
-7.525
-7.550
-7.575
-7.600
-7.625
-7.650
1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

Figure 12: Effect of Technology on Hours in the Larger Systems

Effect of All Technology Shocks

Effect of Investment-Specific Shocks

in the Two-Shock Model

0.050

in the Two-Shock Model

0.050

0.025

0.025

0.000

0.000

-0.025

-0.025

-0.050

-0.050
1959

1965

1971

1977

1983

1989

1995

1959

Effect of Neutral Shocks

1971

1977

1983

1989

1995

1989

1995

Effect of Neutral Shocks

in the Two-Shock Model

0.050

1965

in the One-Shock Model

0.050

0.025

0.025

0.000

0.000

-0.025

-0.025

-0.050

-0.050
1959

1965

1971

1977

1983

1989

1995

1959

42

1965

1971

1977

1983

Figure 13: Responses to Technology Shocks 1955:1-1979:2 and 1982:2-2000:4
2.0

1955:1-1979:2

1955:1-1979:2

1982:3-2000:4

1982:3-2000:4

Effect of I-Shock on P

Effect of N-Shock on P

Effect of I-Shock on P

Effect of N-Shock on P

3.0

1.5
1.0

-2.0
16

24

32

Effect of I-Shock on Y/H

24

32

Effect of N-Shock on Y/H

24

32

Effect of I-Shock on H

1.00

32

Effect of N-Shock on H

0.25
0.00
-0.25
-0.50
16

24

32

Effect of I-Shock on H

16

1.00

0.75

1.5

0.75

1.0

0.50

1.0

0.50

0.5

0.25

0.5

0.25

0.0

0.00

0.0

0.00

-0.5

-0.25

-0.5

-0.25

-1.0

-0.50

-1.0

-0.50

-1.5

-0.75

-1.5

-1.00
8

16

24

32

32

43

32

Effect of N-Shock on H

-0.75

-2.0
16

Effect of N-Shock on Y/H

0.50

1.5

-2.0

32

0.75

8

2.0

24

1.00

-0.5
16

16

1.25

0.0

-0.50
16

8

1.50

0.5

-0.25
8

Effect of I-Shock on Y/H

1.0

0.00

-0.5

32

1.5

0.25

0.0

24

2.0

0.50

0.5

16

2.5

0.75

1.0

-0.5
8

3.0

1.00

1.5

2.0

-2.0
16

1.25

2.0

0.0

-1.5
8

1.50

2.5

0.5

-1.0

-0.5
8

1.0

-0.5

0.0

-1.5

1.5

0.0

0.5

-1.0

2.0

0.5

1.0

-0.5

2.5

1.0

1.5

0.0

3.0

1.5

2.0

0.5

3.0

2.0

2.5

-1.00
8

16

24

32

16

32

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Extracting Market Expectations from Option Prices:
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Measurement Errors in Japanese Consumer Price Index
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Taylor Rules in a Limited Participation Model
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Maximum Likelihood in the Frequency Domain: A Time to Build Example
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Unskilled Workers in an Economy with Skill-Biased Technology
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Asset Price Fluctuation and Price Indices
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Labor Market Policies in an Equilibrium Search Model
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Hedging and Financial Fragility in Fixed Exchange Rate Regimes
Craig Burnside, Martin Eichenbaum and Sergio Rebelo

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Banking and Currency Crises and Systemic Risk: A Taxonomy and Review
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WP-99-12

Wealth Inequality, Intergenerational Links and Estate Taxation
Mariacristina De Nardi

WP-99-13

Habit Persistence, Asset Returns and the Business Cycle
Michele Boldrin, Lawrence J. Christiano, and Jonas D.M Fisher

WP-99-14

Does Commodity Money Eliminate the Indeterminacy of Equilibria?
Ruilin Zhou

WP-99-15

A Theory of Merchant Credit Card Acceptance
Sujit Chakravorti and Ted To

WP-99-16

1

Working Paper Series (continued)
Who’s Minding the Store? Motivating and Monitoring Hired Managers at
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Robert DeYoung, Kenneth Spong and Richard J. Sullivan

WP-99-17

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Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher

WP-99-18

Fiscal Shocks in an Efficiency Wage Model
Craig Burnside, Martin Eichenbaum and Jonas D.M. Fisher

WP-99-19

Thoughts on Financial Derivatives, Systematic Risk, and Central
Banking: A Review of Some Recent Developments
William C. Hunter and David Marshall

WP-99-20

Testing the Stability of Implied Probability Density Functions
Robert R. Bliss and Nikolaos Panigirtzoglou

WP-99-21

Is There Evidence of the New Economy in the Data?
Michael A. Kouparitsas

WP-99-22

A Note on the Benefits of Homeownership
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WP-99-23

The Earned Income Credit and Durable Goods Purchases
Lisa Barrow and Leslie McGranahan

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Globalization of Financial Institutions: Evidence from Cross-Border
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Allen N. Berger, Robert DeYoung, Hesna Genay and Gregory F. Udell

WP-99-25

Intrinsic Bubbles: The Case of Stock Prices A Comment
Lucy F. Ackert and William C. Hunter

WP-99-26

Deregulation and Efficiency: The Case of Private Korean Banks
Jonathan Hao, William C. Hunter and Won Keun Yang

WP-99-27

Measures of Program Performance and the Training Choices of Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel Sullivan

WP-99-28

The Value of Relationships Between Small Firms and Their Lenders
Paula R. Worthington

WP-99-29

Worker Insecurity and Aggregate Wage Growth
Daniel Aaronson and Daniel G. Sullivan

WP-99-30

Does The Japanese Stock Market Price Bank Risk? Evidence from Financial
Firm Failures
Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman

