Policy makers believe that uncertainty matters, 1/4

Really Uncertain Business Cycles

Nick Bloom (Stanford, CEP & NBER)Max Floetotto (Stanford)Nir Jaimovich (Stanford & NBER) Very Preliminary

Kellogg, May 19th 2009

Policy makers believe that uncertainty matters, 1/4

FOMC (April 2008)“Several participants reported that uncertainty about the economic outlook was leading firms to defer spending projects until prospects for economic activity became clearer.”


Olivier Blanchard (January 2009)"Crises feed uncertainty. And uncertainty affects behavior, which feeds the crisis. Were a magic wand to remove uncertainty, the next few quarters would still be tough, but the crisis would largely go away.”


Larry Summers (March 2009)“…unresolved uncertainty can be a major inhibitor of investment. If energy prices will trend higher, you invest one way; if energy prices will be lower, you invest a different way. But if you don’t know what prices will do, often you do not invest at all.”


Christina Romer (April 2009)“Volatility has been over five times as high over the past six months as it was in the first half of 2007. The resulting uncertainty has almost surely contributed to a decline in spending.”

We model uncertainty as a new type of shock

First moment shocks in the literature● Neutral technology shocks● Investment-specific technology shocks● Oil price shocks● Labor supply shocks● Monetary policy shocks● Financial shocks● News shocks

We want to consider a second moment (uncertainty) shock● For simplicity focus on technology shocks

The paper examines empirical evidence and a model on uncertainty and the business cycle

Paper has three main parts:

● Empirical evidence that uncertainty is counter-cyclical

● DSGE model of the impact of time varying uncertainty:

● Uncertainty shocks lead to business cycles

● Uncertainty substantially reduces the impact of policy

● Examine Census micro-data, to investigate further predictions we get from the model for the impact of uncertainty

What this paper does not (currently) do

● Attempt to endogenize uncertainty

● Modeled as exogenous, like first moment shocks

● If uncertainty is endogenous could think of as a propagation and amplification mechanism

● Include/analyze other potentially important uncertainty channels:

● Consumer durables

● Credit

● Risk

Measuring Uncertainty

Model

Testing the model on Census micro data

Uncertainty over the business cycle

● Uncertainty is hard to measure and the concept is vague

● Build on prior literature and use different types of proxies:

● Cross-industry, firm and plant evidence

● Time-series aggregate data

● Cross-forecaster disagreement evidence

● Combine these into an aggregate uncertainty index

● This reduced index rises by 48% during recessions

.01

.02

.03

.04

.05

.06

1970 1980 1990 2000 2010Year

Cross industry output growth spread

Inter-quartile range of the 3-month growth rates of industrial production. Covers all 196 manufacturing NAICS sectors in the Federal Reserve Board database.

until 2009 Q1

-.4

-.2

0.2

1970 1980 1990 2000 2010Year

1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th and 99th percentiles of 3-month growth rates of industrial production within each quarter. All 196 manufacturing NAICS sectors in the Federal Reserve Board database.

99th percentile,2.2% higher in recessions

1st percentile7.4% lower in recessions

50th percentile,1.3% lower in recessions

Cross industry output growth distribution

.2.3

.4.5

1970 1980 1990 2000 2010Year

Cross firm sales growth spread

Interquartile range of sales growth (Compustat firms). Only firms with 25+ years of accounts, and quarters with 500+ observations. SIC2 only cells with 25+ obs.

Across all firms(+ symbol)

Across firms in a SIC2 industry

until 2008 Q2

.05

.1.1

5.2

1970 1980 1990 2000 2010Year

Cross firm stock returns spread

Interquartile range of stock returns (CRSP firms). Only firms with 25+ years of accounts, and quarters with 500+ observations. SIC2 only cells with 25+ obs.

Across all firms(+ symbol)

Across firms in a SIC2 industry

until 2008 Q4

Cross establishment sales & labor productivity growth spread

ASM data on 60,000 manufacturing establishments 1974-2006

Found two stylized facts:

• Cross-sectional spreads strongly counter cyclical

• Increase both overall and within SIC 4-digit category

.005

.01

.015

.02

.025

For

eca

st s

tan

dard

-dev

iatio

n, fr

om

AR

CH

mo

del

1965 1970 1975 1980 1985 1990 1995 2000 2005Year

Industrial production growth volatility

Monthly industrial production conditional heteroskedasticity, from a GARCH(1,1) auto-regression with 12 lags.

