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Eco
In This Issue:
Inflation and Prices
Monetary Policy
• Difficulties Forecasting Wage Growth
• Cleveland Fed Estimates of Inflation
Expectations, May 2015
■ The Gap between Services Inflation and Goods
Inflation
■ The Yield Curve and Predicted GDP Growth,
May 2015
■ Mutable Economic Laws and Calculating Unemployment and Output Gaps-An Application to
Taylor Rules
FEDERAL RESERVE BANK
CLEVELAND
of
Inflation and Prices
Difficulties Forecasting Wage Growth
05.08.15
by Edward S. Knotek II
Wages have generated considerable discussion since
the end of the recession. The income that households earn from working is an important support
for consumer spending, which drives the bulk of
activity in the U.S. economy. By this logic, strong
labor income gains should boost consumer spending, thereby contributing to a strong economy,
which begets strong hiring and wage gains, in a
virtuous circle. Some previous research has found
support for a wage Phillips curve: historically,
as economic conditions have improved and the
amount of slack in the labor market has decreased,
wage growth has tended to pick up.
Unemployment and Wage Growth
Percent, level or year-over-year growth rate
12
10
8
6
4
2
0
2010
2011
2012
2013
2014
2015
Source: Bureau of Labor Statistics
Note: ECI is compensation for private industry workers; average hourly earnings is
for all employees on private nonfarm payrolls.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
This business-cycle expansion has been notable
because it has been characterized by a generally subdued rate of wage growth. Even though the unemployment rate fell from 9.8 percent to 5.5 percent
between January 2010 and March 2015, growth in
average hourly earnings for all employees on private
nonfarm payrolls has been remarkably steady near a
2 percent annual rate. An alternative measure from
the Bureau of Labor Statistics called the Employment Cost Index (ECI) captures broader compensation costs based on wages and salaries along with
benefits. Growth in the ECI for compensation for
private industry workers has been relatively similar.
However, the far right side of the chart shows some
positive signs. First, an unemployment rate of 5.5
percent is closing in on levels that many economists and policymakers think are consistent with
relatively normal conditions. For example, in the
Summary of Economic Projections following the
March 2015 meeting of the Federal Open Market
Committee (FOMC), the central tendency for the
unemployment rate in the long run was 5.0 percent
to 5.2 percent. Second, there are signs that compensation as measured by the ECI is accelerating.
On a year-over-year basis, private industry compensation increased 2.8 percent through March 2015,
2
its highest reading since September 2008. After a
long stretch, the wage Phillips curve may finally be
coming back to life.
NFIB Compensation Measures
Net percent (seasonally adjusted)
35
Increased worker compensation,
last three months
30
25
20
15
10
5
Planning to increase worker compensation,
next three months
0
-5
1990
1994
1998
2002
2006
2010
2014
Note: Quarterly averages.
Sources: National Federation of Independent Business, Haver Analytics.
A pickup in compensation growth is consistent
with some reports coming from businesses. Several prominent national retail chains have recently
announced plans to increase wages. The National
Federation of Independent Business (NFIB) provides monthly survey evidence from small businesses showing the net percentage of respondents
reporting plans to increase worker compensation
in the next three months and the net percentage
of respondents who increased worker compensation over the past three months. After falling off
to extremely low levels during the recession, these
readings have gradually recovered and are back
within the range of readings from the previous two
business cycles.
What can shrinking slack in the labor market or
reports from businesses on their wage plans tell us
about the trend for wages going forward? To address this question, I consider three models for forecasting wage growth. I take a broad view of "wages"
by looking at employee compensation for private
industry workers as measured by the ECI.
The first model is a medium-scale statistical model
used in previous work, called a Bayesian vector autoregression (BVAR), which includes ECI growth,
the unemployment rate, productivity, inflation, and
several other typical macroeconomic data series.
This model allows for the possibility that there is a
wage Phillips curve in which a falling unemployment rate puts upward pressure on wage growth,
but it also includes a variety of other factors that
may affect wage growth, such as productivity and
inflation. The second model uses information
from businesses to predict future ECI growth. In
particular, I map the NFIB survey responses on
plans to raise worker compensation to ECI growth
via a simple forecasting model. The third model is
not much of a model at all: it simply assumes that
future year-over-year ECI growth will be equal to
its most recently observed value. This is a random
walk model.
