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Federal Reserve Bank of Chicago

Gathering Insights on the Forest from
the Trees: A New Metric for Financial
Conditions
Scott Brave and R. Andrew Butters

WP 2010-07

Gathering Insights on the Forest from the Trees:
A New Metric for Financial Conditions∗
Scott Brave†, R. Andrew Butters‡
August 24, 2010

Abstract
By incorporating the Harvey accumulator into the large approximate dynamic factor
framework of Doz et al. (2006), we are able to construct a coincident index of financial
conditions from a large unbalanced panel of mixed frequency financial indicators. We
relate our financial conditions index, or FCI, to the concept of a “financial crisis” using
Markov-switching techniques. After demonstrating the ability of the index to capture
“crisis” periods in U.S. financial history, we present several policy-geared threshold
rules for the FCI using Receiver Operator Characteristics (ROC) curve analysis.
JEL Code: G01, G17, C22
Keywords: financial crisis, financial conditions, dynamic factor, EM algorithm, Harvey
accumulator, ROC curve, Markov-switching

1

Introduction

Financial innovation over the course of the past 40 years has made it difficult to capture
broad financial conditions in a small number of variables covering just a few traditional
financial markets. Recent research has instead focused on capturing financial conditions
from variables that span several traditional as well as more recently developed markets.
Here, we take this approach a step further applying the large approximate dynamic factor
methods of Doz et al. (2006) to a data set of 100 financial indicators capturing a broad array
of information on financial markets.
∗
The views expressed herein are not necessarily those of the Federal Reserve Bank of Chicago or the
Federal Reserve System. The authors would like to thank, without implicating, Alejandro Justiniano, Hesna
Genay, Spencer Krane, Jeff Campbell, Gadi Barlevy, Simon Gilchrist, Bill Nelson, and Federal Reserve Bank
of Chicago seminar participants. We would also like to thank An Qi and Christian Delgado for excellent
research assistance.
†
Federal Reserve Bank of Chicago: sbrave@frbchi.org
‡
Northwestern University: r-butters@kellogg.northwestern.edu

1

The information in these indicators is distilled in a single high frequency index, the
Financial Conditions Index, or FCI. What separates the FCI from other similar indexes is
that it allows for real-time examination of financial conditions from an unbalanced panel of
mixed frequency indicators. We show that increasing values of the FCI capture rising risk
premia, declining credit volumes, and deleveraging across the financial system.
To illustrate its usefulness, we relate the FCI to the concept of a “financial crisis”, using
differences in the mean and volatility in the index over time to identify a Markov-switching
process. Our results demonstrate that the FCI accurately captures the major events in
U.S. financial history over the past 37 years. Based on these major events, we then develop
threshold rules for the index using methods of analysis common to medical statistics to derive
a framework that balances the costs and benefits of identifying financial crises in real-time.

2

Literature

Measures of financial conditions generally involve a weighted average of a number of
financial indicators. Some prominent examples are the indexes of Illing and Liu (2006), Nelson
and Perli (2007), Hakkio and Keeton (2009) and Hatzius et al. (2010). Several of these indexes
use a method called “principal components analysis” (PCA) to estimate the weight placed
on each series. PCA interprets the importance of movements in any number of data series
based on their relative historical correlations. In this fashion, contradictory movements are
sorted out in a way that is consistent with the historical importance of individual indicators
to the broader financial system.
Typically, these PCA-based measures are constructed as the first principal component of
a set of underlying financial indicators, where “first” signifies that it is the element common
to all of the indicators that explains the largest amount of the total variation among them.
Standardizations vary, but often a positive index value is associated with deteriorating financial conditions with the magnitude measured in standard deviations from the sample mean
of the index.
This form of index construction has an equivalent representation as a restricted least
squares problem given a latent factor Ft and factor loadings Λ such that ε0t εt = N̂ IN̂ where
N̂ is the number of data series. Consider the linear model below, where Yit is a vector of
stationary variables that have been demeaned and standardized to have a unit variance:

Yit = Λi Ft + εit

(1)

Given a panel of sufficient size, Bai and Ng (2002) show that PCA estimates of the latent
factors are consistent.
Indexes of this kind also have the advantage of capturing the interconnectedness of financial indicators. The more interrelated an indicator is with its peers, the higher the weight
it receives in the index. This opens up the possibility that a small deterioration in a highly
2

weighted indicator may mean more for the state of financial conditions than a large deterioration in an indicator that receives little weight. In this way, PCA assigns a relative ranking
consistent with an indicator’s systemic importance.
The PCA method of index construction has become commonplace in the measurement of
business cycles. A prominent example is the Chicago Fed National Activity Index based on
the work of Stock and Watson (1999) which captures a single latent factor extracted from 85
variables describing U.S. economic activity. More recently, it has also begun to be applied to
financial variables. For instance, Hakkio and Keeton (2009) estimate their Kansas City Fed
Financial Stress Index, or KCFSI, from a sample of U.S. financial indicators on the health
of the banking system, debt, equity and money markets.
Variation in the frequency or availability of data, however, makes the PCA decomposition
above infeasible without alteration. For financial variables where higher frequency data is
generally available, the typical alteration consists of using only those variables with consistent
time series and/or moving to a monthly or even quarterly frequency of measurement. The
KCFSI fits this description, focusing on a small sample of indicators at a monthly frequency.
In contrast, Stock and Watson (2002) show how this problem can instead be addressed
by introducing an iterative estimation strategy. Their algorithm relies on the fact that
PCA reduces to ordinary least squares (OLS) estimation when Ft is known. From an initial
balanced subset of the data, one produces an initial guess for Ft by means of PCA, followed
by Λ by OLS projection onto this guess. Missing values are then replaced by their expectation
conditional on the observed data and estimates of Ft and Λ are updated until the sum of
squared errors for the complete data converges to some criterion.
As the size of the cross-section grows, Stock and Watson (2002) demonstrate that this
strategy produces a consistent estimate of Ft robust to weak serial correlation between the
idiosyncratic errors and within them across time. Furthermore, Bai and Ng (2004) show that
this is also true for dynamic factor models in the sense that the static factors estimated by
PCA can potentially span the same space as the true dynamic factors.
Hatzius et al. (2010) employ this “cross-sectional averaging” strategy to construct the
USMPFFCI, stretching the index history back to the early 1970’s compared with the early
1990’s for the KCFSI. They demonstrate that this method also has the advantage of incorporating information in a manner consistent with the development of financial markets that
have grown in importance over time, i.e. securitized debt, repo, and derivatives markets.
By doing so, they are able to compare the level of financial conditions during many known
periods of crisis while still taking account of financial innovation.
A shortcoming of their work, however, is that it, too, requires an initial balanced panel in
order to use PCA. Hatzius et al. (2010) relax this constraint by estimating a quarterly index.
The potential usefulness of high frequency financial indicators in a real-time setting where
decisions must be made is a feature we would like to retain in our FCI. Therefore, the model
we describe below will incorporate an unbalanced panel of mixed frequency indicators. Here,
we build off the work of Aruoba et al. (2009) in their measurement of business conditions at
a weekly frequency.

3

3

The model

Fundamentally, the model for the Financial Conditions Index is similar to any coincident
index model where the variation in a number of indicators is governed by one common source
(a factor) and an idiosyncratic term. In a purely static sense, equation (1) represents the
model, with t = 1, . . . , T̂ and i = 1, . . . , N̂ where in our particular baseline model T̂ is the
time series length of the longest available indicator and N̂ is the number of variables. Here,
Ft represents the common source of variation, or factor, in Yit . For our purposes, this factor
will serve as a proxy for the state of financial conditions.
In this model, each indicator can have different sensitivities to the common factor (controlled by the heterogenous Λi ), however, the overall responsiveness of each indicator to
changes in the factor remains constant over time.1 A unique characteristic of Yit in our
particular context is not only its size (in both the cross section and frequency domain) but
also that it contains series of varying reported frequencies and series that start and end at
different times within the sample.2
Where we depart from the previous literature is that our estimate of the unobserved factor
will take into account both the cross-sectional correlations in the data as well as any dynamic
correlations. In our model, any dynamic correlations will be absorbed in the dynamics of Ft .
Adding dynamics of some finite order (p? ) to the unobserved factor moves the model above
into the standard state space representation:

yt = Zt αt + t
αt+t = Tt αt + Rt ηt ,

(2)
(3)

where t ∼ N (0, H) and ηt ∼ N (0, Q).3
This model can be estimated using the EM algorithm outlined by Shumway and Stoffer
(1982) and Watson and Engle (1983) in order to obtain maximum likelihood estimates of
the system matrices and subsequently the unobserved factor. In general, the EM algorithm
requires one pass through the Kalman filter and smoother, and then re-estimation of the
state-space parameters using ordinary least squares estimation at each iteration.4 The resulting sequence of log-likelihood function valuations is non-decreasing, and convergence of
the algorithm is governed by its stability.5
1

This restriction can be relaxed. Consistency requires only that the factor loadings “change slowly” over
time. For further details, see Stock and Watson (2002).
2
In what follows, we restrict our attention to the period from 1973 onward during which at least 25 percent
of the series in the index have complete time series.
3
For a more descriptive formulation of the the system matrices and the state variables see section A.2.
4
A small alteration in the least squares step is required to account for the fact that the unobserved
components of the model must first be estimated.
5
For more details on the particular log-likelihood function and the methods used to implement the EM
algorithm see section A.3.

4

The EM algorithm proves advantageous in this setting because it allows for a complete
characterization of the data-generating process using incomplete data and combines two easy
to implement statistical methods. In addition, the typical critique of the algorithm, its slow
convergence rate, is not problematic in this setting due to the size of both the frequency and
cross section dimensions which allow for consistent initial estimates using the standard or
iterative PCA techniques described in Stock and Watson (2002).

4

Estimating the model

As described above, the EM algorithm utilizes the Kalman filter and smoother in order to
obtain the sufficient statistics necessary to re-estimate the system’s matrices using standard
ordinary least squares techniques. Due to the irregular observation of the data in our framework, two extensions to the standard Kalman recursion equations need to be made. The
first alteration involves setting up the Kalman filter to deal with missing values as discussed
by Durbin and Koopman (2001). The second modification involves including additional state
variables that evolve deterministically to properly adjust for the varying temporal aggregation properties of the mixed frequency data. Next, we give a brief summary of both of these
extensions to the standard filter as well as a more explicit formulation of the state-space
system matrices and the EM algorithm.

