government spending news and the term structure of interest rates (paper)
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BARCELONA GRADUATE SCHOOL OF ECONOMICS MASTER PROGRAM IN MACROECONOMIC POLICY AND FINANCIAL MARKETS
Government spending news and the term structure of interest rates
Nicola Cofelice
Sarah Zoi
Supervisor: Luca Gambetti
May, 2014
Abstract
Studying the effect of a fiscal policy shock on the term structure of interest rates has long
been a controversial issue. On the one hand, economic theory predicts that government
spending should drive up interest rates; on the other hand, many empirical analyses
found negative or not significant responses of the yield curve to different types of fiscal
shocks. A recent stream of literature on fiscal foresight showed how news about future
fiscal policy may anticipate the effects of public expenditure and pose a challenge for the
recovery of structural shocks due to a problem of non-fundamentalness. We study the
effect of a “foresight shock” on the term structure of interest rates using an identification
strategy based on the information contained in the projections by the Survey of
Professional Forecasters. Our results support the evidence of fiscal foresight and show
how changes in expectations stimulate positive responses of the term structure
anticipating the effects of a government spending shock.
2
1. Introduction
Understanding how the dynamics of the term structure of interest rates is
affected by fiscal imbalances has long been a central issue in financial economics
and macroeconomic literature. Its popularity is due to its crucial importance for
both policy makers and financial markets but gained new relevance in the
immediate aftermath of the last financial crisis. From 2008 many governments
undertook huge discretionary actions as a complement to the countercyclical
effects of automatic stabilizers. For most countries the adoption of expansionary
fiscal policies meant a severe deterioration of their public finances and a
substantial increase in their borrowing costs. These events renewed the interest
in studying the effects of fiscal policy on the business cycle and on financial
markets reopening the debate on the sustainability of large and persistent public
deficits and on their consequences for interest rates, investments and future
growth.
Standard economic theory predicts that an exogenous increase in public
expenditure should drive up interest rates by means of an exogenous shift in
aggregate demand. Beyond this, additional effects on interest rates are produced
if public expenditure is financed with debt. In particular, the issue of new bonds
may increase aggregate demand for credit and cause interest rates to rise,
dampening domestic savings and investments (the so called crowding out effect).
In addition, another convincing argument why interest rates should go up in
response to a public expenditure shock financed by deficit is that as debt
increases, the amount of bonds sold to the public increases as well, generating a
downward pressure on prices and an upward pressure on yields.
Until the last decade the majority of empirical macroeconomic research had been
unable to assess whether fiscal imbalances play a significant role in the dynamics
of the term structure providing mixing and ambiguous evidences of either
negative, mildly positive or no effects on interest rates. For example, Evans and
Marshall (2002) find no evidence of significant responses of interest rates to a
fiscal shocks identified à la Blanchard and Perotti; Caporale and Williams (2001)
find significant negative effect of an increase in deficit-to-GDP ratio on long term
bond yields; Uhlig and Mountford (2002 and 2008) find significant negative
effect of an expenditure shock on interest rates, and a mildly positive one once
they impose a time restriction that allows expenditure to increase only one year
after an hypothetical announcement.
A recent stream of literature on fiscal foresight pointed out how not taking into
account agents’ expectations may lead to a misspecification of the information
set needed to properly address questions on the effects of fiscal policy on the
business cycle. In fact, institutional characteristics of fiscal policy provide clear
3
signals about the timing and the magnitude of future expenditure and these are
immediately incorporated in the agents’ information set. In other terms, fiscal
foresight generates a misalignment between the time in which government
expenditure begins to show its effects (few periods after its announcement) and
the time in which this expenditure leaves its historical trace in the time series. A
technical consequence of fiscal foresight is that the information the
econometrician has at hand at every point in time does not coincide with the one
available to agents. Leeper, Walker and Young (2012) show how, in a structural
VAR framework, this can lead to a non-fundamental representation of the VMA
and pose a serious challenge for the proper identification of structural fiscal
shocks. A solution to this problem would be to enlarge the econometrician
information set in order to account for the information on which agents take
their consumption and investments decisions. However, even if agents are able
to partially anticipate government spending, foresight is not perfect and it’s
reasonable to think that a non-negligible portion of actual expenditure cannot be
foreseen. Hence, the proper way to model a fiscal shock according to the fiscal
foresight literature, would be to distinguish between the anticipated part of the
shock, call it “news shock”, and the one that cannot be foreseen and reflects an
immediate increase in government spending, call it “surprise shock”.