Using data available through the fourth quarter of
2014, I generate forecasts from these three models .
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
3
ECI Forecasts
Percent, year-over-year growth rate
3.5
---~
BVAR model
3.0
2.5
RW model
2.0
1.5
1.0
0.5
0.0
2015:Q1
2015:Q3
2016:Q1
2016:Q3
2017:Q1
2017:Q3
Source: author's calculations.
ECI Forecast Errors, 1994-2014
Root mean squared forecast errors, percentage points
1.4
BVAR model
1.2
1.0
0.8
0.6
0.4
0.2
0.0 ..____.__ _.___ _.__ _..___ _.____..___,__ __.__.....,___ _..____,
1
2
3
4
5
6
7
Horizon
8
9
10
11
Source: author's calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
12
After a decline in the middle of this year, the BVAR
model puts ECI growth on an upward trajectory
over the next several years, consistent with further
improvements in labor markets, which the model
predicts as well. By the end of 2017, ECI growth is
a little above 3 percent in this forecast. The simple
NFIB model almost perfectly predicted the ECI
reading in the first quarter of 2015 of 2.8 percent.
But this model would actually forecast that ECI
growth should taper off somewhat, gradually falling to about 2½ percent by the end of the forecast
period. By construction, the random walk forecast
calls for ECI growth to be steady at a little under
2½ percent for the next three years. Of course, if
I were to redo the forecasts using the most recent
ECI reading of 2.8 percent, the random walk
model would now call for that rate of ECI growth
to persist going forward.
Given that the NFIB model made an excellent forecast for the first quarter of 2015, should we place
the most weight on that model? Looking at the
historical forecast accuracy of these three models
is revealing. For each quarter starting in the first
quarter of 1994 and ending in the fourth quarter
of 2014, I generate the ECI growth forecast coming from each model for the next 12 quarters and
then see how accurate those forecasts turned out to
be. I assume the forecasts would have been made
approximately in the middle of the middle month
of the quarter, and wherever possible I use the data
that would have been available to a forecaster in
"real time" at that point. The BVAR model has
historically generated reasonably accurate forecasts
at short horizons and much less accurate forecasts
at longer horizons, based on the typical forecast
misses-technically, the root mean squared forecast
errors-from this model. Relative to the BVAR
model, typical forecast misses have been somewhat
larger at short horizons for the simple NFIB model
but smaller at longer horizons. But at each horizon,
the random walk model has been the most accurate
of the three models. This result suggests that movements in compensation growth-which depend on
a complex combination of labor market slack, bargaining power, worker productivity, inflation, and
myriad other factors-have been essentially unpredictable since the mid-1990s. These difficulties
4
in forecasting labor compensation provide at least
some evidence for why wages often appear to have
little predictive power when forecasting inflation
(see, for example, Stock and Watson 2008). In discussing the outlook for wages in her press conference following the March FOMC meeting, Federal
Reserve Board Chair Yellen raised the possibility
that wage growth may not pick up, a forecast in
line with the predictions of a random walk model.
ECI Forecast Errors, 1994-2009
Root mean squared forecast errors, percentage points
1.2
Green book
1.0
0.8
0.6
0.4
0.2
0.0 .____,._____.__ _ _ . _ _ ~ - ~ - ~ -~ ~ -~ 1
2
3
4
6
7
5
8
9 10
~~
11
12
Horizon
Source: author's calculations.
Of course, one distinct possibility is that these
models for forecasting wage growth are inferior to
other models. In this case, looking at the ECI forecast accuracy of other forecasters could be instructive. For the period 1994-2009, it is possible to see
the publicly available forecasts for ECI growth that
were made by one well-known forecasting body:
the Federal Reserve Board of Governors staff, in the
Greenbook. Greenbook forecasts are made immediately prior to each FOMC meeting. There are two
regularly scheduled meetings of the FOMC in each
quarter, and thus two Greenbooks; I use the second
forecast from each quarter, potentially giving the
Greenbook an information advantage over my previous forecasts, which were made using information
available only through the first half of each quarter.