4.1

Missing Value Kalman Filter

Because of the aforementioned irregularity of observation in our data-set, as one moves
through time, the vector of observables (denoted yt ), changes size from period to period.
Figure 1 summarizes this pattern of data availability. The early part of the sample is dominated by quarterly and monthly variables. The number of weekly variables grows steadily
from the mid-1980’s through the end of the sample. Nineteen series span the entire sample
period (1971–2010), while the shortest series begins in 2009.
Consequently, as one proceeds through the Kalman filter and smoother, to accommodate
the partially observed vector yt? in the data one must use the known matrix Wt whose rows
are a subset of the rows of I(N̂ ) (such that yt? = Wt yt ) to alter the system matrices at that
particular point in time. Taking this Wt as given, the system matrices Z and H become
Z ? = Wt Z and H ? = Wt HWt0 , respectively. Substituting these matrices into the standard
filter and smoother equations allows one to proceed as usual through the recursive equations
outlined in the appendix.

4.2

Temporal aggregation and the Harvey Accumulator

Another unique characteristic that results from the irregular frequency of observation is
the different temporal aggregations inherent in our data-set. By applying the accumulator
of Harvey (1989), one can manage this data irregularity with relative ease. The goal of
5

the accumulator is to augment the state with a deterministically evolving indicator that is a
summary of all past values of the unobserved factor aggregated in such a way as to correspond
with the nature of the series observed at a frequency differing from the base frequency.
More specifically, variables viewed as a “stock”, or a snap shot in time, will not need such
aggregation of past realizations of the factor. Variables that correspond to sums or averages
over the higher base frequency, however, will need to accumulate all the higher frequency
factor realizations over that period in order to properly account for the contemporaneous
factor’s contribution to what is being observed.6 Any “stocks” that are differenced can be
interpreted as sum variables and treated as such.
Our data-set includes both variables that resemble “sums” as well as “averages,” in addition to indicators that are first differenced. Combining this with the weekly, monthly and
quarterly frequencies of observations, our particular model will need three Harvey accumulators in the state.7
Sum Variables Accumulator
For both the monthly and quarterly sums accumulator we follow Aruoba et al. (2009)’s
implementation of the Harvey (1989) accumulator. The accumulators for sum variables will
be denoted St . By construction, any sum accumulator should represent the current sum of all
of the factor realizations (base frequency) that have occurred within the current period of the
lower frequency of observation. Additionally, the accumulator should be defined recursively
so as to be included in the state space equations of (2)-(3). Analytically, the sum accumulator
evolves each period by the following equation:
St = st St−1 + αt
where st is a calendar determined indicator that evolves according to:

st =

0 if t is the first period (base frequency) within the lower frequency
1 otherwise

For notational purposes, it is assumed in what follows that αt is an AR(1) process defined
by αt+1 = ραt + Rηt . Incorporating this representation of the accumulator into the state
space model follows from a simple substitution of the contemporaneous factor as outlined
by Aruoba et al. (2009).
6
Variables have different interpretations of how they are accumulated over the period of time they are
observed. Some, such as monthly corporate bond issuance, represent sums of the higher frequency (in
this case weekly issuance). Other variables, like Citigroup’s monthly asset-backed security yield spread,
represent averages of the higher frequency (weekly spreads). Ultimately, this difference will lead to a different
construction of the accumulator.
7
One does not need a Harvey accumulator for series that are observed at a weekly (the base) frequency,
as well as any series that are stocks. In our data-set we have an accumulator for (1) monthly averages (2)
monthly sums and (3) quarterly sums.

6

Average Variables Accumulator
For the purposes of exposition, we will denote the desired accumulator for the average
variables with Mt , and derive it as though we are aggregating from a weekly base frequency to
monthly observations of the financial indicators.8 By construction, this accumulator should
represent the current average of all of the factor realizations (occurring every week) that
have occurred within the current month (frequency that is being observed) and be defined
recursively for seamless addition to the state space equations of (2)-(3). Analytically, the
average accumulator evolves each period by the following equation:

Mt =

(mt − 1)Mt−1 + αt
mt

where mt is a calendar determined indicator that evolves:

mt


if t is the first week of the month
 1
2
if t is the second week of the month
=

etc.

Explicitly including the accumulator in the state requires augmenting the state and some
substitution. The resulting formulation is given by:


4.3

αt
Mt




=

ρ

0

ρ
mt

mt −1
mt



αt−1
Mt−1




+

R



R
mt

ηt−1

EM Algorithm

At each iteration of the EM algorithm, one pass through the Kalman filter and smoother
is made using the system matrices Z, H, T, Q, P1 and a1 as well as the Harvey accumulator
and missing observation extensions. By utilizing both the smoothed estimates and their
covariance matrices, one can update the expectation of the conditional loglikelihood function;
the (E) step. Then, using OLS techniques, the system matrices are re-estimated; the (M)
step. This process will yield a non-decreasing sequence of log-likelihood values. A concise
version of the log-likelihood, and the one that can be computed at each iteration, is as follows:
T̂

1X
0
−1
log L = −
(log |Ft? | + vt? Ft? vt? )
2 t=1

(4)

8
All methods outlined in this section generalize fully to any particular combination of base and observation
frequencies that one might encounter with the only necessary modifications occurring in the evolution of the
calendar indicator mt or st .

7

Now that the (E) and (M) steps have been completely defined, one can iterate between
the two steps until (4) becomes stable.9 Further details of the algorithm can be found in the
appendix.

5

What is the FCI Capturing?

Table 2 lists all of the 100 financial indicators in our FCI along with their stationary
transformations and estimated weights. The weights, or factor loadings, are a useful way of
interpreting the systemic relationship between the indicators in the index. With the large
approximate dynamic factor method as with PCA, the resulting index and weights are only
identified up to scale. To make the weights comparable to PCA, we have scaled each according
to the PCA convention that E(Λ0 Λ) = I.

5.1

Contributions to the FCI

Credit risk measures tend to be positive contributors to the index, while money and
credit aggregates and measures of leverage tend to be negative contributors. This pattern of
increasing risk premia and declining credit volumes and leverage is consistent with tightening
financial conditions and provides the basis for the FCI’s interpretation. The way in which
leverage enters the index may seem counterintuitive, but is in line with the findings of Adrian
and Shin (2009) that leverage is procyclical. In this way, the process of deleveraging appears
in the FCI as an indicator of deteriorating financial conditions.
Without index dynamics, it is not possible to fully capture the risk inherent in the buildup of leverage. Our dynamic framework relaxes this constraint allowing the procyclical nature
of leverage to be reflected in the estimated dynamic process for the index. A large build-up
of leverage that pushes the index well below its sample mean will generate a tendency to
reverse this decline that depends on the estimated degree of mean reversion.
Taking into account the financial markets represented in our FCI, we have segmented our
100 financial indicators into three categories: Money Markets (28), Debt/Equity Markets
(27), and the Banking System (45). Measures of the health of the banking system capture
41 percent of the variation in the data explained by the FCI, followed by money market
measures at 30 percent and debt and equity markets at 28 percent.
The Money Markets category is comprised mostly of interbank, repo, swap, and commercial paper spreads and is the basis of most other financial conditions indices. These measures
primarily capture credit risk and liquidity. Some of the biggest contributors to the FCI in
this category include the 2-year swap and TED spreads as well as the 1-month nonfinancial
A2P2/AA commercial paper credit spread and repo market volume. The latter two variables
9
As a convergence criterion we used | log L(k)−log L(k−1)/((log L(k)+log L(k−1))/2)| < 10−6 . Important
to note, though, is that because the initial estimates are consistent, the EM algorithm converges rather
quickly; within 150 iterations.

8

are fairly unique to our FCI, as are the measures of open interest in money market derivatives
that we include in this category.
In the Debt/Equity Markets group are mostly equity and bond price measures. In terms of
equity prices, the largest weights are given to the index of volatility for the S&P 500, the VIX,
and the relative price of financial stocks in the S&P 500. Like Hatzius et al. (2010), we also
include here residential and commercial real estate prices and measures of stock market capitalization. In terms of bond prices, the index covers corporate, municipal, and asset-backed
bond markets. Bond spreads like the high yield/Baa corporate and financial/corporate enter
strongly here with large positive weights, but so do non-mortgage, mortgage, and commercial
mortgage asset-backed bond spreads and credit default swap spreads tied to corporate bonds.
The Banking system category is comprised mainly of survey-based measures of credit
availability and the assets and liabilities of commercial and “shadow” banks. The Senior
Loan Officer Opinion Survey questions on commercial bank loan spreads and standards all
enter strongly into the index as do several other measures of business and consumer credit
conditions. The Credit Derivatives Research Counterparty Risk index measured as the average of the credit default swap spreads of the largest 14 issuers of CDS contracts also receives
a large weight in this category, with the remaining weight split roughly evenly between measures of asset quality and measures of commercial and “shadow” bank lending and leverage.

5.2

Comparisons with other indexes

It is important to keep in mind that the estimated factor loadings are not unique. The
same estimated index may have more than one set of weights that are consistent with it.
Therefore, to make comparisons it is best to compare the indices, themselves. To establish a
reference scale for the index, in what follows we have expressed it relative to its sample mean
and standard deviation. A zero value is, thus, equivalent to the sample mean, and deviations
from zero are measured in standard deviation units.
Figure 2 compares our FCI against the USMPFFCI and KCFSI. All three indexes tell
a similar overall story: the 1970’s and 80’s were a particularly stressful period for financial
markets that only recent years can match in magnitude. Compared to the USMPFFCI,
the differences with the FCI are most considerable in the recent period with much of this
attributable to the FCI’s broader coverage of high frequency data on securitized debt, repo,
and derivatives markets.
In contrast, the KCFSI is more similar to the FCI in the recent period, but with higher
peaks and valleys. This stems mostly from the fact that it weights more highly recent events
given the generally lower volatility of the index and its financial indicators post-1984. To
see this, consider figure 3 which depicts the KCFSI and the initial PCA estimate of the FCI
beginning in 1997. The initial estimate resembles the KCFSI to a high degree, although the
difference in data coverage is apparent even here at times.