until 2009 Q1

1020

3040

5060

1965 1970 1975 1980 1985 1990 1995 2000 2005Year

Stock market volatility

S&P 100 implied volatility (the VXO, which is very similar to VIX) from 1987, and normalized realized volatility of actual S&P100 daily stock returns prior to 1986.

until 2009 Q2

0.0

5.1

.15

.2IQ

R o

f une

mp

gro

wth

rat

e fo

reca

st

1970 1975 1980 1985 1990 1995 2000 2005Year

Forecaster dispersion for unemployment

Interquartile range of year ahead unemployment rates / mean unemployment rates. From Survey of Professional Forecasters. Average of 41 forecasts per quarter.

until 2009 Q2

0.0

2.0

4.0

6

1970 1975 1980 1985 1990 1995 2000 2005Year

Forecaster dispersion for productionuntil 2009 Q2

Interquartile range of year ahead production/mean production. From Survey of Professional Forecasters. Average of 41 forecasts per quarter.

.51

1.5

22.

5

1965 1970 1975 1980 1985 1990 1995 2000 2005Year

Uncertainty index – average of last 7 measuresuntil 2009 Q1

Mean of the 7 prior indicators after they have all been normalized to an average of 1 during non-recessionary quarters. Only reported when 5+ indicators present.

1968:41969:11969:2

1969:31969:4

1970:1

1970:2

1970:31970:4

1971:11971:2 1971:3 1971:4

1972:11972:2 1972:31972:41973:1

1973:2

1973:3

1973:4

1974:11974:2

1974:3

1974:4

1975:1

1975:2

1975:31975:4

1976:11976:21976:31976:4

1977:11977:21977:31977:4

1978:11978:21978:3

1978:41979:1

1979:2

1979:31979:4

1980:11980:2

1980:31980:4

1981:1

1981:2

1981:3

1981:41982:1

1982:2

1982:3

1982:4

1983:1

1983:21983:3

1983:41984:11984:2

1984:31984:4

1985:11985:2

1985:3

1985:4

1986:11986:2

1986:31986:41987:1

1987:2

1987:3

1987:41988:1

1988:2

1988:31988:41989:1

1989:21989:3

1989:41990:11990:2

1990:31990:4

1991:1

1991:21991:3

1991:4 1992:1

1992:21992:31992:41993:11993:2

1993:31993:4

1994:11994:2

1994:31994:41995:11995:21995:31995:4

1996:11996:21996:31996:41997:11997:21997:3

1997:41998:1

1998:21998:3

1998:41999:11999:21999:31999:42000:1

2000:2

2000:3

2000:4

2001:12001:22001:3

2001:4

2002:12002:22002:32002:4

2003:12003:22003:3

2003:42004:1

2004:22004:3

2004:42005:12005:2

2005:3

2005:42006:12006:22006:32006:42007:12007:2

2007:32007:4

2008:12008:2

2008:3

2008:4

2009:1

.51

1.5

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-10 -5 0 5Quarterly industrial production growth (annualized in %)

Uncertainty index and industrial production growth

Are recessions also conditionally associated with recessions?

● So far only shown unconditional correlation between recessions and uncertainty

● Use VAR analysis to investigate the conditional correlation of uncertainty with a recession, noting this does not imply causality

VAR analysisUse standard VAR framework from Christiano, Eichenbaum and Evans (2005) that includes the following variables (in order):

● Real GDP (logs)● Real consumption (logs)● GDP deflator (logs)● Real investment (logs)● Real wage (logs)● Labor productivity (logs)● Federal Funds rate● Real profits (logs)● Growth rate of M2

Add aggregate uncertainty index, but ● Check robustness to change in ordering (first, last)● TFP to control for first moment shock (Basu, Kimball & Fernald)

VAR analysis – uncertainty firstShock calibrated to increase uncertainty 48% during recessions

Cholesky orthogonalized on quarterly data from 1968:4 to 2006:4 using 4 lags. Dotted lines are 95% confidence intervals

VAR analysis – different experiments

Cholesky orthogonalized on quarterly data from 1968:4 to 2006:4 using 4 lags. Dotted lines are 95% confidence intervals

Shock calibrated to increase uncertainty 48% during recessions

Results for Germany (from Frank Smets)

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of UNCERTAINTY to UNCERTAINTY

-.010

-.005

.000

.005

.010

.015

1 2 3 4 5 6 7 8 9 10

Response of TFP to UNCERTAINTY

-0.8

-0.4

0.0

0.4

0.8

1.2

1 2 3 4 5 6 7 8 9 10

Response of IR3M to UNCERTAINTY

-.8

-.4

.0

.4

1 2 3 4 5 6 7 8 9 10

Response of CPI to UNCERTAINTY

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of WAGE to UNCERTAINTY

-.02

-.01

.00

.01

.02

1 2 3 4 5 6 7 8 9 10

Response of GDP to UNCERTAINTY

Response to Cholesky One S.D. Innovations ± 2 S.E.