The literature on fiscal foresight rigorously formalized an intuition that many
authors studying the response of the term structure to fiscal shocks had already
implemented: since financial markets are forward looking interest rates are
affected not only by the current and past states of the economy but also by
agent’s expectations about the future. Hence, anticipated future budget deficits
can drive up interest rates today hampering economic activity in the short term.
Moreover, the upward pressure on yields is expected to be stronger on longer
maturities. In fact, under the expectation hypothesis, long-term yields are
determined as a geometric mean of future short-term rates plus a risk premium.
When expected government spending increases, agents know that aggregate
savings and investments will decline pushing up demand for credit and future
short-term interest rates. The increase in expected short-term yields translates
in an immediate increase of current long-term yields that may discourage
investments and consumption, pushing down economic activity today. Hence, a
government expenditure announcement can anticipate of some periods those
effects on financial markets and real activity that are predicted by economic
theory.
Gale and Orszag (2003) underline how taking into account expectations about
government spending in empirical analyses completely overturns the evidence of
the effects of fiscal shocks on the term structure: while more than 50% of
empirical works using current measures of fiscal imbalances find no effects (or
very small effects), the majority of those using expected measures find positive
4
and significant effects. In particular, Cohen and Garnier (1991) find that an
increase of 1 percent point in the revision of the projected deficit by the Office of
Management and Budget (OMB) raises the current 10-year interest rate by
around 50 basis points. Canzoneri, Cumby and Diba (2002) show with univariate
regressions strong positive relations between the slope of the term structure
(spread between 5- years or 10-years yield and 3-months yield) and the
cumulative budget projections at different horizons published by the
Congressional Budget Office (CBO). Laubach (2009) finds strong positive
correlation between the CBO projections and long horizon forward rates using
linear regression analysis.
This work studies the effects of a government spending shock on interest rates
for the US market and investigates what is the role of fiscal foresight in the
propagation of fiscal impulses to the term structure. We accomplish this in three
steps.
First, following what is convincingly suggested by the fiscal foresight literature,
we use the Survey of Professional Forecasters to test for non-fundamentalness in
a VAR with government spending, GDP, inflation, 3 Months Treasury Bill, 5 and
10 years Treasury yields. Our test is the same proposed in Forni and Gambetti
(2012) and it is based on the idea that if the government spending shock in our
six variables specification is correlated with the available information embedded
in the past of the Survey, the model is misspecified and its VMA representation is
non-fundamental. The results of our test reject fundamentalness and suggest
how taking into account the information contained in the Survey may be of
crucial importance to properly identify the fiscal policy shock.
Second, we assume that the SPF is a good proxy of agents’ expectations about
future government spending and define a “news” variable as the revision of the
forecast provided by the Survey. We introduce fiscal foresight in our structural
VAR analysis by adding the news variable ordered first and studying the effects
of an “anticipated” spending shock, or “news shock”, on the term structure. We
find that the responses of interest rates to the news are positive and that longer-
term yields reacts more than shorter-term ones. These results support the
evidence of fiscal foresight: since agents are forward looking and revise their
decisions every time new information is available, a fiscal policy shock displays
its effects before the time in which public expenditure is effectively realized and
few periods after its announcement.
Third, we ask if introducing the information embedded in the Survey overcame
non-fundamentalness and at what extent it is possible to interpret the shock on
government spending as a “surprise shock”. We repeat the orthogonality test and
find that now non-fundamentalness is barely rejected. We argue that, despite of
fiscal foresight, other identification problems can affect this shock.
5
2. Government spending news and the term structure of
interest rates
In order to understand the effect of a government spending increase on the term
structure of interest rate, we estimate a Bayesian structural VAR with flat prior
using an identification strategy of the fiscal shock similar to the one in Blanchard
and Perotti (2002) (Cholesky identification scheme with government spending
ordered first followed by the other variables in the same order presented in the
list above). Our baseline model includes the following variables for USA:
FEDGOV: Real federal government consumption and expenditures and
gross investment (chain-type quantity index, BEA code B823RA3)
GDP: Real Gross Domestic Product (BEA code A191RX1)
INFL: Annual inflation defined as the annualized growth rate of the
Consumer Price Index (CPI).