9

5.3

Comparisons across time

Our method obtains a significant time series for the FCI just as Hatzius et al. (2010)
do for the UMPFFCI. One side effect of this is a much higher mean due to the volatility in
the 1970’s and 80’s that the longer indexes capture. This suggests that comparisons across
time using indices of shorter duration like the KCFSI may be biased. However, in some ways
they may also be more relevant. Financial markets since the early 1980’s have undergone
significant transformations. If the relationships between financial indicators have changed,
i.e. the weights have changed, then a shorter sample makes sense.
To test this hypothesis, we also constructed the FCI using only data from the post-1984
period. Figure 4 plots both the shorter sample and full sample FCI.10 As with the KCFSI
and the FCI, for the period of time in which the two overlap most of the difference appears
in their levels. This suggests that it is primarily the lower volatility of the post-1984 period
that is driving what differences we do see, and is line with broader findings on the “Great
Moderation.”
The factor loadings in table 2 confirm this with small differences between indexes for
most variables. However, the post-1984 index does shift around weight between the three
broad groups of financial indicators. Figure 5 displays the contribution of each of the these
groups to the total variance of the 100 financial indicators explained by the full and post-1984
sample indexes. One can see from this figure that the money market variables explain much
more of the post-1984 index with the extra weight shifted almost entirely from the banking
system group.

5.4

Comparisons across indicators

An alternative to using a shorter sample period is to instead focus only on the subset of
financial indicators whose history extends back over most of the sample. In this way, we can
judge if it is possible to consistently capture financial conditions over an extended period
without incorporating information from more recently developed financial markets. Figure 6
plots the FCI computed from the 39 financial indicators in our data-set that extend back to
1978 against the 100-variable index over the same time period.
One can see from both figure 6 and the factor loadings in table 2 that the smaller-variable
index is capturing something very different than the larger one. Except for the most recent
period where both indexes demonstrate large positive values, the two are highly negatively
correlated. Well-known periods of deterioration in financial conditions, such as the late 1970’s
and early 1980’s, appear in the narrower index as very loose periods for financial conditions.
In fact, looking at the factor loadings in table 2, many of them are of the opposite sign
compared to the weight given to the same variable in the 100-variable index.
The above suggests several explanations for what may be confounding the estimation of
the smaller variable index in a way that does not appear in our larger variable FCI. First,
10

Just as with the full sample index, we do not consider the first two years of estimates so that the shorter
sample index begins in 1987. At this point, over 50 percent of the indicators have complete time series.

10

the 100-variable index appears to be spanning a space that is larger than the 39-variable
index. This is not surprising given what we know about financial development over the past
40 years and the greater inclusion of these financial markets in the larger index. Second, the
subset of indicators we have chosen for the smaller variable index contains a bias towards
those also more likely to be affected by the change in volatility post-1984. This can be seen
in the fact that many of the same indicators in the 39-variable index also show large changes
in their factor loadings in the post-1984 100-variable index.

5.5

Stability of the FCI

As an example of where the smaller index seems to be going awry, consider the Treasury
yield curve indicators. Measures from both the short and long end of the curve get large
positive weights in the 39-variable index, meaning that as the yield curve steepens the index
rises. In contrast, the weight they receive in the larger 100-variable index is much smaller
and negative, meaning that as the yield curve steepens financial conditions tend to improve.
However, even this relationship is not stable over time. In the shorter sample 100-variable
index, the long end of the curve receives a large positive weight while the short end receives
a smaller negative weight.
This pattern suggests to us that the instability over time and indicators described above is
due to changes in the level and volatility of economic growth and inflation. The high inflation,
high negative growth periods of the late 1970’s and early 1980’s suggest a correlation pattern
in the data that is counterintuitive to the low inflation, high negative growth period of the
recent crisis. The fact that the larger 100-variable index does not exhibit to the same degree
these problems over time suggests to us that it is spanning a space that is less sensitive to
the level of economic growth and inflation. However, it does still appear to be somewhat
influenced by the change in volatility post-1984.

6

Identifying “Financial Crises”

The measure of financial conditions that we have constructed is not unlike a temperature
in a person. Consequently, one might expect to have to address some of the same issues faced
by medical practitioners in utilizing a patient’s temperature in a diagnosis. Specifically, (1)
what is a “normal” level and subsequently a level that would warrant concern?; (2) are the
risks associated with both extremely low values and high values the same? ; and (3) how well
does this measure predict the true underlying state of the patient? By implementing Markovswithcing and receiver operator characteristics (ROC) curve techniques, we will attempt to
address each of these issues.
What proves to be a “normal” temperature in a person often tends to be a range rather
then a particular value. Similarly, it makes sense to consider that what constitutes a “normal”
value for financial conditions could also be a range as well as something that might change
over time. In practice, redefining normal for every person, or in this context point in time,
11

would be counterproductive. Instead, the average across the population is usually a suitable
starting point.
Because we have already standardized the FCI to have a mean of zero and unit variance,
a value of zero seems a reasonable place to initially deem as normal. It captures the average
level of financial conditions in our sample, and corresponds to a weighted average of measures
of risk, liquidity, and leverage all expressed relative to their average levels over the same time
period. In an attempt to build a “range of normal,” it is common to simply select some
number of standard deviations from the sample mean, or, equivalently, “build the range” by
including everything that falls within a desired percentage of the population.
A possible source of bias in this kind of reasoning, however, might result if a priori we
believe that some members of that population are in fact “really sick” and their temperatures
are skewed as a result. Including these members when calculating the average will reduce
the power of this metric to distinguish between states of the world, i.e. a “healthy state”
and a “sick state”. For us, the “really sick state” conforms with the notion of a financial
crisis, where a number of financial indicators are deviating substantially from their historical
norms. Ideally, we would want to develop the concept of normal for the FCI with some sort
of reference to these different segments of the population.
If we envision every week in our sample as many different patients, some “sick” and some
“normal,” and their particular value of the FCI as their “temperature” we can begin to
build the intuition behind an optimal range or value for normal. Ideally, we would have a
professional consensus on which of these patients turned out to be in fact “sick” and “normal”,
much like NBER produces for recessions and expansions. Unfortunately, what we have to
resort to is the historical accounts of various financial events in U.S. history over the sample
period we examine.
Table 3 provides a list of 5 financial crisis episodes in U.S. financial history over the last
40 years along with some of the major events that occurred during each of them. It will be
these episodes that we take as given as the “sick” members of the population. In general,
they are associated with periods of high risk premia, low liquidity, and declining leverage.
To arrive at these episodes, we conducted a survey of the literature on banking and financial
crises over the past 40 years. The dating of each of the five episodes is our interpretation of
the consensus in the literature as to the beginning and ends of each crisis.11
As a robustness check on the validity of these episodes, we begin by using a Markovswitching model to estimate a two state model of financial conditions and the probabilities of
each state. Ultimately, the interpretation of the two states estimated by the Markov-switching
model is ambiguous, but the identifying restrictions we impose will help to characterize one
state as a “crisis period.” Because we avoid using the state probabilities estimated to define
our financial episodes explicitly, similarity in the dates of high crisis probability to the ones
used to define our crisis episodes lend some credibility to the threshold policy analysis that
will follow.
11

Some examples include: FDIC (1984), FDIC (1997), Laeven and Valencia (2008), Reinhart and Rogoff
(2008), Minsky (1986), Spero (1999), Schreft (1990), Cameron (2008), and El-Gamal and Jaffe (2008)

12

State 1
State 2
State 3

µSt
ςSt
-0.01 0.00
0.18 0.01
-0.01 0.01

Table 1: Estimated parameters from Markov-switching model

6.1

Markov-switching model of financial conditions

Taking the estimated FCI as given, we estimate a variant of the Markov-switching model
of Hamilton (1989) to characterize changes over time in the mean and variance of the index.
It will be assumed that there are three states of the world, denoted by St ∼ {St = 1, St =
2, St = 3}. Equation (5) defines the univariate time series specification used in our estimation
of the Markov-switching model
Ft (I − A(L)) = µSt + εt

(5)

where εt ∼ N (0, ςSt ) and St denotes the three states of the world. In this particular
specification, the FCI (Ft ) will have some finite ordered dynamics that are state in-variant,
A(L), while the mean and variance of the errors of the FCI will vary by state, µSt and ςSt ,
respectively.12
As noted earlier, our interpretation of the state that represents “really sick” patients is
characterized by periods of high risk premia, low liquidity, and deleveraging. In the context
of the Markov-switching model, we would expect that if in fact the states that the model is
distinguishing conform with this interpretation, a “high mean, high variance” state would
emerge. In terms of estimation, there is no restriction that these parameters would have to
be grouped in this way. Given what we found above concerning the reduction in volatility
post-1984, we also want to allow for a third state that could potentially be a combination of
the other two.
Table 1 displays the mean and variance estimated for each of the three states. In fact, with
a mean of -0.01 and variance of 0.00 for state 2, and a mean of 0.18 and variance of 0.01 for
state 1, the Markov-switching model separately identifies a high mean, high variance and a
low mean, low variance state. The third state has the same mean as state 1 and the same
variance as state 2 consistent with the notion of a high volatility state at a lower mean due
to the overall lower volatility in the post-1984 period.
Figure 7 plots the FCI in panel A where the shading indicates the particular crisis episodes
found in table 3. The estimated probability of what we refer to as the crisis state, state 2, is
shown through the entire sample in panel B. It is clear from figure 7 that what we are calling
12

We use three lags of the FCI in the estimation that follows, although results are qualitatively similar
with four lags instead. Substantial loss of degrees of freedom prevents the use of more than four lags.