For the sake of comparability, I shorten the sample
and look at the forecasting performance of the other models over the period 1994-2009 as well. Over
short horizons-one to two quarters-the Greenbook's forecasts for ECI growth were slightly more
accurate than those from the other models. But as
the forecast horizon lengthens, the typical forecast
misses from the random walk model were again
smaller than those coming from the Greenbook.
In other words, extrapolating the recent past into
the future was also a more accurate forecast for ECI
growth on average than the Greenbook forecasts.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
5
Inflation and Prices
Cleveland Fed Estimates of Inflation Expectations, May 2015
News Release: May 22, 2015
The latest estimate of 10-year expected inflation
is 1.79 percent, according to the Federal Reserve
Bank of Cleveland. In other words, the public currently expects the inflation rate to be less than 2
percent on average over the next decade.
Ten-Year Expected Inflation and
Real and Nominal Risk Premia
Percent
7
6
5
~
4
3
2
\,,/"'"-'r".~.,.,r,-'-'---.,.,"'-___,,__
~-"'-":"'_.,,_-_.,.,~-..--~----_:-_,.,,_:--_-._-_::-_-,_~_-r~-_:-_~_v~-.._"'-_-_:-~--__:-_-_,,,,.~--~
0
1982
1986
1990
1994
1998
2002
2006
2010
Expected inflation
Real risk premium
Inflation risk premium
2014
Source: Haubrich, Pennacchi, Ritchken (2012).
The Cleveland Fed's estimate of inflation expectations is based on a model that combines information from a number of sources to address the
shortcomings of other, commonly used measures,
such as the "break-even'' rate derived from Treasury
inflation protected securities (TIPS) or surveybased estimates. The Cleveland Fed model can
produce estimates for many time horizons, and it
isolates not only inflation expectations, but several
other interesting variables, such as the real interest
rate and the inflation risk premium.
Ten-Year TIPS Yields versus Real Yields
Expected Inflation Yield Curve
Percent
5
2.5
Percent
4
2.0
3
2
1.5
't, Ten-year model yield
O 1----
- --
-
-
- --
--~..,JI.II---!:' Ten-year TIPS yield
1.0
-1
-2 ~ -- - - -~ - ~ - ~- ~ -~ --'1999
2001
2003
2005
2007
2009
2011
2013
0.5
2015
Source: Haubrich, Pennacchi, Ritchken (2012).
1 2 3 4 5 6 7 8 910 12
15
20
25
30
Horizon (years)
Source: Haubrich, Pennacchi, Ritchken (2012).
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
6
Inflation and Prices
The Gap between Services Inflation and Goods Inflation
06.02.2015
by Saeed Zaman
Core PCE Inflation and Its Components
12-month percent change
6
5
4
3
Core PCE
2
services
Core PCE
0 f - - - - -~ - ----,-r-------flr.iri:n--m-+.c+-....:.;--
Core PCE
-1
goods
-2
-3 ~ - -- - ' - - - -~ - - ~ -- --'-- - ~
1990
1995
2000
2005
2010
2015
Notes: Last observation was April 2015. Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis.
Inflation as measured by the price index for personal consumption expenditures (PCE) has been running below the Federal Reserve's longer-run objective of 2 percent for the last three years. Similarly,
the PCE price index excluding food and energy,
also known as core PCE inflation, has been below
2 percent over the same period. Core PCE inflation as of April 2015 was 1.24 percent on a yearover-year basis. This reading is little changed from
where it was in early 2014 in spite of improvements
seen in the labor market over the last year, as passthrough from sharply lower oil prices and a sharply
stronger dollar have weighed on inflation readings.
Digging a little deeper into the behavior of the two
components of core PCE inflation, core services
and core goods, may provide some additional
insights into why core inflation has been coming
in persistently low and whether there is a cause for
concern that it could remain low going forward.
Doing so reveals that subdued core services inflation continues to be the primary factor keeping
core inflation low.
Since the early 1990s, inflation rates for both core
services and core goods inflation have declined
sharply, with core goods inflation falling more than
core services inflation. While core services inflation
never fell below 2 percent, core goods inflation continued to decline and eventually became negative
by the mid-l 990s. Since then, it has been consistently and significantly negative. Core services
inflation has gradually trended up from its recession
lows and stabilized around 2 percent over the last
three years. Currently it remains near that level, a
full percentage point lower than its average in the
five years prior to the Great Recession.