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of UNCERTAINTY to UNCERTAINTY

-.010

-.005

.000

.005

.010

.015

1 2 3 4 5 6 7 8 9 10

Response of TFP to UNCERTAINTY

-0.8

-0.4

0.0

0.4

0.8

1.2

1 2 3 4 5 6 7 8 9 10

Response of IR3M to UNCERTAINTY

-.8

-.4

.0

.4

1 2 3 4 5 6 7 8 9 10

Response of CPI to UNCERTAINTY

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of WAGE to UNCERTAINTY

-.02

-.01

.00

.01

.02

1 2 3 4 5 6 7 8 9 10

Response of GDP to UNCERTAINTY

Response to Cholesky One S.D. Innovations ± 2 S.E.

Impact of a one SD impulse in uncertainty. Prepared by creating a German Uncertainty Index over 10 years and running the same VAR specification.

Results for US consumption (from Mark Doms)

Source: Mark Doms (SF Federal Reserve Board), figure used for Board of Governors briefing work

Taking stock

● Uncertainty - however measured - is strongly countercyclical

● An increase in uncertainty robustly associated with a significant drop and rebound in output in a VAR framework

● Well known identification problems in VAR, so results are only suggestive

● Model allows us to study a possible mechanism further and provides additional micro-predictions to test in Census data


Model


Model conforms as much as possible to the standard frictionless RBC

● Main deviations are:

● Second moment shocks

● Non-convex adjustment costs in both capital and labor

● Firm-level heterogeneity

Mechanism is linked to Ss investment thresholds arising from non-convex adjustment costs

Disinvest Invest

Productivity / Capital

Den

sity

of

un

its

Disinvest Invest

Productivity / Capital

Den

sity

of

un

its

Mechanism is linked to Ss investment thresholds arising from non-convex adjustment costs

Technology

● Large number of heterogeneous firms

● “Productivity” follows an AR process with time variation in the variance of innovations

● Uncertainty (σA and σZ) follow a 2-point markov chain

Capital and labor adjustment costs● Capital and labor follow the laws of motion:

where i: investment δk: depreciation

s: hiring δn: attrition

● Allow for the full range of adjustment costs

● Fixed – lump sum cost for investment and/or hiring

● Partial – per $ disinvestment and/or per worker hired/fired

● Quadratic – to invest/disinvest and/or hire/fire more rapidly

● To match micro data paid on all investment and hiring (even replacement investment and hiring)

Firm’s value function

Households

● Representative agent who works, consumes and owns the firms

● We assume the functional form for household utility

● Separability of preferences yields a simple SDF:

● The FOC for hours worked

General equilibrium solution overview

● We have a recursive competitive equilibrium

● Solve numerically as no analytical solution

● Numerical solution approximates μ (the firm-level distribution over z, k and n) with moments, building on Krusell and Smith (1998)

● Follow Kahn and Thomas (2008) and Bachman, Caballero and Engel (2008) in using two tricks to simplify the numerical solution

Simplifying the problem

Calibration

Calibration of the uncertainty process

Simulation of a shock to uncertainty

0%

20%

40%

60%

80%

100%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter

Share of economies in high uncertainty state (in 1000 simulations)

Results not driven by a first moment shock

Average firm times macro productivity in the simulation

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter

The effect of an increase in uncertainty on employment: 3 phases

-3.0%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter

Drop Rebound

Overshoot

Deviation from steady state (%)

The effect of an increase in uncertainty on investment

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter


The effect of an increase in uncertainty on output

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter


The effect of uncertainty on measured TFP

-0.8%

-0.6%

-0.4%

-0.2%

0.0%

0.2%

0.4%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter

Deviation from steady state (%)Measured TFP = output/(capitalαlaborν)