3MBILL: The 3 Months Treasury Bill, taken as a proxy of short-term
interest rate.
5YBOND: The 5 Years Treasury Constant Maturity Rate, taken as a proxy
of the medium-term interest rate.
10YBOND: The 10 Years Treasury Constant Maturity Rate, taken as a
proxy of the long-term interest rate.
The Federal government expenditure (FEDGOV) and the Gross Domestic Product
(GDP) are taken in logs. The inflation (INFL) has been calculated as the
annualized growth rate of the quarterly Consumer Price index (CPI); the Three
Months Treasury Bill Rate (3MBILL), the 5 Years the 10 Years Treasury Constant
Maturity (5YBOND and 10YBOND) are yields, and therefore already annualized.
The time span of our database is 1981:III – 2013:IV and contains 130 quarterly
observations. Multivariate and univariate residuals autocorrelation tests1 show
that the model is correctly specified with four lags.
We want to underline that the implicit assumption behind the specification
above is that there is no delay between the announcement of an increase in
government spending, its implementation and its effects on agents’ consumption
and investments choices. In the fiscal foresight terminology, our first shock can
only be interpreted as a “surprise”, a not announced and unexpected expenditure
shock that immediately pushes agents to revise their decisions. The impulse
response functions are displayed in FIGURE 1.
1 We performed both the Portmanteau and the Breusch-Godfrey tests for multivariate and univariate autocorrelation of residuals in Gretl.
6
FIGURE 1 IMPULSE RESPONSE FUNCTIONS AND 68% BAYESIAN CONFIDENCE BANDS OF
FEDGOV, GDP, INFL, Y3M, 5YBOND AND 10YBOND TO THE FIRST SHOCK. THE IRFS ARE THE
AVERAGES OF THE POSTERIOR DISTRIBUTION OVER 2000 DRAWS
The response of GDP is slightly positive on impact and then turns negative after
the second quarter, showing certain persistence in the long run. The response of
INFLATION is more controversial and barely significant in the first quarters but
suggests a tendency of prices to reduce on impact. The term structure shows a
clear puzzling response: the 3MBILL decreases immediately and reaches its
minimum after 10 quarters, while the 5YBOND and 10YBOND follow a similar
pattern increasing slightly in the first two quarters and decreasing dramatically
after that.
We now ask if this shock has the characteristics of a “surprise” and we carry out
a test to verify this assumption.
2.1. Testing for non-fundamentalness
In order to be able to label the first shock as a “surprise” (here denoted as ), a
critical step is to check whether the information it delivers was not forecastable
in previous periods using data that economic agents had at hand at that time. We
test this hypothesis assuming that the Survey of Professional Forecasters2 is a
good proxy of agents’ expectations given the information set available at the time
of the forecast. Following Forni and Gambetti (2012) we check the presence of
non-fundamentalness by means of a global significance test on the coefficients of
different auxiliary regressions of the first shock on the SPF’ forecasts at different
horizons.
2 The Survey of Professional Forecasters (SPF) is the oldest quarterly survey of macroeconomic forecasts in the United States. The survey began in 1968 and it was conducted by the American Statistical Association and the National Bureau of Economic Research. The Federal Reserve Bank of Philadelphia took over the survey in 1990
7
The Survey reports the forecasts made at time t for periods t (with
) of the annualized growth of a set of macroeconomic variables. For
the purposes of the test, we use
SPF_GOV: The forecast of the federal government expenditure growth for
the next quarter (with respect to the current one) and for the next four
quarters.
SPF_GDP: The nowcast of the GDP growth for the current quarter and the
forecast for the next four quarters.
SPF_CPI: The nowcast of the Consumer Price Index and the forecast for
the next four quarters.
