View
220
Download
1
Tags:
Embed Size (px)
Citation preview
Identifying Government Spending Identifying Government Spending Shocks:Shocks:
It’s all in the timingIt’s all in the timing
ByBy
Valerie A. RameyValerie A. Ramey
What is the Impact of an Increase in Government Spending?
Traditional Keynesian
Increase in Government Spending leads to:
↑ Output ↑ Consumption
↑ Hours ↑ Real Wage
↓ Investment
Neoclassical Theory: Impact of Increased Gov.
Aiyagari, Christiano, & Eichenbaum (1992), Baxter & King (1993)
Key mechanism: Negative wealth effect increases labor supply
↑ Output ↓ Consumption
↑ Hours ↓ Real Wage
↓ or ↑ Investment
New Keynesian Theory
Rotemberg & Woodford (1992): Countercyclical markups
Devereux, Head & Lapham (1996): increasing returns
Gali, Lopez-Salido, Valles (2007): sticky prices, noncompetitive labor markets, rule-of-thumb consumers
Ravn, Schmitt-Grohe, Uribe (2006): deep habits
Empirical Evidence on the Impact of ↑ G
• Papers using VAR techniques for identification
e.g. Rotemberg and Woodford (1992), Blanchard and Perotti (2002), Fatás and Mihov (2001), Perotti (2004), Montford and Uhlig (2005), Galí, López-Salido, and Vallés (2006)), Perotti(2007)
↑ output ↑ hours ↑ consumption ↑ real wages ↓ investment • Papers using Ramey-Shapiro war dates
e.g. Ramey and Shapiro (1998), Edelberg, Eichenbaum, and Fisher (1999), Burnside, Eichenbaum, and Fisher (2004), and Cavallos (2005)).
↑ output ↑ hours ↓consumption ↓ real wages ↓ or ↑ investment
• Other Event Studies
e.g. Giavazzi & Pagano’s (1990), Cullen & Fishback (2006), Barro & Redlick (2009)
↓consumption or no effect
Outline of talk1. Overview of behavior of government spending2. Generate results using the two methods on
the same data and time period3. Compare the timing of war dates to the
timing of VAR shocks4. Present theoretical model that explains both
results5. Show that changing the timing on the war
dates produces the same results as the VAR6. Introduction of 2 new “news” variables and
extension of sample back to 1939
Red lines are Ramey-Shapiro military dates + 9/11.
(numbers in thousands of 2005 dollars)
11.
52
2.5
33.
5
1950 1960 1970 1980 1990 2000 2010year
Real Defense Spending Per Capita
34
56
78
1950 1960 1970 1980 1990 2000 2010year
Real Government Spending Per Capita
Some Historical Perspective
Real Defense Spending Per Capita
(thousands of 2005 dollars)
02
46
810
1940 1950 1960 1970 1980 1990 2000 2010year
Components of Government SpendingFraction of nominal GDP
0.0
5.1
.15
1950 1960 1970 1980 1990 2000 2010year
defense nondefense federalstate & local
Two Leading Identification Methods
1. VAR method Order government spending first in a VAR, use standard Choleski
decomposition to identify shocks to government spending.
2. Ramey-Shapiro Dates
Augment system with a dummy variable that takes the value of unity at times when a major political event caused Business Week to begin forecasting large increases in defense spending. Shocks to the dummy variable rather than actual government spending are identified as the shock.
Ramey-Shapiro Dates: The dummy variable takes the value of unity at 4 dates.
1950:3: North Korea invaded South Korea in late June 1950.
1965:1 Johnson began air strikes against N. Vietnam in Feb. 1965.
1980:1 The USSR invaded Afghanistan on Dec. 24, 1979.
*2001:3 Terrorists struck the World Trade Center and the Pentagon on 9/11.
Framework for Comparison
X includes: total govt spending, GDP, Barro-Redlick tax rate, total hours (including military), nondurable + services consumption, private fixed investment, product wage in private business.
Log per capita (except BS tax, product wage)
Quarterly data: 1947:1 – 2008:4, 4 lags, quadratic trend
ttt UXLAX 1)(
Identification
Standard VAR: order government spending first
War dates: also include current and 4 lags of Ramey-Shapiro war dates variable augmented with 9/11.
1950:3, 1965:1, 1980:1, 2001:3
Set shock size so that peak response of government spending is the same across specifications
Comparison of Identification Methods VAR shocks in top row; War dates in bottom row
68% bands
-.4
0.4
.81.
2
0 4 8 12 16 20quarter
government spending
-.2
0.2
.4
0 4 8 12 16 20quarter
gdp
-.12
0.1
2.2
7
0 4 8 12 16 20quarter
total hours
-.4
0.4
.81.