One can glean additional insight into the deflationary behavior of core goods inflation by looking at
the behavior of its two subcomponents: durable
goods and nondurable goods (both of which exclude energy and food). It is durable goods which
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
7
have had the greatest effect on total core goods
inflation. Since 1995, durable goods inflation has
been persistently and significantly negative. It measured -2.2 percent in April 2015 on a year-overyear basis, whereas nondurable goods inflation was
1.2 percent.
Core PCE Goods Breakdown
12-month percent change
6
4
2
0
Nondurable
goods
1----- -m--,- -- -<.UJ·- + - --
...__---,---
-4
-6 '--- ---'-- -- - ' - -1990
1995
2000
--'--2005
----'' -- ---'2010
2015
Notes: Last observation was April 2015. Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis.
Core Services Less Core Goods Inflation
(Gap)
Over the last two years, the inflation rates for nondurable goods and durable goods have been on a
divergent path, with durable goods inflation trending lower and nondurable goods inflation trending
higher. The combined effect has kept overall core
goods inflation relatively stable (but negative).
Past studies (see Peach, Rich, and Antoniadas 2004,
and Peach, Rich, and Linder 2013) have stressed
the importance of examining the underlying behavior of these two major components of aggregate inflation along with the measured gap between them,
because such information may provide deeper
insights into the observed behavior of aggregate
inflation and help to inform the near-term outlook
for aggregate inflation
Percent
6
5
4
3
2
0
-1
1990
The large spikes observed in total core goods inflation around the Great Recession and one year later
were primarily driven by spikes in nondurable
goods inflation. A little digging reveals that those
spikes were indeed driven by temporary factors.
The spike in 2009 was due to an increase in tobacco taxes introduced that year, which at the time was
dubbed one of the largest federal tax increases in
US history. Another spike in nondurables occurred
around 2011-2012 and was partly due to a sharp
rebound in clothing prices, which had been falling
for more than a decade.
1995
2000
2005
2010
2015
Note: Last observation was April 2015.
Sources: Bureau of Economic Analysis.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
It has been well documented that core goods inflation is usually lower than core services inflation (see
Clark 2004). As a result, the gap between them has
historically been positive. The Great Recession saw
a reversal of this relationship, when the slowdown
in core services inflation and the surge in core
goods inflation caused the gap to turn negative in
May 2009 for the first time since 1990. The gap
once again turned positive in February 2010 and
has been widening since. The improving economy
helped support services inflation, and from 2011
onwards the rising value of the dollar has weighed
on core goods inflation. Recently, with core goods
8
Services and Goods Differential
versus Exchange Rate
Percent
Index, March 1973=100
6
5
Correlation : 0.55
Core services
140
130
4
120
3
110
2
100
0
-1
exchange
rate
inflation remaining relatively stable, and core
services inflation edging slightly lower, the gap
between the two has narrowed slightly. Currently
it is 2.3 percentage points, which is near its historical average over the past 25 years of 2.8 percentage
points. This gap is significantly lower than the peak
value of 5.5 percentage points attained in May
2003, which at the time contributed to concerns
about the potential for deflation.
90
80
~ - - - ~ - - - - - -- ~ -- - ~ - - -~ 70
1990
1995
2000
2005
2015
2010
Notes: Last observation was April 2015. Exchange rate is the broader trade
weighted index.
Sources: Bureau of Economic Analysis, Board of Governors of the Federal Reserve
System, and author's calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
9
Monetary Policy
Yield Curve and Predicted GDP Growth, May 2015
Covering April 24-May 22, 2015
by Joseph G. Haubrich and Sara Millington
Overview of the Latest Yield Curve Figures
Highlights
May
April
March
Three-month Treasury bill rate (percent)
0.02
0.03
0.03
Ten-year Treasury bond rate (percent)
2.23
1.94
2.00
Yield curve slope (basis points)
221
191
197
Prediction for GDP growth (percent)
2.2
2.2
2.1
Probability of recession in one year (percent)
3.42
5.25
4.85
Sources: Board of Governors of the Federal Reserve System; authors' calculations.