13

a crisis state coincides closely with the crisis episodes of table 3. During the majority of these
episodes, the probability of a crisis state is quite high, particularly surrounding their peak
events. The prominent exception is the fourth episode where volatility was higher, but at a
lower level. This episode should, however, be picked up by the third state that accommodates
this possibility.
Panel C displays the combined probability of states 2 and 3 through the entire sample. The
fourth episode is indeed classified as a crisis period under this broader definition. However, so
are several other periods outside of the episodes we consider.13 Almost all of these instances
occur in the period post-1984 as one would expect given the parameter estimates above and
our previous subsample results for the index.
A particularly interesting period where states 2 and 3 receive a high probability is the
2002-2007 period. State 2’s probability during this period is essentially zero, but state 3
receives a very high probability on more than one occasion despite the fact that the index
during this period changes very little over time. This period, in other words, confounds our
model. The persistently negative values of the FCI during this time are consistent with state
3 in that they have a similar mean to state 1, and the variance during this period is only
marginally different than several other instances of where there is a high probability of state
2.
This result is interesting in two respects. First, because it may indicate that state invariant
dynamics are not reasonable in this case. More interesting, however, is the fact that this is
the period leading up to the most recent crisis. The model may be signaling something
very different about this period, i.e. that financial conditions below their historical average
for a significant period of time may contain information on future crises. It would not be
unreasonable to imagine a “sickness” that was instead linked closer to periods of low risk, high
liquidity, and increasing leverage. This particular interpretation we leave to future research.

6.2

Receiver Operator Characteristics (ROC) curve analysis

The above results provide some justification for the broad categorical descriptions of
different periods in U.S. financial history in table 3. Here, we develop a unique threshold
rule that will be used to identify the “crisis” state of financial conditions in real-time based
on past instances of U.S. financial crises. To do so, we follow the approach used by Berge
and Òscar Jordà (2009) in estimating optimal threshold values for common business cycle
indicators including the Chicago Fed National Activity Index.
The Social Planner’s Utility Function
The nonparametric estimation strategy of Berge and Òscar Jordà (2009) requires that we
categorize each observed value of the FCI as a “crisis” or “non-crisis” period as in table 3
13

Most of these instances include major events that were excluded from the other episodes for various
reasons. In fact, it would be rather easy to group the mid 1980’s and early 1990’s with the third episode
encompassing the S&L crisis.

14

and then place relative weights on the utility from correctly predicting each of these states
and the disutility from making a false positive versus false negative evaluation of the state of
financial conditions. By varying these relative utility and disutility weights, we can develop
boundaries for the index corresponding with competing alternatives for addressing the state
of future financial conditions.
Keep in mind, however, that this analysis remains subject to the Lucas critique in that
it holds fixed both the reaction of financial markets to past policy and past policy to past
financial market events. At best, what it can answer is only what level of the index has
been associated with crisis conditions in the past. Only a fully articulated model of the
financial system and the policy process can tell you how both policymakers and financial
market participants may respond to current and future events. Presumably, however, even
such a model would take account of past responses which is what we detail here.
Consider the derivation in Berge and Òscar Jordà (2009),
T P (c) = P [Ft ≥ c||St = 1]
F P (c) = P [Ft ≥ c||St = 0]

(6)
(7)

with St ∈ {0, 1} indicating the non-crisis and crisis states of financial conditions, respectively. T P (c) is typically referred to as the true positive, sensitivity, or recall rate, and
F P (c) is known as the false positive or 1-specificity rate. The relationship between the
two is described by the receiver operating characteristics (ROC) curve. With the Cartesian
convention, this curve is given by
{ROC(r), r}1r=0

(8)

where ROC(r) = T P (c) and r = F P (c). Figure 8 depicts this curve and calculates the
area under the curve for the FCI using the crisis periods in table 3. The closer to one the
area under the curve the more predictive the index is of these periods.
The social planner’s utility function for the classification of the FCI at each point in time
as a crisis or non-crisis period is expressed as in Baker and Kramer (2007),
U = U11 ROC(r)π + U01 (1 − ROC(r))π + U10 r(1 − π) + U00 (1 − r)(1 − π)

(9)

where Uij is the utility (or disutility) associated with the prediction i given that the true
state is j, i, j ∈ {0, 1} and π is the unconditional probability of observing a crisis episode in
the sample. Utility maximization implies that the optimal threshold value c is given by,
∂ROC
∂r

=

U00 − U10 1 − π
U11 − U01 π
15

(10)

that is the point where the slope of the ROC curve equals the expected marginal rate of
substitution between the net utility of accurate crisis and non-crisis episode prediction.
Essentially, in this type of policy analysis one is weighing the costs of a Type I versus Type II error relative to the benefits of correctly predicting the true state. This intuitively amounts to deciding on whether one wants to put more emphasis (in utility terms)
on correctly identifying either state, or possibly equal weight to both. An example of assigning equal weight to both identifying crisis and non-crisis episodes would be assigning
U00 = U11 = 1, U01 = U10 = −1.
On the other hand, if one wanted to put all the emphasis on correctly identifying financial
crises, and subsequently no emphasis on the likely error of identifying the other state as a
crisis, we could assign the utilities this way: U00 = 0, U11 = 1, U01 = −1, U10 = − where
 needs to be small but non-zero in order to prevent the policy utility function from being
degenerate. Finally, a threshold rule that puts more emphasis on identifying non-crisis periods
could be identified by using a utility function defined this way: U00 = 1, U11 = 0, U01 =
−, U10 = −1.
The optimal thresholds defined above form a particular subset of the estimated index
values consistent with three parameterizations of the level sets of the social planner’s utility
function. Graphically, each policy attempts to find the unique intersection of the linear utility
function with the convex ROC curve. A policy placing a very steep penalty on missing an
occurrence of a financial crisis thus looks to intersect the upward sloping portion of the ROC
curve. A policy that places a relatively larger penalty on missing an occurrence of a non-crisis
period does the opposite and instead intersects the flatter portion of the ROC curve. The
equal weight, or “unbiased”, policy falls somewhere in between the other two on the ROC
curve.
Alternatively, consider the following thought experiment with regard to the FCI depicted
in figure 9. Draw a horizontal line across the graph at the highest value of the index that
does not fall in a period categorized as a crisis period. This defines the threshold for the
index that puts a very large weight on correctly identifying non-crisis episodes and avoiding
false positives. Similarly, draw a horizontal line across the graph at the lowest value of the
index that falls in a period of financial crisis. This defines a threshold which puts a very
large weight on correctly identifying non-crisis periods and avoiding false negatives.
With relatively equal weight on all four utilities (disutilities), Berge and Òscar Jordà
(2009) derive a threshold value that balances the need to catch financial crisis periods in
advance with the desire to avoid assigning movements in the FCI at low levels to a financial
crisis. In essence, this derivation “rebases” the index based on the historical financial crises
we have identified so that the optimal threshold now takes on a similar meaning for the
probability of a crisis period that a zero value of the index does for historical financial
conditions. It will, however, be sensitive as to how we date periods of past financial crisis.
We have found through trial and error that this sensitivity is not very high as long as
the beginning and end of the episodes are liberally defined. Small changes in the dating of
crisis episodes can have a big impact on the upper and lower thresholds defined above, but

16

the equal weighting method tends to balance out these changes so that the optimal threshold
produced varies very little when the unconditional probability of a crisis is well defined. This
is somewhat reassuring given that in real-time it may be difficult to assess the beginning and
end of a new episode.
Interpreting the ROC thresholds
Interestingly, a policy which equally weights the costs and benefits of identifying a crisis
episode suggests that even financial conditions slightly below their historical trend can be
associated with a financial crisis. The resulting threshold for the index in this case is a
negative number (-0.4). This result makes sense intuitively as it is very apparent in figure
9 that the transition in many cases into and out of a crisis episode is characterized by a
sudden and sharp deviation from below trend, often greater than a standard deviation in
size. Furthermore, as the index is currently defined, several of these episodes are preceded
by periods of persistently negative index values.
In essence, the ROC analysis above suggests that the relevant baseline for financial conditions based on historical crisis episodes is not necessarily the sample mean of the FCI. Given
what we found above in relation to the change in the mean of the FCI over time, this is
not surprising. However, one could also imagine a policy that puts more weight on avoiding
severe crises and less weight on crisis episodes like the third and the fourth in table 3. Here,
even though it remains sensitive to the dating of crisis episodes, the upper threshold value
may be a more relevant benchmark.
Alternatively, one could consider the range between the equal weighted and upper threshold as an early indicator of crisis financial conditions. We refer to this range in figure 9 as a
“Reactionary” policy for classifying financial conditions as it puts progressively more weight
on avoiding incorrectly classifying increasing levels of the FCI as indicating a crisis. A similar
definition can also be applied to the range of values between the lower and equal-weighted
thresholds. In this case, a “Pre-emptive” policy for classifying financial conditions would
put progressively more weight on avoiding missing the early signs of a financial crisis at low
levels of the FCI. The union of the two ranges can be considered as a “range of normal” in
the sense above that most instances of crisis and non-crisis weeks fall outside this range.
It is helpful to consider what each of the above policies would have meant in hindsight
for the most recent crisis compared to others in the past. An unbiased policy first signals
the development of crisis conditions in August 2007 nearly in step with our dating. This is a
feature common to several of the crises we consider, although both the late 1970’s and 90’s
would have registered some false positives using this policy. In contrast, a reactive policy
would not have signaled concern until the summer of 2008, and not consistently until the
post-Lehman period. It also would have entirely avoided classifying the the 1998-2002 period
as a crisis. The latter is the only instance of a crisis where this would have been the case.
Conversely, in the post-Lehman era the FCI remained in the region between the reactive
and unbiased policy until late 2009. Most recently, it has fluctuated within a small range
around this policy threshold suggesting financial conditions have returned to levels consistent
17

in the past with non-crisis periods. For a brief period following the spring 2010 European
debt crisis, it once again breached this threshold, but subsequently has returned below it.
However, in contrast to the aftermath of all of the other episodes, it remains above its level
just prior to the crisis. As such, a pre-emptive policy would still classify its most recent
behavior as characteristic of crisis conditions.
The “range of normal” interpretation, on the other hand, accords well with the Markovswitching analysis. The periods of highest probability for both types of crises tend to fall
above a reactionary and below a pre-emptive policy. Not much is gained from the former,
but the latter is instructive. For instance, such a policy would have raised red flags several
times during the period from 2002-2007. The same can be said of the late 1970’s and early
1980’s, although it applies just as well to the mid-1990’s. Thus, although ROC analysis is
not very well suited for more than two states of financial conditions, it appears a case could
be made based on our results that they are consistent with the three states considered above.