2
0 4 8 12 16 20quarter
government spending
-.2
0.2
.4
0 4 8 12 16 20quarter
gdp
-.12
0.1
2.2
70 4 8 12 16 20
quarter
total hours
Comparison of Identification Methods VAR shocks in top row; War dates in bottom row
68% bands
-.2
-.1
0.1
0 4 8 12 16 20quarter
consumption, ndur + serv
-.9
-.6
-.3
0.3
0 4 8 12 16 20quarter
investment, nonres + res
-.18
-.12
-.06
0.0
6.1
2
0 4 8 12 16 20quarter
real wages
-.2
-.1
0.1
0 4 8 12 16 20quarter
consumption, ndur + serv
-.9
-.6
-.3
0.3
0 4 8 12 16 20quarter
investment, nonres + res
-.18
-.12
-.06
0.0
6.1
2
0 4 8 12 16 20quarter
real wages
Why Do these Two Methods Give Such Different Results?
It’s All in the timing
I will argue that the shocks identified by the VAR are mostly anticipated and this explains all of the difference in the results.
0.5
1
1948 1949 1950 1951 1952 1953 1954 1955
Defense Spending During Korean War
.4.5
.6.7
1964 1965 1966 1967 1968 1969 1970
Defense Spending During Vietnam War
-.1
-.05
0.0
5.1
.15
1948 1949 1950 1951 1952 1953 1954 1955
VAR Shocks During Korean War
-.04
-.02
0.0
2.0
4
1964 1965 1966 1967 1968 1969 1970
VAR Shocks During Vietnam War
10
20
30
40
50
1950 1951 1952 1953 1954fiscal year
actual Jun 50 forecastAug 50 forecast Sep 50 forecast
Business Week Defense Forecasts
50
60
70
80
1965 1966 1967 1968 1969fiscal year
actual Jan 65 forecastAug 65 forecast Sep 66 forecastJan 67 forecast May 67 forecast
Business Week Defense Forecasts
.2.3
.4.5
.6
1978 1980 1982 1984 1986
Defense Spending During Carter-Reagan
.15
.2.2
5.3
.35
.4
1999 2000 2001 2002 2003 2004
Defense Spending During 9/11
-.04
-.02
0.0
2.0
4
1978 1980 1982 1984 1986
VAR Shocks During Carter-Reagan
-.1
-.05
0.0
5.1
1999 2000 2001 2002 2003 2004
VAR Shocks During 9/11
100
150
200
250
300
350
1980 1982 1984 1986fiscal year
actual Jan. 1979 forecastJan. 1980 forecast Jan. 1981 forecastOct. 1981 forecast
OMB Defense Forecasts
300
350
400
450
500
2001 2002 2003 2004 2005 2006
actual Apr. 2001 forecastFeb. 2002 forecast Feb. 2003 forecastFeb. 2004 forecast
OMB Defense Forecasts
Survey of Professional Forecasters
Forecasts of Real Federal Spending Growth from Quarter t-1 to t+4
0.0
2.0
4.0
6
1999 2000 2001 2002 2003 2004
Are VAR shocks anticipated? Yes.
Hypothesis Tests p-value in parenthesis
Do War dates Granger-cause VAR shocks? Yes (0.017)
Do one-quarter ahead Professional Forecasts Granger-cause VAR shocks? 1981:3 – 2008:4
Yes (0.025)
Do four-quarter ahead Professional Forecasts Granger-cause VAR shocks? 1981:3 – 2008:4
Yes (0.016)
Do VAR shocks Granger-cause War dates? No (0.148)
A simple model shows how getting the dates wrong can lead to faulty
inferences.
Very simple model with government spending and lump-sum taxation.
Simulate a defense spending process similar
to data. The key is that the new spending is
announced two quarters before it happens, similar to what we saw in the data.
Model
2
321
1
1
lnln
028.0,ln25.0ln18.0ln4.1constant ln
008.0,ln95.ln
01.0,ln95.ln
)023.01(1
)1log()log(33.067.0)(
tt
egttttt
ettt
ezttt
GFG
egGFGFGFGF
e
ezZZ
tKtItKtGtItCtY
tNttCUtKtNtZtY
Theoretical Effect of an Increase in Government Spending(announced two quarters in advance)
0.2
5.5
.75
1g
g
0 4 8 12 16 20quarter
government spending
0.0
1.0
2.0
3.0
4yg
0 4 8 12 16 20quarter
gdp
0.0
1.0
2.0
3.0
4.0
5n
g
0 4 8 12 16 20quarter
hours
-.0
4-.
03
-.0
2-.
01
0cg
0 4 8 12 16 20quarter
consumption
-.2
-.1
0.1
.2.3
ig
0 4 8 12 16 20quarter
investment
-.0
15
-.0
1-.
