The Impact of Uncertainty Shocks Nick Bloom (Stanford & NBER) October 2008.

The Impact of Uncertainty Shocks

Nick Bloom (Stanford & NBER)

October 2008

10

20

30

40

50

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Monthly US stock market volatility

Note: CBOE VXO index of % implied volatility, on a hypothetical at the money S&P100 option 30 days to expiry, from 1986 to 2007. Pre 1986 the VXO index is unavailable, so actual monthly returns volatilities calculated as the monthly standard-deviation of the daily S&P500 index normalized to the same mean and variance as the VXO index when they overlap (1986-2004). Actual and implied volatility correlated at 0.874. The market was closed for 4 days after 9/11, with implied volatility levels for these 4 days interpolated using the European VX1 index, generating an average volatility of 58.2 for 9/11 until 9/14 inclusive.* For scaling purposes the monthly VOX was capped at 50. Un-capped values for the Black Monday peak are 58.2 and for the Credit Crunch peak are 64.4

OPEC II

Monetary turning point

Black Monday*

Gulf War I

Asian Crisis

Russia & LTCM

9/11Enron

Gulf War II

Implied VolatilityActual Volatility

Afghanistan

JFK assassinated

Cuban missile

crisis

Cambodia,Kent State

OPEC I

Franklin National

An

nu

aliz

ed s

tan

dar

d d

evia

tio

n (

%)

Vietnam build-up

Credit crunch*

Stock market volatility appears to proxy uncertainty

• Correlated with many other uncertainty proxies, for example with the cross-sectional spread of:

• Quarterly firm-level earnings-growth (corr = 0.536)• Monthly firm-level stock-returns (corr = 0.534)• Annual industry-level TFP growth (corr = 0.582)• Bi-annual GDP forecasts (corr = 0.618)

• Robust to including trend and period dummies (Table 1)

40

60

80

10

012

014

0

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010Year

Stock market volatility is also quite distinct from stock market levels (shown log-detrended below)

Note: S&P500 index from 1962 to 2008. Log de-trended by converting to logs, removing the time trend, and converting back into levels. The coefficient (s.e.) on days is 0.0019 (0.000038), implying a nominal average trend growth rate of 7.4% over the period.

Detrended stock market levels correlated with monthly volatility at -0.340

OPEC II

Monetary turning point

Black Monday

Gulf War I

Asian Crisis

Russia & LTCM

9/11

Enron

Gulf War II

Afghanistan

JFK assassinated

Cuban missile

crisis Cambodia,Kent State

OPEC IFranklin National

Vietnam build-up

Credit crunch

But do these uncertainty shocks matter empirically?

Want to look at the average impact of an uncertainty shock

Estimate a monthly orthogonal VAR:• log(S&P 500 level), uncertainty shocks, FFR, log(wages),

log(CPI), hours, log(employment), log(industrial production)

uncertainty shocks defined by a (1/0) indicator for the 16 shocks

10

20

30

40

50

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005ym

Bars denote the 16 uncertainty shocks in the VAR

Implied VolatilityActual Volatility

An

nu

aliz

ed s

tan

dar

d d

evia

tio

n (

%)

Shocks selected as those 2 SD above the HP filtered trend. VAR run on data until 2007 (so credit crunch not covered)

-1-.

50

.51

0 6 12 18 24 30 36year

-2-1

01

2

0 6 12 18 24 30 36year

VAR estimate of the impact of an uncertainty shock%

im

pac

t

Months after the shock

Response to 1% shock to the Federal Funds Rate

% i

mp

act


Response to an uncertainty shock

Industrial Production

Employment

Note: results robust to different variable inclusion, ordering & detrending (see appendix figures A1 to A3 ). Dotted lines are +/- one standard-error bands

Response to 1% shock to the Federal Funds Rate

Response to an uncertainty shock

2001 2002

9/11

Frequency of word “uncertain” in FOMC minutes

Policy makers also appeared to talk a lot more about uncertainty after one recent shock – 9/11

Source: [count of “uncertain”/count all words] in minutes posted on http://www.federalreserve.gov/fomc/previouscalendars.htm#2001

“The events of September 11 produced a marked increase in uncertainty ….depressing investment by fostering an increasingly widespread wait-and-see attitude about undertaking new investment expenditures”