For each variable we also calculate the cumulated forecasts between and ,
defined as
3 ∑
,
Then, we estimate the government spending shock from our six variables VAR
and regress it on six sets of regressors: the first five include the series listed
above, from f(0) to f(4) taking one forecast horizon at a time, while the sixth set
includes the cumulated growth of all the three variables over the four horizons:
∑
TABLE 1 provides an overview of the regressions carried out to verify the
orthogonality between our supposed “surprise shock” and the variables of the
Survey.
3 Where we divided by to get the average annual growth with respect to the period of the
forecast.
8
Regressors Regression model Null
hypothesis
f(0) SSt = β1 + β2 SPF_GOVT-LAGS(0) + β3 SPF_GDPT-LAGS (0) + β4 SPF_CPIT-LAGS (0) + εt β2 = β3 = β4 = 0
f(1) SSt = β1 + β2 SPF_GOVT-LAGS(1) + β3 SPF_GDPT-LAGS (1) + β4 SPF_CPIT-LAGS (1) + εt β2 = β3 = β4 = 0
f(2) SSt = β1 + β2 SPF_GOVT-LAGS(2) + β3 SPF_GDPT-LAGS (2) + β4 SPF_CPIT-LAGS (2) + εt β2 = β3 = β4 = 0
f(3) SSt = β1 + β2 SPF_GOVT-LAGS(3) + β3 SPF_GDPT-LAGS (3) + β4 SPF_CPIT-LAGS (3) + εt β2 = β3 = β4 = 0
f(4) SSt = β1 + β2 SPF_GOVT-LAGS(4) + β3 SPF_GDPT-LAGS (4) + β4 SPF_CPIT-LAGS (4) + εt β2 = β3 = β4 = 0
F(1,4) SSt =β1+β2 SPF_GOVT-LAGS(1,4)+β3 SPF_GDPT-LAGS (1,4)+β4 SPF_CPIT-LAGS (1,4)+εt β2 = β3 = β4 = 0
TABLE 1: OVERVIEW OF THE AUXILIARY REGRESSIONS FOR THE NON-FUNDAMENTALNESS TEST
If the test does not reject the null hypothesis (p-value > α), our “government
spending shock” cannot be explained by a linear combination of the information
contained in the SPF. If it is the case, the model does not suffer from non-
fundamentlaness and our shock can be actually labeled as a “surprise” since it
was not predictable by the Forecasters and was not embedded in the agents’
information set.
Conversely, if the test rejects the null (p-value < α), the “government spending
shock” cannot be considered orthogonal to the SPF, meaning that it delivers
some information that was already included in agents’ information set. Hence,
the change in public expenditure with respect to the previous period was (at
least) partially predicted by the Forecasters and the shock cannot be considered
an actual “surprise”. If it is case, our model will suffer from non-fundamentalness
and we cannot attribute an unambiguous economic meaning to the impulse
response functions reported in FIGURE 1.
The outcomes of the F-test are reported in TABLE 2
9
TABLE 2 P-VALUE OF THE F-TEST FOR ORTHOGONALITY BETWEEN THE SURPRISE SHOCK AND
THE INFORMATION OF THE SURVEY OF PROFESSIONAL FORECASTERS
The results above show that for several combinations of regressors-number of
lags, the test rejects the null of orthogonality between the shock and the SPF at
90-95%. This means that our six variables model suffers from non-
fundamentalness. Hence, our first shock cannot be considered an “unexpected”
shock on public expenditure and the relative IRFs cannot be properly
interpreted.
2.2. Identification of the “foresight shock”
Let’s sum up our analysis until this point. We started asking what was the effect
of a government spending shock on the term structure. We chose a structural
VAR model with six variables, Cholesky identification with government spending
ordered first. The implicit assumption behind this specification is that there is no
delay between the time in which the government spending is implemented and
the one in which it is announced, that is our shock represents a “surprise” in
government expenditure. We explained in the introduction that, according to
what is argued by the fiscal foresight literature, this assumption is not exactly
true in practice. In fact, institutional characteristics of fiscal policy provide
signals about future public expenditure that affect agents’ decisions few periods
before the actual implementation of the measures. If this is true, it means that
the first shock in our six-variables VAR cannot represent an “unexpected”
government expenditure shock. We proved this statement giving evidence that
the shock was actually forecasted by the Survey of Professional Forecasters and
hence it is not properly identified.