7

Conclusion

In this paper, we outlined the econometric methods needed to incorporate the most
general set of indicators necessary to build a real-time metric of financial conditions. To
provide evidence of its applicability, Markov-switching methods were used to evaluate the
index’s ability to capture well known financial crises in U.S history. Then, using ROC analysis
threshold rules were developed to help predict future crises based on the level of the index
during past crises and subjective utility weights.

18

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20

A

Appendix

A.1

Kalman Filter and Smoother Recursive Equations

The Kalman filter and smoother equations are a standard tool for producing forecasts
and smoothed estimates of state-space models. Combined with the assumption of Gaussian
errors, it can be shown that the recursive equations of the filter will yield the minimum meansquared error estimate within the class of linear estimators. In this paper, we will adopt the
notation of Durbin and Koopman (2001) and define the Kalman filter equations as follows.14
With a1 and P1 given, the filter equations are
vt = yt − Zat
Ft = ZPt Z 0 + H
Kt = Tt Pt Z 0 Ft−1
Lt = Tt − Kt Z
at+1 = Tt at + Kt vt Pt+1 = Tt Pt L0t + RQR0
The equations for the backwards smoother are given by15 :
Nt−1 = Z 0 Ft−1 Z + L0t Nt Lt
rt−1 = Z 0 Ft−1 vt + L0t rt
α̃t = at + Pt rt−1
Vt = Pt − Pt Nt−1 Pt
Jt = Pt−1 L0t−1 (I − Nt−1 Pt )
with rT̂ = 0, and NT̂ = 0.

A.2

Building the state

This section gives a more detailed explanation of how to build the system matrices of the
state space, as well as how to obtain the initial estimates needed to begin the EM algorithm.
The unobserved factor is assumed to have some finite order dynamics p? . Using standard OLS
techniques on a PCA estimated version of the factor Ft◦ , an initial guess of these dynamics
are generated and described by vector ρ◦ .16 Augmenting the state to include p? lags of Ft
yields the following state equation, with αt = [Ft−i ] for i = 0, . . . , p? − 1:

αt+1 =

ρ
?
I(p − 1) 0p? −1×1


αt +




1 ηt

Now, specifying
14

For more details on the derivation of these equations, see (Durbin and Koopman, 2001, 64–73).
It should be noted that the additional matrix Jt , is being calculated so that the maximization step in
the EM algorithm can be defined more easily.
16
The BIC criterion was used on the initial guess of the factor from the cross-sectional PCA method to
determine the number of lags to be 15.
15

21


T̃ =

ρ
I(p? − 1) 0p? −1×1



one can augment the state (currently a p? long vector), by the additional states needed
for each of the accumulators derived in section (4.2) to yield the state equation (taking the
ρ dynamics above as given):







αt
T̃
 Mt  =  ρ
mt
St
ρ

0
mt −1
mt

0







1



αt−1
0 0
 0p? −1×1 
 ηt−1
0 0   Mt−1  + 
1


m
t
St−1
st 0
1

(11)

It should be noted that as written above the T̃ within the general transition system matrix, T , here is time invariant, and subsequently the dynamics being estimated (essentially a
re-estimation of ρ) at each iteration of the EM algorithm are from a time invariant system.
However, our (accumulator augmented) state transition system matrix (as well as the coefficient matrix on the ηt ) does vary over time due to the different number of weeks in a given
month, mt , or quarter, qt , which also must be carried in the state vector for our purposes.
Moving to the measurement equation, assume that a priori the vector of factor loadings for
each series Λ is known.17 Then, taking the state equation (11) as given, the Z measurement
system matrix is simply a N̂ by p? + 3 matrix, where each row has the particular loading λi
in either the first column (if it corresponded with a weekly or stock variable) or one of the
last three columns (corresponding to one of the accumulators i.e. monthly average, monthly
sum, or quarterly sum) and zeros everywhere else. The initial guess of H is the diagonal
matrix of the variances of the residuals from the initial PCA estimate of the factor, σi I(N̂ ).18

A.3

The EM Algorithm defined

Now that the system matrices have all been defined, the EM algorithm’s updates of each
of these system matrices can be explicitly defined. With initial estimates of Z, H, and T,
we can begin to run algorithm to obtain the maximum likelihood estimates of each. The
log-likelihood function for the complete set of data α1 , . . . , αT̂ , y1 , . . . , yT̂ can be written in
the form19
17
The initial guess of these loadings, Λ◦ , will in fact be the loadings obtained from the PCA estimate of
the factor mentioned above.
18
The lack of identification that is common to these models requires that we restrict the scale of either
the factor loadings, as in PCA, or the factor. We use the normalization of Doz et al. (2006) and restrict the
variance of the state disturbances to be 1 to set the scale of the factor.
19
In what follows, we drop the R matrix in the notation of the log-likelihood for notational convenience.
Due to the fact that there is only one factor and that one is not estimating the dynamics or variances of the
deterministic accumulators, this has no substantive implications.

22

1
1
log L = − log |P1 | − (α1 − a1 )0 P1 (α1 − a1 )
2
2
T̂
T̂ − 1
1X
−
log |Q| −
(αt − T αt−1 )0 Q−1 (αt − T αt−1 )
2
2 t=2
T̂

1X
T̂
(yt − Zαt )0 H −1 (yt − Zαt )
− log |H| −
2
2 t=1

(12)

In order to calculate the conditional expectation defined in the log-likelihood function,
it is convenient to define the conditional mean and covariance of the state given all of the
observed data from the Kalman smoother recursive equations: the covariance matrix of αt
and αt−1 being the additional equation in the Kalman smoother mentioned before.
E(αt |y1 , . . . , yT̂ ) = α̃t
cov(αt |y1 , . . . , yT̂ ) = Vt
cov(αt , αt−1 |y1 , . . . , yT̂ ) = Jt0

(13)
(14)
(15)

Now, taking conditional expectations yields:
1
1
G(a1 , P1 , T, Z, Q, H|y1 , . . . , yT̂ ) = − log |P1 | − tr {P1 (V1 + (α̃1 − a1 )(α̃1 − a1 )0 )}
2
2
T̂ − 1
1 
−
log |Q| − tr Q−1 (C − BT 0 − T B 0 + T AT 0 )
2
2

T̂
1
− log |H| − tr H −1 (F − EZ 0 − ZE 0 + ZDZ 0 ) (16)
2
2
where tr denotes trace and
PT̂ −1
P
A = i=1
(Vi + α̃i α̃i0 )
D = T̂i=1 (Vi + α̃i α̃i0 )
P
P
0
B = T̂i=2 (Jt0 + α̃i α̃i−1
) E = T̂i=1 (yi? α̃i? )
P
P
0
C = T̂i=2 (Vi + α̃i α̃i0 )
F = T̂i=1 (yi? yi? )
It should be noted that yi? and α̃t? denotes only using periods in which yt is observed.
Now taking the partial derivative with respect to each of the system matrices T, Z and H of
the conditional expectation log-likelihood yields the following updating equations for each of
the system matrices.

23

EM Updating Equation 1. The new estimate for the transition matrix T 0 that maximizes
the conditional loglikelihood function in (16) is given by:
T 0 = BA−1
EM Updating Equation 2. The new estimate for the observation equation matrix Z 0 that
maximizes the conditional loglikelihood function in (16) is given by:
Z 0 = ED−1
While the above equations derive estimates for an unrestricted coefficient matrix, in our
particular model every indicator only loads onto one particular element in the state; either
the contemporaneous value of the factor or one of the accumulators. Subsequently, one can
estimate each particular loading in Z 0 by running individual OLS regressions. If one selects
the particular row in both the yt? and α̃t? matrices when constructing E and D, which become
scalars, then the particular loading λij for indicator i which loads onto the j state variable
E
that gets put into Z will be Dijj .
For the purposes of defining the new estimate H 0 , taking into account this restriction on
the coefficient matrix Z, it will make sense to define each of the N̂ elements along the diagonal
of H separately. With new estimates of λij , the expected log-likelihood with respect to H
can be separated into N̂ different equations with the optimal value of Hi for the indicator i
that loads onto state variable j defined as:
EM Updating Equation 3. The new estimates for each of the elements in the diagonal
matrix H 0 that maximizes the conditional loglikelihood function in (16) is given by:
Hi0 =

1
T̂ ?

? 2
(yi? − λi α̃j,t
)

Where the ? denotes one only includes observations and estimates of the state for periods
that the indicators are observed and adjusts the scalar T̂ accordingly.
Finally, for a1 the loglikelihood is maximized by updating this parameter to the smoothed
estimate α̃1 . For the update of P1 , we follow Shumway and Stoffer (1982) by initializing it
at some reasonable baseline level. Now, the EM algorithm for our dynamic factor model
is complete with the extensions of handling missing observations and temporal aggregation
issues in the underlying data.

24

25

Frequency
W
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Q
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W
M
W

Haver/Bloomberg*/Call Report^ Mnemonic
FAP1M-FCP1M
T111W2-R111G2
FLOD3-FTBS3
SPMLV1
SPMLSV3
FFP3M-FFP7D
FAB1M-FFP1M
FDB3-FTBS3
FYCEPA-FCM10
T111WA-R111GA
FFP3-FTBS3
FFED-RPGT01D*
T111W3M-R111G3M
FDDM/(FDDM+FDTM)
FLOD1Y-FLOD1
FDDG/(FDDG+FDTG)
FDDS/(FDDS+FDTS)
FFED-RPAG01D*
FDDC/(FDDC+FDTC)
FFED-RPMB01D*
FCM10
SPMD
RPGT03M*-RPGT01W*
FYCEP2-FTBS3
FCPT
FYCEPA-FYCEP2
COPED3P+COPTN2P+COPT10P+COPIRSP
FDFR+FDFV
SYCAAB-FCM5
CMBSAAA5*
FMLHY-FBAA
SPVIX
S009LIG
S009LHY
SYCF-SYCT
SYMT-FCM10
SBMAS-FCM20
FSLB-FCM20
FBAA-FCM10
ICMMA/ICIA
XL14TCRE5/GDP
(XL31CRE5+XL21CRE5)/GDP
N/A
(SPSP5CAP+SPNYCAPH+SPNACAP)/GDP
FNSIPS
SPWIE
USLPHPIS
FNSIS
MTBIP
N/A
COPSPMP+COPSP5P+COPNAMP+COPNASP
N/A
FNSIPB
D001TOTH
S5N40I/SPN5COM
FTCIS
FSCIS
FTCRE
FTCIL
FSCIL
ILMJNAVG*-ILM3NAVG*
S000CRI
NFIB20
FCM-FCM10