Yield Curve-Predicted GDP Growth
Percent
4
2
0
-2
Ten-year minus
three-month yield spread
GDP growth
(year-over-year change)
-4
-6
2002
2004
2006
2008
2010
2012
2014
2016
Sources: Bureau of Economic Analysis; Board of Governors of the Federal Reserve
System; authors' calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
The yield curve got steeper in May. As has been
typical lately, most of the action was mainly at the
long end while the short end dropped slightly, with
the three-month (constant maturity) Treasury bill
rate falling to 0.02 percent (for the week ending
May 22), down a hair from April's 0.03 percent,
which was even with March's rate. The ten-year
rate (also constant maturity) rose a full 29 basis
points-almost a third of a percent-to 2.23 percent from April's 1.94 percent and was even nearly
a quarter point above the March number of 2.00
percent. These changes increased the slope to 221
basis points, up from the 191 basis points seen in
April and the 197 basis points in March.
The steeper slope did not have a large impact on
predicted real GDP growth and expected growth
stayed constant. Using past values of the spread
and GDP growth suggests that real GDP will grow
at about a 2.2 percent rate over the next year, even
with last month's reading, which was barely up
from March. The influence of the past recession
continues to push towards relatively low growth
rates, but recent year-over-year growth has been
stronger (despite the recent negative number for
the first quarter of 2015) and is counteracting that
push. Although the time horizons do not match
exactly, the forecast is slightly more pessimistic than
some other predictions, but like them, it does show
moderate growth for the year.
The increased slope, however, did have the usual
effect on the probability of a recession, which
dropped. Using the yield curve to predict whether
or not the economy will be in recession in the
future, we estimate that the expected chance of the
economy being in a recession next May at 3.42 percent, down from April's at 5.25 percent and even
below the March number of 4.85 percent. So even
though the most recent real GDP estimate saw
the economy contract in the first quarter of 2015,
10
the yield curve is optimistic about the recovery
continuing, even if it is somewhat pessimistic with
regard to the pace of growth over the next year.
The Yield Curve as a Predictor of Economic
Growth
Recession Probability from Yield Curve
Percent probability, as predicted by a probit model
100
90
Probability of recession
80
70
60
Forecast
50
40
30
20
10
0
1960 1966 1972 1978 1984 1990 1996 2002 2008 2014
Source: Board of Governors of the Federal Reserve System; NBER; authors'
calculations.
Yield Curve Spread and Real GDP Growth
Percent
10
We use past values of the yield spread and GDP
growth to project what real GDP will be in the future. We typically calculate and post the prediction
for real GDP growth one year forward.
6
4
2
0
Predicting the Probability of Recession
Ten-year minus
three-month yield spread
-2
--4
-6
1953
More generally, a Bat curve indicates weak growth,
and conversely, a steep curve indicates strong
growth. One measure of slope, the spread between
ten-year Treasury bonds and three-month Treasury
bills, bears out this relation, particularly when real
GDP growth is lagged a year to line up growth
with the spread that predicts it.
Predicting GDP Growth
GDP. growth
(year-over-year change)
8
The slope of the yield curve-the difference between the yields on short- and long-term maturity
bonds-has achieved some notoriety as a simple
forecaster of economic growth. The rule of thumb
is that an inverted yield curve (short rates above
long rates) indicates a recession in about a year,
and yield curve inversions have preceded each of
the last seven recessions (as defined by the NBER).
One of the recessions predicted by the yield curve
was the most recent one. The yield curve inverted
in August 2006, a bit more than a year before the
current recession started in December 2007. There
have been two notable false positives: an inversion
in late 1966 and a very Bat curve in late 1998.
1965
1977
1989
2001
2013
Note: Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis, Board of Governors of the Federal Reserve
System.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
While we can use the yield curve to predict whether future GDP growth will be above or below average, it does not do so well in predicting an actual
number, especially in the case of recessions. Alternatively, we can employ features of the yield curve
to predict whether or not the economy will be in a
recession at a given point in the future. Typically,
we calculate and post the probability of recession
one year forward.