Given this identification problem, a natural way to proceed would be to include
in our six-variables VAR the information about future expenditure delivered by
the Survey of Professional Forecasters. Since this information was proved to be
useful to predict our government spending shock but at the same time is crucial
to introduce agents expectations, including it in our model should overcome non-
10
fundamentalness and enable the identification of the “foresight”, or “anticipated”
shock.
In order to do this we follow Forni and Gambetti (2014) introducing a variable
intimately connected with agents’ foresight and is defined as the revision of the
expected government spending growth, , for the next three quarters:
∑
Relying on the previous assumption that the Survey of Professional Forecasters
is a good proxy of agents’ expectations, we approximate the revision in the
expectations about future government spending with the revision of its forecast
taken from the Survey:
This new variable describes how agents change their projections about future
with respect to the previous period due to new information available. In this
sense it is a proxy of the portion of government spending growth that is foreseen
in each period.
We cleaned this variable from spurious terms running an OLS regression of
onto a constant, and the revision of the forecast of GDP,
and of CPI, . The estimated residual represents our spending news
variable (called “NEWS”). Its correlation with the revision of spending growth,
, is 0.98 hence the cleaning was almost ineffective. Our cleaned NEWS
variable is illustrated in FIGURE 2. We can observe a correspondence between the
positive and negative spikes of the NEWS variable and important historical
events.
11
FIGURE 2: NEWS VARIABLE
We introduce in our baseline model the NEWS variable ordered first keeping
unchanged the ordering of the other variables (we now have the following
scheme: NEWS, FEDGOV, GDP, INFL, Y3M, 5YBOND and 10YBOND).
Applying Cholesky decomposition we identified the “foresight shock” as the
unexpected revision of agents’ forecast about future government expenditure
due to the availability of new information. It is important to remember that,
differently from the “surprise shock”, the “foresight shock” is not connected with
a contemporaneous government expenditure, but affects agents’ expectations
pushing them to revise their consumption and investment decisions.
FIGURE 3 shows the impulse response functions of the seven variables to an
“anticipated” government expenditure shock or “foresight shock” and the 68%
Bayesian confidence bands.
12
FIGURE 3: IMPULSE RESPONSE FUNCTIONS AND 68% BAYESIAN CONFIDENCE BANDS OF
NEWS, FEDGOV, GDP, INFL, Y3M, 5YBOND AND 10YBONDTO A NEWS SHOCK. THE IRFS ARE THE
AVERAGES OF THE POSTERIOR DISTRIBUTION OVER 2000 DRAWS
As expected, the response of government spending to the “foresight shock” is not
significant on impact and it reaches its maximum after 10 quarters. Both GDP
and Inflation increase on impact, but after 5-7 quarters the effect is negligible.
The responses of interest rates are similar: all of them increase on impact, reach
a peak after 3 quarters, shrink below their initial level and tend to zero after 17-
20 quarters.
We observe that the “foresight shock” drives up GDP and inflation on impact
stimulating a positive response of the short-term interest rate. As expected, the
responses of longer maturity yields are positive due to the increase in current
short-term rate and in expected future rates. The increase in output contradicts
the anticipated crowding out effect predicted by the theory. However, a deeper
investigation on the responses of consumption and investments (that goes
beyond the purposes of this work) would be necessary in order to disentangle
the effects of the “foresight shock” on real aggregates4.
It is interesting to stress the different magnitudes of the effects of the “news”
shock on different maturities: a 1% increase in the revision of government
spending forecast increases 3-months Treasury yield of at most 12 basis points,
while the responses of 5 and 10-years rates reach respectively 21 and 23 basis
points. This is in line with what was anticipated in the introduction: the
4 It’s important to underline here that the effects of the “foresight shock” on aggregate output may also depend on the way in which government expenditure is financed if increasing taxes or issuing new debt.
13
announcement of an increase in future government expenditure makes agents to
revise upward their expectations on future short-term interest rates driving up
current long-term yields.
These results support the evidence of fiscal foresight and are in line with those
presented by the literature on the effects of expected measures of fiscal
imbalances on the term structure.