1: Money Markets
2: Debt/Equity Markets
3: Banking System

Table 2: Financial Indicators in the FCI

Transformation
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
DLNQ
LV
DLNQ
DLNQ
LV
DLNQ
LV
DLV
DLN
LV
LV
DLN
LV
DLNQ
DLNQ
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
LV
DLN
DLN
LVMA
DLN
LVMA
DLN
DLN
LV
DLN
LVMA
DLNQ
LVMA
LVMA
DLN
LVMA
LV
LV
LV
LV
LV
LV
LV
LV
LV

LV: Level
LVMA: Level relative to MA
DLV: First Difference
DLN: Log First Difference
DLNQ: 13-week Log Difference

Financial Indicator
1-month Nonfinancial CP A2P2/AA credit spread
2-year Swap/Treasury yield spread
3-month TED spread (LIBOR-Treasury)
1-month Merrill Lynch Options Volatility Expectations (MOVE)
3-month Merrill Lynch Swaption Volatiltiy Expectations (SMOVE)
3-month/1-week AA Financial CP spread
1-month Asset-backed/Financial CP credit spread
3-month Eurodollar spread (LIBID-Treasury)
On-the-run vs. Off-the-run 10-year Treasury liquidity premium
10-year Swap/Treasury yield spread
3-month Financial CP/Treasury bill spread
Fed Funds/Overnight Treasury Repo rate spread
3-month OIS/Treasury yield spread
Agency MBS Repo Delivery Failures Rate
1-year/1-month LIBOR spread
Treasury Repo Delivery Fails Rate
Agency Repo Delivery Failures Rate
Fed Funds/Overnight Agency Repo rate spread
Corporate Securities Repo Delivery Failures Rate
Fed Funds/Overnight MBS Repo rate spread
10-year Constant Maturity Treasury yield
Broker-dealer Debit Balances in Margin Accounts
3-month/1-week Treasury Repo spread
2-year/3-month Treasury yield spread
Commercial Paper Outstanding
10-year/2-year Treasury yield spread
3-month Eurodollar, 10-year/3-month swap, 2-year and 10-year Treasury Optio
Total Repo Market Volume (Repurchases+Reverse Repurchases)
Citigroup Global Markets ABS/5-year Treasury yield spread
Bloomberg 5-year AAA CMBS spread to Treasuries
Merrill Lynch High Yield/Moody's Baa corporate bond yield spread
CBOE S\&P 500 Volatility Index (VIX)
Credit Derivatives Research North America Investment Grade Index
Credit Derivatives Research North America High Yield Index
Citigroup Global Markets Financial/Corporate Credit bond spread
Citigroup Global Markets MBS/10-year Treasury yield spread
Bond Market Association Municipal Swap/20-year Treasury yield spread
20-year Treasury/State \& Local Government 20-year General Obligation Bond
Moody's Baa corporate bond/10-year Treasury yield spread
Total Money Market Mutual Fund Assets/Total Long-term Fund Assets
Nonfinancial business debt Outstanding/GDP
Federal, state, and local debt Outstanding/GDP
Total MBS Issuance (Relative to 12-month MA)
S\&P 500, NASDAQ, and NYSE Market Capitalization/GDP
New US Corporate Equity Issuance (Relative to 12-month MA)
Wilshire 5000 Stock Price Index
Loan Performance Home Price Index
New State \& Local Government Debt Issues (Relative to 12-month MA)
MIT Center for Real Estate Transactions-Based Commercial Property Price Ind
Nonmortgage ABS Issuance (Relative to 12-month MA)
S\&P 500, S\&P 500 mini, NASDAQ 100, NASDAQ mini Options and Futur
CMBS Issuance (Relative to 12-month MA)
New US Corporate Debt Issuance (Relative to 12-month MA)
Net Notional Value of Credit Derivatives
S\&P 500 Financials/S\&P 500 Price Index (Relative to 2-year MA)
Sr Loan Officer Opinion Survey: Tightening Standards on Small C\&I Loans
Sr Loan Officer Opinion Survey: Increasing spreads on Small C\&I Loans
Sr Loan Officer Opinion Survey: Tightening Standards on CRE Loans
Sr Loan Officer Opinion Survey: Tightening Standards on Large C\&I Loans
Sr Loan Officer Opinion Survey: Increasing spreads on Large C\&I Loans
30-year Jumbo/Conforming fixed rate mortgage spread
Credit Derivatives Research Counterparty Risk Index
National Federation of Independent Business Survey: Credit Harder to Get
30-year Conforming Mortgage/10-year Treasury yield spread

(all of the financial indicators are in basis points or percentages)

Start Category Full Sample Post-1984 39-variable Index
1997w2
1
2.255
2.213
1987w14
1
2.229
2.424
1980w23
1
1.825
3.066
1988w15
1
1.690
1.551
1996w49
1
1.678
1.008
1997w2
1
1.582
1.783
2001w1
1
1.581
1.996
1971w2
1
1.522
3.200
-1.851
1985w1
1
0.974
0.494
1987w14
1
0.845
0.947
1971w1
1
0.619
2.919
-0.430
1991w21
1
0.495
1.041
2003w38
1
0.452
1.148
1994w40
1
0.426
0.326
1986w2
1
0.368
0.004
1994w40
1
0.307
0.542
1994w40
1
0.168
0.289
1991w21
1
0.150
0.545
2001w40
1
0.103
0.122
1991w21
1
0.037
0.215
1971w2
1
-0.050
-0.126
-0.105
1971w5
1
-0.122
-0.267
0.061
1991w21
1
-0.141
0.141
1971w1
1
-0.237
0.242
1.264
1995w45
1
-0.482
-0.486
1971w34
1
-0.706
-0.375
3.704
2002w7
1
-1.024
-0.802
1994w40
1
-1.331
-1.078
1989w52
2
2.487
2.708
1996w27
2
2.234
1.574
1997w2
2
2.116
1.252
1990w1
2
2.074
1.811
2006w1
2
1.528
1.015
2006w1
2
1.516
0.972
1979w52
2
1.179
1.826
1979w52
2
0.848
1.706
1989w27
2
0.818
1.480
1971w1
2
0.502
-0.587
-2.143
1971w1
2
0.348
1.097
1.897
1974w52
2
0.231
0.217
-0.293
1971w13
2
0.025
0.105
-0.115
1971w13
2
0.024
0.098
0.140
2000w52
2
-0.022
-0.108
1971w13
2
-0.041
-0.090
0.022
1987w52
2
-0.047
0.005
-0.149
0.026
1971w5
2
-0.052
1976w9
2
-0.066
-0.349
-0.137
2004w9
2
-0.108
-0.133
1984w26
2
-0.111
-0.106
2000w52
2
-0.130
-0.127
2000w12
2
-0.134
0.047
1990w52
2
-0.157
-0.195
1987w52
2
-0.179
-0.269
2008w45
2
-0.256
-0.474
1989w37
2
-1.860
-2.040
1990w13
3
2.501
1.591
1990w13
3
2.467
1.471
1990w26
3
2.418
1.628
1990w13
3
2.416
1.513
1990w13
3
2.364
1.314
1998w23
3
2.220
1.776
2006w1
3
1.361
0.859
1973w44
3
1.228
0.779
0.368
1978w35
3
1.154
1.260
-0.574

26

Transformation
DLV
DLV
DLV
DLV
DLN
DLV
DLNQ
DLV
DLN
DLN
DLN
DLN
DLN
DLNQ
DLNQ
DLN
DLN
DLN
DLN
DLNQ
DLN
DLN
DLN
LV
DLN
DLNQ
LV
DLN
LV
DLN
LV
LV
LV
LV
LV
LV

LV: Level
LVMA: Level relative to MA
DLV: First Difference
DLN: Log First Difference
DLNQ: 13-week Log Difference

Financial Indicator
Ameican Bankers Association Value of Delinquent Home Equity Loans/Total L
Ameican Bankers Association Value of Delinquent Consumer Loans/Total Loa
Ameican Bankers Association Value of Delinquent Credit Card Loans/Total Lo
S\&P US Credit Card Quality Index 3-month Delinquency Rate
Noncurrent/Total Loans at Commercial Banks
Ameican Bankers Association Value of Delinquent Non-card Revolving Credit
C\&I Loans/Total Assets
Mortgage Bankers Association Serious Delinquencies
Total Assets of Funding Corporations/GDP
Mortgage Bankers Association Mortgage Applications Volume Market Index
Total Assets of Agency and GSE backed mortgage pools/GDP
Total Assets of ABS issuers/GDP
FDIC Volatile Bank Liabilities
Deposits/Total Assets
Fed funds and Reverse Repurchase Agreements w/ nonbanks and Interbank Lo
Total Assets of Finance Companies/GDP
Total Unused C\&I Loan Commitments/Total Assets
Total REIT Assets/GDP
Total Assets of Broker-dealers/GDP
Real Estate Loans/Total Assets
Total Assets of Pension Funds/GDP
MZM Money Supply
Total Assets of Insurance Companies/GDP
Commercial Bank 48-month New Car Loan/2-year Tteasury yield spread
Consumer Credit Outstanding
Securities in Bank Credit/Total Assets
Commercial Bank 24-month Personal Loan/2-year Treasury yield spread
S\&P US Credit Card Quality Index Receivables Outstanding
S\&P US Credit Card Quality Index Excess Rate Spread
Finance Company Receivables Outstanding
Finance Company New Car Loan interest rate/2-year Treasury yield spread
Sr Loan Officer Opinion Survey: Willingness to Lend to Consumers
UM Household Survey: Auto Credit Conditions Good/Bad spread
UM Household Survey: Mortgage Credit Conditions Good/Bad spread
UM Household Survey: Durable Goods Credit Conditions Good/Bad spread
National Association of Credit Managers Index

(all of the financial indicators are in basis points or percentages)