11
Yield Spread and Lagged Real GDP
Growth
Percent
10
GDP growth
(year-over-year change)
8
6
4
2
0
-2
-4
-6
1953
1965
1977
1989
2001
Note: Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis, Board of Governors of the Federal
Reserve System.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
2013
Of course, it might not be advisable to take these
numbers quite so literally, for two reasons. First,
this probability is itself subject to error, as is the
case with all statistical estimates. Second, other
researchers have postulated that the underlying
determinants of the yield spread today are materially different from the determinants that generated
yield spreads during prior decades. Differences
could arise from changes in international capital
flows and inflation expectations, for example. The
bottom line is that yield curves contain important
information for business cycle analysis, but, like
other indicators, should be interpreted with caution. For more detail on these and other issues related to using the yield curve to predict recessions,
see the Commentary "Does the Yield Curve Signal
Recession?" Our friends at the Federal Reserve
Bank of New York also maintain a website with
much useful information on the topic, including
their own estimate of recession probabilities.
12
Monetary Policy
Mutable Economic Laws and Calculating Unemployment and Output
Gaps-An Application to Taylor Rules
06.04.2015
by Charles T. Carlstrom and Timothy Stehulak
Where the Fed Funds Rate Would Be Using
a Taylor Rule
Percent
6
5 ......---~~
4
3
2
Values at 2015:01 TR with
unemployment
1 _85
0.64
gap
TR with
output gap
0 - -- - -- - - - -------,,..'--7'~t--- -
-1
-2
-3
-4
-5 - -- ~ -- ~ - - - -- -~ - - ~ 2012
2014
2016
2006
2008
2010
Sources: Bureau of Economic Analysis, Bureau of Labor Statistics, Congressional
Budget Office, and authors' calculations.
Estimates of Potential GDP
Trillions (chained 2009 dollars)
19
2007 CBO
potential GDP
18
17
.,, .,,
2015 CBO
potential GDP
.,, .,,- Real GDP
16
15
14 ~ - - ~- - ~ - -- ~- - ~ - - ~2014
2006
2008
2010
2012
2016
Sources: Congressional Budget Office, Bureau of Economics Analysis, and authors'
calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
The Taylor rule, which expresses the federal funds
rate as a function of how far inflation is from its
long-run target and how far output is from its
potential, is often thought to be a useful guide to
monetary policy. Economist John Taylor proposed
that the weight on the inflation gap be 1.5 and the
weight on the output gap be 1. Given that the dual
mandate of the Federal Reserve includes inflation
and employment, many people write the Taylor
rule in terms of an employment gap instead of an
output gap. With an unemployment Taylor rule,
the funds rate responds to deviations of unemployment from its "natural rate," sometimes called the
nonaccelerating inflation rate of unemployment or
NAIRU. The coefficient on the unemployment gap
is usually taken to be 2. Many economists typically
include the lagged funds rate as well. We also include this "inertial" term (which is estimated to be
0.8) because while the funds rate typically moves in
the direction suggested by the Taylor's original rule,
these movements are typically only partial; thus,
it takes a series of policy moves to reach the level a
simple Taylor rule suggests.
The Taylor rule is garnering more attention lately
as many think that the fed funds rate may be raised
soon ("liftoff"). Some believe that the Fed is already
behind the curve and should have raised rates a
while ago. The chart below shows that if the Fed
would have followed an unemployment Taylor rule,
the funds rate today would be 1.85 percent, and
if it had followed the output Taylor rule, the rate
would be 0.69 percent. Notice that there is over
a percentage point difference in these two rules.
Both of these estimates suggest that the Fed may
have been slow to increase rates. But such conclusions depend critically on how accurately potential
output or the natural rate of unemployment is measured (such conclusions also depend in part on the
particular form of the Taylor rule used; for simplicity, this analysis focuses on the simple rule described
13
above). Both concepts are hard to estimate with
any precision, and this lack of precision should be
recognized when policy recommendations are made
using a Taylor-type rule.
Estimates of the Output Gap
Percent
4
3
2
1
O
-1
i------......-----
- . . . - --
- - --
----------
,
~
,,,.