WP-99-31

Bank Competition and Regulatory Reform: The Case of the Italian Banking Industry
Paolo Angelini and Nicola Cetorelli

WP-99-32

2

Working Paper Series (continued)
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The Effects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior
Eric French

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Market Discipline in the Governance of U.S. Bank Holding Companies:
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Robert R. Bliss and Mark J. Flannery

WP-00-3

Using Market Valuation to Assess the Importance and Efficiency
of Public School Spending
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WP-00-4

WP-00-5

WP-00-6

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WP-00-7

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WP-00-8

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WP-00-9

Bank Capital Regulation With and Without State-Contingent Penalties
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WP-00-11

Oligopoly Banking and Capital Accumulation
Nicola Cetorelli and Pietro F. Peretto

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Puzzles in the Chinese Stock Market
John Fernald and John H. Rogers

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Idiosyncratic Risk and Aggregate Employment Dynamics
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WP-00-16

3

Working Paper Series (continued)
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Capital Requirements and Competition in the Banking Industry
Peter J.G. Vlaar

WP-00-18

Financial-Intermediation Regime and Efficiency in a Boyd-Prescott Economy
Yeong-Yuh Chiang and Edward J. Green

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How Do Retail Prices React to Minimum Wage Increases?
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Financial Signal Processing: A Self Calibrating Model
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Savings of Young Parents
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The Pitfalls in Inferring Risk from Financial Market Data
Robert R. Bliss

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What Can Account for Fluctuations in the Terms of Trade?
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Data Revisions and the Identification of Monetary Policy Shocks
Dean Croushore and Charles L. Evans

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Recent Evidence on the Relationship Between Unemployment and Wage Growth
Daniel Aaronson and Daniel Sullivan

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Supplier Relationships and Small Business Use of Trade Credit
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WP-00-28

What are the Short-Run Effects of Increasing Labor Market Flexibility?
Marcelo Veracierto

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Equilibrium Lending Mechanism and Aggregate Activity
Cheng Wang and Ruilin Zhou

WP-00-30

Impact of Independent Directors and the Regulatory Environment on Bank Merger Prices:
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WP-00-31

Does Bank Concentration Lead to Concentration in Industrial Sectors?
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WP-01-01

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4

Working Paper Series (continued)
Sub-Debt Yield Spreads as Bank Risk Measures
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WP-01-03

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WP-01-04

Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
The Regulated in Banking
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Jonas D. M. Fisher and Andreas Hornstein

WP-01-05

WP-01-06

WP-01-07

Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans

WP-01-08

Outsourcing Business Service and the Scope of Local Markets
Yukako Ono

WP-01-09

The Effect of Market Size Structure on Competition: The Case of Small Business Lending
Allen N. Berger, Richard J. Rosen and Gregory F. Udell

WP-01-10

Deregulation, the Internet, and the Competitive Viability of Large Banks and Community Banks WP-01-11
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Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards
Christopher R. Knittel and Victor Stango

WP-01-12

Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles

WP-01-13

A Real Explanation for Heterogeneous Investment Dynamics
Jonas D.M. Fisher

WP-01-14

Recovering Risk Aversion from Options
Robert R. Bliss and Nikolaos Panigirtzoglou

WP-01-15

Economic Determinants of the Nominal Treasury Yield Curve
Charles L. Evans and David Marshall

WP-01-16

Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders
Edward J. Green and Ruilin Zhou

WP-01-17

Earnings Mobility in the US: A New Look at Intergenerational Inequality
Bhashkar Mazumder

WP-01-18

The Effects of Health Insurance and Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones

WP-01-19

5

Working Paper Series (continued)
The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules
Daniel Aaronson and Eric French

WP-01-20

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Meredith A. Crowley

WP-01-21

Is the United States an Optimum Currency Area?
An Empirical Analysis of Regional Business Cycles
Michael A. Kouparitsas

WP-01-22

A Note on the Estimation of Linear Regression Models with Heteroskedastic
Measurement Errors
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WP-01-23

The Mis-Measurement of Permanent Earnings: New Evidence from Social
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WP-01-24

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and Market Discipline
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WP-01-25

Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in
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WP-01-26

Outsourcing Business Services and the Role of Central Administrative Offices
Yukako Ono

WP-02-01

Strategic Responses to Regulatory Threat in the Credit Card Market*
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WP-02-02

The Optimal Mix of Taxes on Money, Consumption and Income
Fiorella De Fiore and Pedro Teles

WP-02-03

Expectation Traps and Monetary Policy
Stefania Albanesi, V. V. Chari and Lawrence J. Christiano

WP-02-04

Monetary Policy in a Financial Crisis
Lawrence J. Christiano, Christopher Gust and Jorge Roldos

WP-02-05

Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers
and the Community Reinvestment Act
Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg

WP-02-06

Technological Progress and the Geographic Expansion of the Banking Industry
Allen N. Berger and Robert DeYoung

WP-02-07

Choosing the Right Parents: Changes in the Intergenerational Transmission
of Inequality  Between 1980 and the Early 1990s
David I. Levine and Bhashkar Mazumder

WP-02-08

6

Working Paper Series (continued)
The Immediacy Implications of Exchange Organization
James T. Moser

WP-02-09

Maternal Employment and Overweight Children
Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine

WP-02-10

The Costs and Benefits of Moral Suasion: Evidence from the Rescue of
Long-Term Capital Management
Craig Furfine

WP-02-11

On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation
Marcelo Veracierto

WP-02-12

Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps?
Meredith A. Crowley

WP-02-13

Technology Shocks Matter
Jonas D. M. Fisher

WP-02-14

7