government spending news and the term structure of interest rates (paper)

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.

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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

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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

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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.

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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.

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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(1,4) 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

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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-

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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

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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

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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

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http://ideas.repec.org/s/jae/japmet.html

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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/

http://www.phil.frb.org/research-and-data/real-time-center/survey-of-professional-forecasters

http://www.phil.frb.org/research-and-data/real-time-center/survey-of-professional-forecasters

http://research.stlouisfed.org/fred2/

government spending news and the term structure of interest rates (paper)

Economy & Finance