-
2015 CBO
estimates
-3
-4
-5
-6
-7
_ 2007 CBO
-8
estimates
-9
-10 - - -~ -- - ~ - - ~ - - - ~ - - - ~ 2008
2006
2010
2012
2014
2016
Sources: Bureau of Economic Analysis, Congressional Budget Office, and authors'
calculations.
Estimates of the Natural Unemployment Rate
Percent
10
9
8
7
Unemployment
rate
6
- - --- -""-~ - 2015 CBO
5
f--- - -,,..""'-- - - - - - - - - - - - - - natural rate
2007 CBO
4 - - - ~ - - - - - - ~ - - - ~ - - ~ - natural rate
2006
2008
2010
2012
2014
2016
Sources: Bureau of Labor Statistics, Congressional Budget Office.
Estimates of the Unemployment Gap
Percent
1
2015 CBO
o 1--- ---"',,,- - - - - - - - - - - - - --~- =- ~
----
-1
estimates
2007 CBO
estimates
-2
-3
-4
-5
-6 .___ __.__ _ ___.__ _ _..__ __.__ _ __.__
2006
2008
2010
2012
2014
2016
Source: Bureau of Labor Statistics, Congressional Budget Office, authors' calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
Potential output and the natural rate of unemployment are useful economic concepts, but they are
measured with considerable error. Estimates are
extremely di$.cult to make, and large revisions are
made periodically. This is particularly true in the
aftermath of the Great Recession. For example,
the Congressional Budget Office's (CBO) 2007
estimate of what output would be in 2015 differs
from its current estimate by approximately a trillion
dollars. This difference is similar to the decline in
the gap at the trough of the recession. It is as if the
CBO now sees the recession as a permanent shock
to GDP. Put another way, after the large fall in
GDP, output increased at roughly the same rate as
potential (as projected in 2007).
The revision translates into nearly a 7 percentagepoint difference in the output gap. In terms of Taylor rules, this difference suggests that if potential
output today were at the 2007 estimate, the Taylor
rule would call for a federal funds rate of about -5
percent (if interest rates were not constrained by
the zero lower bound). This is not meant to say
that people currently believe that output is over 9
percent below potential. But estimates of potential
GDP are very fluid, and it suggests there is considerable error in our current measure.
The CBO's estimate of the natural rate of unemployment is also fluid, though the revisions are
much smaller. In 2007 the CBO estimated that
the natural rate of unemployment was, and would
remain, at 5 percent. But today the estimate is 5.4
percent. A Taylor rule with the 5 percent natural
rate would predict a federal funds rate of 0.59
percent in 2015:Ql, while the funds rate predicted
would be 1.85 percent with a 5.4 percent natural
rate.
This difference is especially dramatic because a
Taylor rule with our current estimate of the natural
rate suggests that liftoff would have been about a
year ago, in 2013:Q4 (the old natural rate estimate
suggests liftoff in 2014:Q4). Contributing to this
difference is that the CBO now thinks the natural
14
rate peaked at about 6 percent in 2012:Ql. This is
compared to the 5 percent that in 2007 they had
projected it would be in 2012.
Estimates of the Federal Funds Rate Using
Taylor Rule with the Unemployment Gap
Percent
6
5
4
Values at 2015:01
TR, CBO 2015: 1.85
TR, CBO 2007: 0,59
3
2
1
0
-1
-2
.. -
2015 CBO
estimates
.,,-,--., .. 2007 CBO
" ,,,, '
estimates
.,,
-3
-4
-5
-6
2006
2008
2010
2012
2014
2016
Sources: Bureau of labor Statistics, Congressional Budget Office; authors' calculations.
Phillips Curve, 1985-2014
Unemployment gap Q4(t-1 ), percent
2
..
0
-1
I
•
-2
-5
•
•
•
2007 CBO estimates
2015 CBO estimates
2015 CBO before 2010
•
- 6 ' - - - -'----,...__
-1.5
•
••
-3
-4
•
•
_
,...__
_
,...__ _,...__
•
_
,...__~
-0,5
-1.0
1,0
0
0.5
1.5
Change in inflation Q4(t-(t-1 )), percentage points
2.0
Note: Trend line calculated using data from 1985-2009.