2.3. Identification of the “surprise shock”
In the previous paragraph we proved the presence of fiscal foresight and verified
its role in anticipating the effects of a government spending shock as predicted
by economic theory. Given that our “foresight shock” is well identified, we now
ask if introducing the NEWS variable overcame non-fundamentalness and helped
to identify the “surprise shock”.
In order to test this, we repeated again the test described in TABLE 1: we
regressed the Surprise shock (now the one ordered second in our seven-
variables specification) onto six different sets of forecasted variables from the
Survey and run a global significance test on the estimated coefficients. The
outcomes of the test are displayed in TABLE 3
TABLE 3 P-VALUES OF THE F-TEST FOR ORTHOGONALITY BETWEEN THE SURPRISE SHOCK
AND THE INFORMATION OF THE SURVEY OF PROFESSIONAL FORECASTERS
The results above do not enable us to clearly assess whether the “surprise” shock
is orthogonal to the information contained in the Survey or not. With respect to
our six variables specification, the introduction of the NEWS variable increased
some p-values in favor of orthogonality but there are still some combinations of
lags and forecast horizons for which the p-values are below 0.1. This means that
the “surprise” shock may still be not well identified.
FIGURE 4 shows the impulse response functions of the seven variables to the
second shock (our supposed “surprise shock”) together with the 68% Bayesian
14
confidence bands. These impulse responses change very little with respect to the
ones represented in FIGURE 1.
FIGURE 4: IMPULSE RESPONSE FUNCTIONS AND 68% BAYESIAN CONFIDENCE BANDS OF
NEWS, FEDGOV, GDP, INFL, Y3M, 5YBOND AND 10YBOND TO THE SECOND SHOCK. THE IRFS
ARE THE AVERAGES OF THE POSTERIOR DISTRIBUTION OVER 2000 DRAWS
We have good reasons to think that, even after the introduction of the NEWS
variable, our supposed “surprise shock” is still not identified. In fact, despite the
ambiguous result of the orthogonality test, it is reasonable to suspect that our
framework is affected by another serious threat for the identification of the
“surprise shock”. The issue comes from the need of disentangling the effect of
fiscal policy from the effects of many other variables that may influence interest
rates at the same time. One clear example of this is the state of the business cycle.
If automatic stabilizers increase expenditure during recessions but monetary
policy cut interest rates at the same time in response to a slowing down in
economic activity and a drop in inflation, then observing a puzzling behavior of
the term structure like the one highlighted above shouldn’t be surprising. If both
fiscal and monetary authorities show this automatic and countercyclical pattern
(as they usually do5), it is possible that what we wanted to label as the “response
of the term structure to a unexpected fiscal shock” is, in truth, a mixed effect of
the two policies.
In light of this additional identification issue, the analysis of the effects of the
“anticipated shock” gains even more relevance: if current measures of public
expenditure and interest rates are correlated in a way that is also influenced by
5 The countries in the European Monetary Union represent an important exception.
15
other dynamics, considering expected measures of government expenditure may
presumably overcome the problems connected with the contemporaneous
interaction between the effects of countercyclical monetary policy and fiscal
automatic stabilizers6.
To conclude our analysis we illustrate the importance of the “foresight shock”
and the second shock for the dynamics of the term structure. FIGURE 5 shows
the variance decomposition of the three interest rates: the blue and the red lines
represent the percentage of the forecast error variance explained by the
“foresight” and by the second shock respectively. The percentage of the variance
explained by NEWS represents only the 5% of the total variance of the 3 Months
Treasury Bill in the long run. However, for longer maturities the portion of the
total variance explained by the shock is much higher and represents 20% - 25%.
This confirms again that fiscal policy news shocks affect long term interest rates
more than shorter term ones.
FIGURE 5: VARIANCE DECOMPOSITION OF THE TERM STRUCTURE IN THE SEVEN -VARIABLES
SPECIFICATION. PERCENTAGE OF THE FORECAST ERROR VARIANCE EXPLAINED BY THE
“FORESIGHT” SHOCK AND BY THE SECOND SHOCK.
6 The same argument (the mixed effects of countercyclical fiscal and monetary responses to the business cycle) motivates the analysis in Laubach (2009) on the relationship between projected budget deficits for the next 5- 10 years and forward interest rates.