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

-4 -2 0 2 4 6 8 10 12 14 16

Quarter

Bad fit? The effect of uncertainty on consumption


0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

-4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

Quarter

Investment threshold

Disinvestment threshold

90th

10th

50th

Cross-sectional distribution of firm TFP/capital

Thresholds & percentiles of firm distribution over z/k (for fixed k & l)

Uncertainty alters the impact of policy

● Uncertainty widens firms’ Ss bands for investment and hiring, thereby reducing the impact response of any given stimulus

● But, once uncertainty falls firms will start to respond again

Illustrate with an investment credit from Mars

● Example of a 1% investment credit from Mars for 3 quarters

● From Mars so not GE (much simpler to model)

● Again for simplicity assume it’s a complete surprise to agents – they just find investment is 1% cheaper for 3 quarters

● Evaluate during a normal period and after an uncertainty shock

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

-2 0 2 4 6 8 10 12

Quarter

Impact of the 1% investment tax credit

(2) Low uncertainty + investment credit

(1) Low uncertainty

(4) Uncertainty shock + investment credit

(3) Uncertainty shock

Quarter

Out

put

(% d

evia

tion

from

low

-unc

erta

inty

sta

te)

-0.1%

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

-2 0 2 4 6 8 10 12

Quarter

Uncertainty reduces and delays the impact

(2) – (1): Investment credit impact in low uncertainty

(4) – (3): Investment credit after uncertainty shock

Quarter

Out

put

(% d

evia

tion

from

low

-unc

erta

inty

sta

te)

Implications for policy impact of an uncertainty shock

● Suggests that stabilization policy to address the impact of an uncertainty shock would ideally be relatively:

● Rapid – to minimize creating additional policy uncertainty

● Large – you need a big stimulus given low responsiveness

● Temporary – want to avoid overshoot once uncertainty falls


Model


Reduced response in periods of high uncertainty

High uncertainty (recession)

Low uncertainty (boom)

ΔZ / Z

ΔL

/ L

Use micro data to test differences in establishment response to

TFP during periods of low and high uncertainty

Conclusions and next steps

● Uncertainty appears strongly counter cyclical

● Realistically calibrated DSGE model shows:

● Uncertainty can lead to moderate business cycle fluctuations in output, investment, hiring and TFP growth

● Suggests micro rigidities are important

● Policy impact different at high uncertainty

● Next steps to:

● Improve numerical simulations and run parameter tests

● Develop policy in presence of uncertainty

● Investigate model predictions in micro data

BACKUP

Sketch of the numerical solution

Increase in uncertainty during a recession

Uncertainty index and GDP growth

1968:41969:11969:2

1969:31969:4

1970:1

1970:21970:3

1970:4

1971:11971:21971:31971:4

1972:11972:21972:3 1972:4 1973:1

1973:2

1973:3

1973:4

1974:1 1974:2

1974:3

1974:4

1975:1

1975:2

1975:31975:4

1976:11976:21976:31976:4

1977:11977:21977:31977:4

1978:11978:2

1978:3

1978:41979:1

1979:2

1979:31979:4

1980:11980:2

1980:31980:4

1981:1

1981:2

1981:3

1981:41982:1

1982:2

1982:3

1982:4

1983:1

1983:21983:3

1983:41984:11984:2

1984:31984:4

1985:11985:2

1985:3

1985:4

1986:11986:2

1986:31986:41987:1

1987:2

1987:3

1987:41988:1

1988:2

1988:3 1988:41989:1

1989:21989:3

1989:4 1990:11990:2

1990:31990:4

1991:1

1991:21991:31991:4 1992:1

1992:21992:31992:41993:11993:2

1993:31993:4

1994:11994:2

1994:3 1994:41995:11995:2 1995:31995:4

1996:11996:21996:31996:41997:1 1997:21997:3

1997:41998:1

1998:21998:3

1998:41999:11999:21999:3 1999:42000:1

2000:2

2000:3

2000:4

2001:12001:22001:3

2001:4

2002:12002:22002:32002:4

2003:12003:2 2003:3

2003:42004:1

2004:22004:3

2004:42005:12005:2

2005:3

2005:4 2006:12006:22006:32006:42007:1 2007:2

2007:32007:4

2008:12008:2

2008:3

2008:4

2009:1

.51

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-10 0 10 20dgdp


Yoda (May 2009)“Uncertainty is the path to the dark side. Uncertainty leads to anger. Anger leads to hate. Hate leads to suffering.”

Tobin’s Q spread

Documents

Policy makers believe that uncertainty matters, 1/4