Sources: Bureau of Economic Analysis, Bureau of labor Statistics, Congressional
Budget Office, authors' calculations.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
One problem in calculating the natural rate and
potential output is that they both rely on statistical
regularities that do not hold independent of policy
and expectations. Both NAIRU and potential output are basically theoretical constructs that are fundamentally unobservable. For example, to calculate
the natural rate of unemployment the CBO and
others use an empirical relationship called the Phillips curve. The Phillips curve stipulates that when
unemployment is above the NAIRU, inflation will
decrease one year later. During the recession inflation did not decline nearly as much as some estimates of the Phillips curve would predict, and this
discrepancy has continued to this day. The issue
is sometimes referred to as the missing inflation
puzzle. The Phillips curve is a statistical relationship
and is not necessarily stable over time, the CBO
revises its NAIRU series so that it better aligns with
an estimated Phillips curve. This is seen in the chart
below, where the orange dots have been revised up
to the blue dots. While other input such as demographic data is used to determine the NAIRU, the
CBO still relies on an empirical relationship to help
inform it about what NAIRU is.
It should not be surprising that this empirical relationship missed substantially after a big crisis like
the Great Recession, especially given all the unconventional monetary policies during the period.
But a problem arises when this simple statistical
relationship is taken to always hold. Today most researchers believe the Phillips curve is not backward
looking but forward looking. But because expectations are hard to measure, many forecasters use a
backward-looking curve. Arguably during normal
times this may not be too far off, but currently it is
much less clear. The version of the Phillips curve researchers use states that changes in inflation depend
on expectations of the gap going forward and not
just today's gap. One reason inflation did not collapse during the Great Recession is because the Fed
promised low interest rates going forward as well.
How much this mattered quantitatively is not clear,
making it extremely difficult to know how much
missing inflation there should have been during the
Great Recession.
15
Potential output is calculated from the natural rate
series. Thus, potential output will incorporate all
the errors involved in calculating the NAIRU. To
arrive at potential, researchers make some adjustments to the natural rate of unemployment and
use Okun's law to transform unemployment into
output. Okun's law is a historical correlation stating
that when unemployment decreases by 1 percent,
GDP increases by 2 percent. Just like the Phillips
curve, this is actually not a law, but is instead a
rough empirical relationship that has been observed
over time. Unfortunately, this relationship, too,
has fared particularly poorly during the recovery.
Now instead of a missing inflation puzzle, there is a
puzzle over missing output. Unemployment has declined from a high of 10 percent in October 2009
to 5.5 percent today, but GDP has not had nearly
such a robust recovery.
Okun's Law 1985-2014
Q4/Q4 change in unemployment rate, percentage points
4
3
.
2
2008
•
1
0
-1
2010-2014
-2
-3
-2
•
••
-1
0
2
3
4
Q4/Q4 percent change in real GDP
5
6
Note: Trend line calculated using data from 1985-2007.
Sources: Bureau of Economic Analysis, Bureau of Labor Statistics, authors'
calculations.
Full-Time or Part-Time Employment
Percent
Percent
18
81
80
Full-time employment
17
79 ~ - ~-....__- ~- - ' -- ~ - - - ' - - ~ -- ~- ~ 16
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Source: Bureau of Labor Statistics.
These misses are quite large and accumulate. Because of the failure of Okun's law, GDP is about 2
percent below potential, while unemployment is
only 0.3 percentage points above its natural rate.
Potential will likely be revised down in the future if
these misses continue.
But even here the policy implications are not obvious. One reason why Okun's law may have fared so
poorly is because of the increased use of part-time
employment. Part-time workers are not unemployed, but they do not produce the same output as
full-time workers.
Because of the relatively large share of employment
that is part-time, some think that the output gap
may be closer than the unemployment rate gap
in terms of measuring the amount of slack in the
economy. These are important issues for the stance
of policy, but unfortunately there is no dear right
or wrong answer. It does suggest that perhaps we
should pay relatively more attention to inflation in
setting policy than our slack estimates.
While the prevailing methodologies to estimate
NAIRU and potential output are imperfect, they
are still useful. However, everyone needs to be
mindful of the imprecision that results from the
methodologies, and take care in predicting policy
actions like liftoff.
Federal Reserve Bank of Cleveland, Economic Trends I May 2015
16
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17
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