00
50
wg
0 4 8 12 16 20quarter
real wages
Estimation of VARs on Simulated Data Actual Spending Identification in top row; News in bottom row
68% bands
0.4
.81.
2
0 4 8 12 16 20quarter
government spending
-.1
-.05
0.0
5
0 4 8 12 16 20quarter
gdp
-.05
0.0
5.1
0 4 8 12 16 20quarter
hours
0.4
.81.
2
0 4 8 12 16 20quarter
government spending news
-.1
-.05
0.0
5
0 4 8 12 16 20quarter
gdp
-.05
0.0
5.1
0 4 8 12 16 20quarter
hours
Estimation of VARs on Simulated Data Actual Spending Identification in top row; News in bottom row
68% bands
-.05
-.02
50
.025
.05
0 4 8 12 16 20quarter
consumption
-.8
-.4
0.4
0 4 8 12 16 20quarter
investment
-.06
-.04
-.02
0.0
2
0 4 8 12 16 20quarter
real wages
-.05
-.02
50
.025
.05
0 4 8 12 16 20quarter
consumption
-.8
-.4
0.4
0 4 8 12 16 20quarter
investment
-.06
-.04
-.02
0.0
20 4 8 12 16 20
quarter
real wages
If the argument is right, then delaying the military dates should produce the standard VAR results
Procedure: delay Ramey-Shapiro dates to time of first big VAR defense shock.
Actual Delayed Dates
1950:3 1951:11965:1 1965:31980:2 1981:42001:3 2003:2
Empirical Results from Delaying the Timingof the Ramey-Shapiro Military Dates
Estimates on Actual Data
-.5
0.5
11.
5
0 4 8 12 16 20quarter
government spending
-.2
0.2
.4
0 4 8 12 16 20quarter
gdp
-.1
0.1
.2.3
0 4 8 12 16 20quarter
total hours
-.05
0.0
5.1
.15
0 4 8 12 16 20quarter
consumption, ndur + serv
-.4
-.2
0.2
.4.6
0 4 8 12 16 20quarter
investment, nonres + res
-.1
0.1
.2.3
0 4 8 12 16 20quarter
real wages
A New Measure of Defense Shocks
The simple dummy variable incorporates only a small part of the information available in the narrative record.
Thus, I created a new variable: the present discounted value of the forecasted changes in defense-related spending. This is what matters for the wealth effect.
I created the variable by reading mostly Business Week, but also the New York Times, Washington Post, and Wall Street Journal from January 1939 to December 2008.
Business Week 5/25/40, p. 60: “The German drive to the English Channel this week assured quick adoption of the President’s program to speed up war preparations. But the proposed expenditure of less than $3.5 billion in the coming fiscal year is only a small beginning; of that, business men can now be certain… In the 1919 fiscal year costs ran to $11 billion. A major war effort in the ‘40s would come higher… since we have started six years behind, a vast outlay is required if we are to attain military parity with Hitler’s industrial machine. In a major war at least four times the $3.5 billion we plan to spend in 1941 would be needed, and quite conceivably five to six times that – or anywhere from 20% to 30% of the peacetime national income. However, it is not possible to jump immediately up from a $3.5 billion to a $14 billion military effort. It takes time to shift a nation from a peace economy to a war-preparation economy and thence to a war economy. Right now we are at the very beginnings of a war-preparation economy.”
Business Week 10/25/41: “Expect dramatic developments in the defense program in the next few weeks. Plans were under way before the Kearny incident and the sinking of two more American ships but they have been speeded by this week’s shocks and by the heartening reports on Russia’s capacity to hold out, brought home by the Harriman mission. Beginning this week, war production –and it’s ‘war,’ not ‘defense’ …-becomes the No. 1 item on the business docket.” p. 7
“Already, Washington is taking cognizance of the imminence of a shooting war…A year ago, the government thought of armament expenditures of $10 billion a year; six months ago the goal was $24 billion; as recently as last month $36 billion was regarded as a desirable but hard-to-achieve outlay; but now an annual expenditure of $50 billion is begin seriously discussed – not as the desirable goal, but as an inescapable necessity.” p. 13
Defense News:PDV of Expected Change in Spending as a % of lagged
GDP
-20
02
04
06
08
0
1940 1950 1960 1970 1980 1990 2000 2010year
Explanatory Power of New Defense Shock Seriesfor Growth of Real Per Government Spending
Columns 1 and 2 from regression on current and 4 lags of shock. Column 3 is from full VAR.
1 2 3
R-squared F-statistic Marginal F-statistic
1939:1 – 2008:4 0.410 37.58 9.00
1947:1 – 2008:4 0.518 51.15 19.65
1955:1 – 2008:4 0.037 1.60 0.429
Framework for Defense News VAR
X includes: defense news variable (as a % of lagged GDP), total govt spending, GDP, Barro-Redlick tax rate, 3-month T-bill rate
6th variable is rotated in.