16
3. Conclusions
This work studied the effects of a government spending shock on interest rates
and investigated what is the role of fiscal foresight in the propagation of fiscal
impulses to the term structure.
We started by setting a VAR with six variables and four lags: government
spending, GDP, inflation, 3 months, 5 and 10 years Treasury yields. We showed
that the first shock identified using a Cholesky scheme couldn’t be interpreted as
a “fiscal foresight shock” because since it didn’t show any fiscal delay. We proved
that it couldn’t be interpreted neither as an “unanticipated fiscal shock”, since it
could be explained by a linear combination of the information contained in the
Survey of Professional Forecaster and therefore could be predicted by economic
agents. We concluded that our six-variables specification suffered from non-
fundamentalness: the information set of the economic agents is larger than the
one of the econometricians and this represents an obstacle for retrieving the
desired shock.
We defined a “news variable”, defined as the revision of the expected
government spending growth gt, and we introduced it in our VAR ordering it as
first variable. We showed that the responses of interest rates to the news were
positive and that longer-term yields react more than shorter-term ones. These
results support the evidence of fiscal foresight: since agents are forward looking
and revise their decisions every time new information is available, a fiscal policy
shock does not display the effects predicted by the theory at the time in which
public expenditure is effectively realized but few periods after its announcement.
Finally, we tested whether introducing the information embedded in the Survey
overcame non- fundamentalness and at what extent it was possible to interpret
the shock on government spending (now ordered second) as a “surprise shock”.
We repeated the orthogonality test and found that non-fundamentlaness was
barely rejected. We finally examined the importance of the first two shocks in
explaining the variance of the interest rates. We found that the “news shock”
explains up to 20-25% of the total variance of the long term yields.
17
References
1. Blanchard O., Perotti R., 2002, “An empirical characterization of the dynamic
effects of changes in government spending and taxes on output”, The Quarterly
Journal of Economics, MIT Press, vol.117(4), pages 1329-1368.
2. Canzoneri M., Cumby R., Diba B., 2002, “Should the European Central Bank and
the Federal Reserve be concerned on fiscal policy?” in “Federal Reserve Bank of
Kansas City’s Rethinking Stabilization Policy” and working paper Georgetown
University.
3. Caporale G.M. and G. Williams , 2002, “Long-term Nominal Interest Rates and
Domestic Fundamentals”, Review of Financial Economics, 11:119-130
4. Dai Q., Philippon T., 2005, “Fiscal Policy and the term structure of interest rates”,
NBER Working Paper No. 11574.
5. Engen E.M., Hubbard G.R., 2004, “Federal government debt and interest rates”, in
M. Gertler and K. Rogoff (eds.) NBER Macroeconomics Annual, Cambridge: MIT
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6. Evans C., Marshall D., 2001, “Economic Determinants of the Nominal Treasury
Yield Curve”, Working Paper Series, WP-01-16, Federal Reserve Bank of Chicago.
7. Forni M., Gambetti L., 2011, “Testing for sufficient information in structural VARs”,
CEPR Discussion Papers n. 8209.
8. Forni M., Gambetti L., 2014, “Government spending foresight shocks”.
9. Gale G., Orszag P.R., 2003, “The economic effects of long term fiscal discipline”,
Discussion Paper n.8, Tax Policy Center, Urban Institute and Brookings
Institution.
10. Laubach T., 2009, “New Evidence on the Interest Rates Effects of Budget Deficits
and Debt”, Journal of European Economic Association, 7(4):858-885.
11. Mountford A., Uhlig H., 2002, 2008 and 2009, “What are the effect of fiscal policy
shocks?” Journal of Applied Econometrics, vol. 24(6), pages 960-992.
12. Perotti R., 2002, “Estimating the effects of fiscal policy in OECD countries”, CEPR
Discussion Paper No.3380.
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Websites and Databases
1. Federal Reserve Bank of Philadelphia, Survey of Professional Forecasters:
http://www.phil.frb.org/research-and-data/real-time-center/survey-of-
professional-forecasters
2. Federal Reserve Bank of S. Luis, FRED database:
http://research.stlouisfed.org/fred2/
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