Frequency
M
M
M
M
Q
M
W
Q
Q
W
Q
Q
Q
W
W
Q
Q
Q
Q
W
Q
M
Q
Q
M
W
Q
M
M
M
M
Q
M
M
M
M

Haver/Bloomberg*/Call Report^ Mnemonic
USHWODA
USSUMDA
USBKCDA
CCQID3
(RCFD1407^+RCFD1403^)/RCFD2122^
USREVDA
FABWCA/FAA
USL14FA+USL149A
OA50TAO5/GDP
MBAM
OA41MOR5/GDP
OA67TAO5/GDP
RCON2604^+RCFN2200^+RCFD2800^+MAX(RCFD2890^,RCFD3190^)+RCFD3548^
FBDA/FAA
(FAIFFA+FABWORA)/FAA
OA61TAO5/GDP
RCON3423^/RCON2170^
OA64TAO5/GDP
OA66TAO5/GDP
FABWRA/FAA
OA57TAO5/GDP
FMZM
(OA51TAO5+OA54TAO5)/GDP
FK48NC-FCM2
FOT
FABYA/FAA
FK24P-FCM2
CCQIO
CCQIX
FROT
FFINC-FCM2
FWILL
N/A
N/A
N/A
CMI

1: Money Markets
2: Debt/Equity Markets
3: Banking System

Start Category Full Sample Post-1984 39-variable Index
1999w9
3
0.284
0.252
1999w9
3
0.264
0.235
1999w9
3
0.220
0.176
1992w9
3
0.157
0.153
1984w26
3
0.139
0.138
1999w9
3
0.139
0.140
1973w9
3
0.068
0.159
-0.415
1972w26
3
0.028
0.116
0.078
1971w13
3
0.022
0.076
-0.019
1990w2
3
0.020
-0.043
1971w13
3
0.011
0.029
-0.069
1983w39
3
0.005
-0.004
1978w26
3
0.000
-0.031
-0.052
1973w9
3
0.000
-0.091
0.083
1973w9
3
-0.005
-0.105
-0.108
1971w13
3
-0.009
0.009
-0.024
1984w26
3
-0.011
-0.019
1971w13
3
-0.012
-0.065
0.030
1971w13
3
-0.013
-0.075
-0.007
1973w9
3
-0.019
-0.100
-0.030
1971w13
3
-0.023
-0.072
0.006
1974w9
3
-0.028
0.060
0.102
1971w13
3
-0.029
-0.054
0.053
1976w26
3
-0.033
-0.010
0.209
1971w5
3
-0.039
-0.063
-0.138
1973w9
3
-0.052
-0.089
0.216
1976w26
3
-0.083
-0.073
0.218
1992w9
3
-0.095
-0.022
1992w5
3
-0.109
-0.193
1985w31
3
-0.149
-0.092
1976w26
3
-0.150
0.196
0.857
1971w13
3
-0.538
-1.085
0.926
1978w5
3
-1.354
-1.178
1.948
1978w5
3
-1.487
-1.619
2.033
1978w5
3
-1.543
-1.611
1.613
2002w9
3
-2.004
-1.374

27

Dow peaks above 1000 and then begins to decline ushering in 73‐74 "bear" market
Bildenberg meeting to discuss developing pressures in "petrodollar" market
United States National Bank of San Diego declared insolvent, first billion dollar bank failure
Arab Oil Embargo begins, intensifying petrodollar market problems
Oil embargo is lifted, but money center banks and REITS continue to experience problems
Regulatory agencies step in with financial assistance for Franklin National Bank
Saudia Arabia agrees to keep price of oil denominated in US $
Franklin National Bank collapses and is acquired by European American Bank
1973‐74 bear market ends with the Dow down 45%
Merger of Security National Bank of New York with Chemical National Bank to avoid failure
Regulatory agencies assist Bank of the Commonwealth to keep it afloat
Dollar begins steep decline against major foreign currencies
Carter announcement of a dollar defense program
OPEC decides to keep its US dollar reserves, but increase oil prices in 1979
Carter announcement of imposition of credit controls
Regulatory agencies step in with financial assistance for First Pennsylvania National Bank
Federal Reserve announces phase out of credit controls
FDIC assists merger of Greenwich Savings Bank, first in a series of mutual savings bank assisted mergers
Penn Square Bank fails
Mexico defaults on their debt, beginning of LDC crisis
Mexico and IMF reach accord on loan plan
Run on Continental Illinois begins, bank borrows $3.6 billion through discount window
Regulators develop plan to take over Contintental's bad loans
Resolution of Continental completed
Federal Savings and Loan Insurance Corporation becomes insolvent
"Black Monday": DJIA ‐22.6%, SP500 ‐20.4%
Financial Institutions Reform Recovery and Enforcement Act (FIRREA) signed into law
Stock market returns to pre‐crash levels
RTC Funding Act of 1991 signed into law
Thai government announces a "managed float" of the baht, which devalued by 15%
IMF approves a stand‐by credit for Thailand
Russia defaults on its debt
Collapse of LTCM (Federal Reserve steps in with support to financial markets)
IMF approves stand‐by credit for Russian Federation
Fed establishes Century Date Change Special Liquidity Facility
Y2K passes
NASDAQ peaks then loses 31% of its value within 4 weeks and 50% within 6 months
Terrorist attack on the World Trade Center
SEC enforcement action against Enron
Arthur Anderson indicted
Sarbanes‐Oxley Act passed
Bear Stearns liquidates two hedge funds investing in MBS
Bear Stearns sold to JPMorgan Chase with NY Fed support
Fannie Mae and Freddie Mac receive government assistance
FHFA places Fannie Mae and Freddie Mac into conservatorship
Lehman Brothers files for bankruptcy, AIG requires government assistance, Reserve Fund "breaks the buck"
Emergency Economic Stabilization Act passed (TARP)
Citigroup requires government assistance
Bank of America requires government assistance
Bank of America announces plans to repay TARP assistance
TARP extended to Oct. 3 2010
Citigroup reaches agreement to repay TARP assistance
EU, ECB, and IMF announce $1 trillion aid package after Greek debt crisis

Table 2: Recent Crises in U.S. Financial History

January 11, 1973
May 11‐13, 1973
October 18, 1973
October, 1973
March, 1974
May 10, 1974
June 8, 1974
October 8‐9, 1974
December 6, 1974
January 19, 1975
May 23, 1975
August 1, 1978
November 1, 1978
December 17, 1978
March 14, 1980
March 26, 1980
July 3, 1980
November 4, 1981
July 5, 1982
August 12, 1982
November 8, 1982
May 9‐11,1984
July 1, 1984
September 26, 1984
December 31, 1986
October 19,1987
August 9, 1989
September 1, 1989
March 23, 1991
July 2, 1997
August 11, 1997
August 17, 1998
September 23, 1998
July 27, 1999
October 1, 1999
January 1, 2000
March 10, 2000
September 11, 2001
December 13, 2001
July 15, 2002
July 30, 2002
July 31, 2007
March 14, 2008
July 13‐15,2008
September 7, 2008
September 14‐16, 2008
October 3, 2008
November 23, 2008
January 16, 2009
December 2, 2009
December 9, 2009
December 14, 2009
May 9‐10, 2010

Table 3: Recent Crises in U.S. Financial History

1973w2
1973w19
1973w42
1973w44
1973‐74 Bear Market, Petrodollar Recyling, and
1974w13
International Banking Crisis
1974w19
(1973‐1975)
1974w23
1974w41
1974w49
1975w3
1975w21
1978w31
1978w44
1978w51
1980w11
1980w13
Dollar Crisis, Mutual Savings Bank Crisis, Penn Square, LDC 1980w27
Crisis, and Continental Illinois
1981w44
(1978‐1984)
1982w27
1982w32
1982w45
1984w19
1984w27
1984w39
1986w52
1987w42
Savings & Loan Crisis and "Black Monday"
1989w32
(1986‐1991)
1989w35
1991w12
1997w27
1997w32
1998w33
1998w38
1999w30
Asian Financial Crisis, Russian Debt Default and LTCM, Y2K,
1999w40
Dot‐com Bubble, 9/11, and Accounting Scandals
2000w1
(1997‐2002)
2000w10
2001w37
2001w50
2002w28
2002w31
2007w31
2008w11
2008w28
2008w36
2008w37
2008w40
Subprime Mortgage Crisis and Subsequent Events
2008w47
(2007‐Current)
2009w3
2009w48
2009w49
2009w50
2010w19

# of Data Series
10 20 30 40 50 60 70 80 90 100
0

28
1970

1990
Year

2000

Figure 1: Pattern of Data Availability

1980

Number of Data Series in the FCI

2010

29

std. dev. units
−4

−2

0

2

4

6

8

10

12

14

1980w1

1990w1

2000w1

Figure 2: Financial Conditions Index vs. USMPFFCI and KCFSI

1975w1

FCI
KCFSI
USMPFFCI

Financial Conditions Indices

2010w1

30
−3

−2

−1

0

1

2

3

4

5

6

−4
1990w1

std. dev. units

Initial
KCFSI

Figure 3: PCA-estimated Indexes

2000w1

Short Sample Financial Conditions Indices

2010w1

31

std. dev. units
−4

−2

0

2

4

6

8

10

12

14

1975w1

1980w1

Full Sample
Post−1984 Sample

2000w1

Figure 4: Full Sample vs. Post-1984

1990w1

Unadjusted FCI

2010w1

32
29%

Debt/Equity Markets

30%

Post-1984

Banking System

Money Markets

27%

24%

Variance Explained

Debt/Equity Markets

49%

Figure 5: Decomposition of Variance Explained by the FCI

Banking System

Money Markets

41%

Full Sample

33

std. dev. units
−4

−2

0

2

4

6

1975w1

39 vars
YDUV

1980w1

2000w1

Figure 6: 100 vs. 39-variable Indexes

1990w1

6WDELOLW\RIWKHFCI2YHU,QGLFDWRUV

2010w1

34
0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

−2

0

2

4

6

1975w1

1975w1

1975w1

1990w1

1995w1

1990w1

1995w1

2000w1

2000w1

1985w1

1990w1

1995w1

2000w1

C. 1 − Non−crisis State Probability

1985w1

B. Crisis State Probability

1985w1

Figure 7: Markov-Switching Model for the FCI

1980w1

1980w1

1980w1

FCI
Crisis Episodes

A. Financial Conditions Index

2005w1

2005w1

2005w1

2010w1

2010w1

2010w1

35

True Positive
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1

0.3

0.4

0.5
False Positive

0.6

0.7

0.8

AUROC = 0.934

Figure 8: Receiver Operator Characteristics curve for the FCI

0.2

ROC curve: FCI

0.9

1

36
−4

−2

0

2

4

6

1975w1

1990w1

2000w1

Unbiased
Pre−emptive

Reactionary

2010w1

Crisis Episodes
FCI
Emphasis on Crisis Episodes
Equal Weight
Emphasis on Non−crisis Episodes