Newly constructed Quarterly data: 1939:1 – 2008:4, 4 lags, quadratic trend
ttt UXLAX 1)(
VAR with Defense News Variable: 1939-2008(red lines: 68%; green lines: 95%)
-.5
0.5
11.
5
0 4 8 12 16 20quarter
government spending
-.1
0.1
.2.3
0 4 8 12 16 20quarter
gdp
-.1
0.1
.2
0 4 8 12 16 20quarter
total hours
-.2
-.1
0.1
.2.3
0 4 8 12 16 20quarter
manufacturing wage
-6-4
-20
24
0 4 8 12 16 20quarter
3 month Tbill rate
-.05
0.0
5.1
.15
0 4 8 12 16 20quarter
average marginal income tax rate
VAR with Defense News Variable: 1939-2008(red lines: 68%; green lines: 95%)
-40
-20
020
0 4 8 12 16 20quarter
real baa bond rate
-.2
-.15
-.1
-.05
0.0
5
0 4 8 12 16 20quarter
nondurable consumption
-.05
0.0
5.1
.15
0 4 8 12 16 20quarter
services consumption
-1-.
50
.5
0 4 8 12 16 20quarter
consumer durable purchases
-1-.
50
.5
0 4 8 12 16 20quarter
nonresidential investment
-2-1
.5-1
-.5
0.5
0 4 8 12 16 20quarter
residential investment
Effect of Defense Shocks with and without WWII(Blue: 1939 - 2008; green: 1947 – 2008)
-.5
0.5
1
0 4 8 12 16 20quarter
government spending
-.05
0.0
5.1
.15
.2
0 4 8 12 16 20quarter
gdp
-.05
0.0
5.1
.15
0 4 8 12 16 20quarter
total hours
-.3
-.2
-.1
0.1
0 4 8 12 16 20quarter
manufacturing wage
-4-3
-2-1
01
0 4 8 12 16 20quarter
3-month Tbill rate
-.1
-.05
0.0
5.1
0 4 8 12 16 20quarter
average marginal income tax
Effect of Defense Shocks with and without WWII(Blue: 1939 - 2008; green: 1947 – 2008)
-40
-20
020
0 4 8 12 16 20quarter
real baa bond rate
-.1
-.05
0.0
50 4 8 12 16 20
quarter
nondurable consumption
-.02
0.0
2.0
4.0
6.0
8
0 4 8 12 16 20quarter
services consumption
-.5
0.5
0 4 8 12 16 20quarter
consumer durable purchases
-.8
-.6
-.4
-.2
0.2
0 4 8 12 16 20quarter
nonresidential investment
-1-.
50
.5
0 4 8 12 16 20quarter
residential investment
Alternative Measure for Later Period
•The defense news variable has little predictive power for the post-Korean War Period.
•As an alternative, I use government spending shocks constructed using data from the Survey of Professional Forecasters.
1968:4 – 1981:2: Forecast nominal defense spending, GDP deflator
1981:3 – 2008:4: Forecast real federal spending
Shock(t) = Δ ln(gt) - Et-1Δ ln(gt)
VAR with Shock Based on Professional Forecasters:1968-2008
(red lines: 68%; green lines: 95%)-.
50
.51
1.5
0 4 8 12 16 20quarter
government spending
-1-.
50
.50 4 8 12 16 20
quarter
gdp
-.6
-.4
-.2
0.2
.4
0 4 8 12 16 20quarter
total hours
-.4
-.2
0.2
.4
0 4 8 12 16 20quarter
business wage
-60
-40
-20
020
0 4 8 12 16 20quarter
3 month Tbill rate
-.3
-.2
-.1
0.1
.2
0 4 8 12 16 20quarter
average marginal income tax rate
VAR with Shock Based on Professional Forecasters:1968-2008
(red lines: 68%; green lines: 95%)
-100
-50
050
0 4 8 12 16 20quarter
real baa bond rate
-.8
-.6
-.4
-.2
0.2
0 4 8 12 16 20quarter
nondurable consumption
-.4
-.2
0.2
0 4 8 12 16 20quarter
services consumption
-3-2
-10
1
0 4 8 12 16 20quarter
consumer durable purchases
-3-2
-10
1
0 4 8 12 16 20quarter
nonresidential investment
-6-4
-20
24
0 4 8 12 16 20quarter
residential investment
Conclusions Standard VAR methods appear to identify shocks
that are anticipated. Anticipation effects are very important from the
view point of a neoclassical model. Both theory and empirical evidence suggests that
difference in standard VAR results and Ramey-Shapiro dates is due to faulty timing of VAR shocks.
Bottom Line: An increase in (nonproductive)
government spending lowers consumption and has a multiplier of unity or less.