Figure 9: Optimal ROC thresholds for the FCI

1980w1

FCI and Threshold Rules

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.
Risk Taking and the Quality of Informal Insurance: Gambling and Remittances in Thailand
Douglas L. Miller and Anna L. Paulson

WP-07-01

Fast Micro and Slow Macro: Can Aggregation Explain the Persistence of Inflation?
Filippo Altissimo, Benoît Mojon, and Paolo Zaffaroni

WP-07-02

Assessing a Decade of Interstate Bank Branching
Christian Johnson and Tara Rice

WP-07-03

Debit Card and Cash Usage: A Cross-Country Analysis
Gene Amromin and Sujit Chakravorti

WP-07-04

The Age of Reason: Financial Decisions Over the Lifecycle
Sumit Agarwal, John C. Driscoll, Xavier Gabaix, and David Laibson

WP-07-05

Information Acquisition in Financial Markets: a Correction
Gadi Barlevy and Pietro Veronesi

WP-07-06

Monetary Policy, Output Composition and the Great Moderation
Benoît Mojon

WP-07-07

Estate Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi

WP-07-08

Conflict of Interest and Certification in the U.S. IPO Market
Luca Benzoni and Carola Schenone

WP-07-09

The Reaction of Consumer Spending and Debt to Tax Rebates –
Evidence from Consumer Credit Data
Sumit Agarwal, Chunlin Liu, and Nicholas S. Souleles

WP-07-10

Portfolio Choice over the Life-Cycle when the Stock and Labor Markets are Cointegrated
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-07-11

Nonparametric Analysis of Intergenerational Income Mobility
with Application to the United States
Debopam Bhattacharya and Bhashkar Mazumder

WP-07-12

How the Credit Channel Works: Differentiating the Bank Lending Channel
and the Balance Sheet Channel
Lamont K. Black and Richard J. Rosen

WP-07-13

Labor Market Transitions and Self-Employment
Ellen R. Rissman

WP-07-14

First-Time Home Buyers and Residential Investment Volatility
Jonas D.M. Fisher and Martin Gervais

WP-07-15

1

Working Paper Series (continued)
Establishments Dynamics and Matching Frictions in Classical Competitive Equilibrium
Marcelo Veracierto

WP-07-16

Technology’s Edge: The Educational Benefits of Computer-Aided Instruction
Lisa Barrow, Lisa Markman, and Cecilia Elena Rouse

WP-07-17

The Widow’s Offering: Inheritance, Family Structure, and the Charitable Gifts of Women
Leslie McGranahan

WP-07-18

Incomplete Information and the Timing to Adjust Labor: Evidence from the
Lead-Lag Relationship between Temporary Help Employment and Permanent Employment
Sainan Jin, Yukako Ono, and Qinghua Zhang

WP-07-19

A Conversation with 590 Nascent Entrepreneurs
Jeffrey R. Campbell and Mariacristina De Nardi

WP-07-20

Cyclical Dumping and US Antidumping Protection: 1980-2001
Meredith A. Crowley

WP-07-21

Health Capital and the Prenatal Environment:
The Effect of Maternal Fasting During Pregnancy
Douglas Almond and Bhashkar Mazumder

WP-07-22

The Spending and Debt Response to Minimum Wage Hikes
Daniel Aaronson, Sumit Agarwal, and Eric French

WP-07-23

The Impact of Mexican Immigrants on U.S. Wage Structure
Maude Toussaint-Comeau

WP-07-24

A Leverage-based Model of Speculative Bubbles
Gadi Barlevy

WP-08-01

Displacement, Asymmetric Information and Heterogeneous Human Capital
Luojia Hu and Christopher Taber

WP-08-02

BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs
Jon Frye and Eduard Pelz

WP-08-03

Bank Lending, Financing Constraints and SME Investment
Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell

WP-08-04

Global Inflation
Matteo Ciccarelli and Benoît Mojon

WP-08-05

Scale and the Origins of Structural Change
Francisco J. Buera and Joseph P. Kaboski

WP-08-06

Inventories, Lumpy Trade, and Large Devaluations
George Alessandria, Joseph P. Kaboski, and Virgiliu Midrigan

WP-08-07

2

Working Paper Series (continued)
School Vouchers and Student Achievement: Recent Evidence, Remaining Questions
Cecilia Elena Rouse and Lisa Barrow
Does It Pay to Read Your Junk Mail? Evidence of the Effect of Advertising on
Home Equity Credit Choices
Sumit Agarwal and Brent W. Ambrose

WP-08-08

WP-08-09

The Choice between Arm’s-Length and Relationship Debt: Evidence from eLoans
Sumit Agarwal and Robert Hauswald

WP-08-10

Consumer Choice and Merchant Acceptance of Payment Media
Wilko Bolt and Sujit Chakravorti

WP-08-11

Investment Shocks and Business Cycles
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-08-12

New Vehicle Characteristics and the Cost of the
Corporate Average Fuel Economy Standard
Thomas Klier and Joshua Linn

WP-08-13

Realized Volatility
Torben G. Andersen and Luca Benzoni

WP-08-14

Revenue Bubbles and Structural Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

WP-08-15

The role of lenders in the home price boom
Richard J. Rosen

WP-08-16

Bank Crises and Investor Confidence
Una Okonkwo Osili and Anna Paulson

WP-08-17

Life Expectancy and Old Age Savings
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-08-18

Remittance Behavior among New U.S. Immigrants
Katherine Meckel

WP-08-19

Birth Cohort and the Black-White Achievement Gap:
The Roles of Access and Health Soon After Birth
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-08-20

Public Investment and Budget Rules for State vs. Local Governments
Marco Bassetto

WP-08-21

Why Has Home Ownership Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais

WP-09-01

Why do the Elderly Save? The Role of Medical Expenses
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-09-02

3

Working Paper Series (continued)
Using Stock Returns to Identify Government Spending Shocks
Jonas D.M. Fisher and Ryan Peters

WP-09-03

Stochastic Volatility
Torben G. Andersen and Luca Benzoni

WP-09-04

The Effect of Disability Insurance Receipt on Labor Supply
Eric French and Jae Song

WP-09-05

CEO Overconfidence and Dividend Policy
Sanjay Deshmukh, Anand M. Goel, and Keith M. Howe

WP-09-06

Do Financial Counseling Mandates Improve Mortgage Choice and Performance?
Evidence from a Legislative Experiment
Sumit Agarwal,Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-09-07

Perverse Incentives at the Banks? Evidence from a Natural Experiment
Sumit Agarwal and Faye H. Wang

WP-09-08

Pay for Percentile
Gadi Barlevy and Derek Neal

WP-09-09

The Life and Times of Nicolas Dutot
François R. Velde

WP-09-10

Regulating Two-Sided Markets: An Empirical Investigation
Santiago Carbó Valverde, Sujit Chakravorti, and Francisco Rodriguez Fernandez

WP-09-11

The Case of the Undying Debt
François R. Velde

WP-09-12

Paying for Performance: The Education Impacts of a Community College Scholarship
Program for Low-income Adults
Lisa Barrow, Lashawn Richburg-Hayes, Cecilia Elena Rouse, and Thomas Brock
Establishments Dynamics, Vacancies and Unemployment: A Neoclassical Synthesis
Marcelo Veracierto

WP-09-13

WP-09-14

The Price of Gasoline and the Demand for Fuel Economy:
Evidence from Monthly New Vehicles Sales Data
Thomas Klier and Joshua Linn

WP-09-15

Estimation of a Transformation Model with Truncation,
Interval Observation and Time-Varying Covariates
Bo E. Honoré and Luojia Hu

WP-09-16

Self-Enforcing Trade Agreements: Evidence from Antidumping Policy
Chad P. Bown and Meredith A. Crowley

WP-09-17

Too much right can make a wrong: Setting the stage for the financial crisis
Richard J. Rosen

WP-09-18

4

Working Paper Series (continued)
Can Structural Small Open Economy Models Account
for the Influence of Foreign Disturbances?
Alejandro Justiniano and Bruce Preston

WP-09-19

Liquidity Constraints of the Middle Class
Jeffrey R. Campbell and Zvi Hercowitz

WP-09-20

Monetary Policy and Uncertainty in an Empirical Small Open Economy Model
Alejandro Justiniano and Bruce Preston

WP-09-21

Firm boundaries and buyer-supplier match in market transaction:
IT system procurement of U.S. credit unions
Yukako Ono and Junichi Suzuki
Health and the Savings of Insured Versus Uninsured, Working-Age Households in the U.S.
Maude Toussaint-Comeau and Jonathan Hartley

WP-09-22

WP-09-23

The Economics of “Radiator Springs:” Industry Dynamics, Sunk Costs, and
Spatial Demand Shifts
Jeffrey R. Campbell and Thomas N. Hubbard

WP-09-24

On the Relationship between Mobility, Population Growth, and
Capital Spending in the United States
Marco Bassetto and Leslie McGranahan

WP-09-25

The Impact of Rosenwald Schools on Black Achievement
Daniel Aaronson and Bhashkar Mazumder

WP-09-26

Comment on “Letting Different Views about Business Cycles Compete”
Jonas D.M. Fisher

WP-10-01

Macroeconomic Implications of Agglomeration
Morris A. Davis, Jonas D.M. Fisher and Toni M. Whited

WP-10-02

Accounting for non-annuitization
Svetlana Pashchenko

WP-10-03

Robustness and Macroeconomic Policy
Gadi Barlevy

WP-10-04

Benefits of Relationship Banking: Evidence from Consumer Credit Markets
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-10-05

The Effect of Sales Tax Holidays on Household Consumption Patterns
Nathan Marwell and Leslie McGranahan

WP-10-06

Gathering Insights on the Forest from the Trees: A New Metric for Financial Conditions
Scott Brave and R. Andrew Butters

WP-10-07

5