FOMC* minutes, October 2nd 2001

*Federal Open Market Committee

And they appeared to believe uncertainty mattered

“Several [survey] participants reported that uncertainty about the economic outlook was leading firms to defer spending projects until prospects for economic activity became clearer”

FOMC minutes, 2008

Policymakers also worried about uncertainty from the credit crunch

Motivation

• Major shocks have 1st and 2nd moments effects

• VAR (and policymaker) evidence suggest both matter– Lots of work on 1st moment shocks– Less work on 2nd moment shocks

• Paper will try to model 2nd moment (uncertainty) shocks

– Closest work is probably Bernanke (1983)

Stage 1: Build and estimate structural model of the firm

• Standard model augmented with

– time varying uncertainty

– mix of labor and capital adjustment costs

Stage 2:

• Estimate on firm data by Simulated Method of Moments

Stage 3: Simulate stylized 2nd moment shock (micro to macro)

• Generates rapid drop & rebound in

– Hiring, investment & productivity growth

• Investigate robustness to a range of issues

Summary of the paper

Estimation

Model

Results

Shock Simulations

Base my model as much as possible on literatureInvestment• Firm: Guiso and Parigi (1999), Abel

and Eberly (1999) and Bloom, Bond and Van Reenen (2007), Ramey and Shapiro (2001), Chirinko (1993)

• Macro/Industry: Bertola and Caballero (1994) and Caballero and Engel (1999)

• Plant: Doms & Dunn (1993), Caballero, Engel & Haltiwanger (1995), Cooper, Haltiwanger & Power (1999)

Labour• Caballero, Engel & Haltiwanger

(1997), Hamermesh (1989), Davis & Haltiwanger (1992), Davis & Haltiwanger (1999),

Labour and Investment• Shapiro (1986), Hall (2004), Merz

and Yashiv (2004)

Real Options & Adjustment costs• Abel and Eberly (1994), Abel and

Eberly (1996), Caballero & Leahy (1996), and Eberly & Van Mieghem (1997), Bloom (2003)

• MacDonald and Siegel (1986), Pindyck (1988) and Dixit (1989)

Time varying uncertainty• Bernanke (1983), Hassler (1996),

Fernandez-Villaverde and Rubio-Ramirez (2006)

Simulation estimation• Cooper and Ejarque (2001), Cooper

and Haltiwanger (2003), and Cooper, Haltiwanger & Willis (2004)

Firm Model outline

Model has 3 main components

Net revenue function, R

Labor & capital “adjustment costs”, C

Stochastic processes, E[ ]

Firms problem = max E[ Σt(Rt–Ct) / (1+r)t ]

Revenue function (1)

Cobb-Douglas Production

A is productivity, K is capital

L is # workers, H is hours, α+β≤1

Constant-Elasticity Demand

B is the demand shifter

Gross Revenue

A is “business conditions” where A1-a-b=A(1-1/e)Ba=α(1-1/e), b=β(1-1/e)

)(~

HLKAQ

eBQP

baba HLKAPQ )(1 ~

~

Revenue function (2)

Firms can freely adjust hours but pay an over/under time premium

W1 and w2 chosen so hourly wage rate is lowest at a 40 hour week

)1()( 21HwwHwages

LHwwPQHLKAR )1(),,,( 21

Net Revenue = Gross Revenue - Wages

Allow for three types of adjustment costs (1)

Quadratic:

C(I,K) = αKK(I/K)2 where I=Gross investment, αK≥0

C(E,L) = αLL(E/L)2 where E=Gross hiring/firing, αL≥0

‘Partial irreversibility’:

C(I,K) = bI[I>0] + sI[I<0] where b≥s≥0

C(E,L) = hE[E>0] - fE[E<0] where h≥0, f≥0

Fixed costs:

C(I,K) = FCKPQ[I≠0] where FCK≥0

C(E,L) = FCLPQ[E≠0] where FCL≥0

“Adjustment costs” (2)

• Assume 1 period (month) time to build

• Exogenous labor attrition rate δL and capital depreciation rate δK

• Baseline δL=δK=10% (annualized value)

• Robustness with δK=10% and δL=20%

Stochastic processes – the “first moment”

“Business conditions” combines a macro and a firm random walk

)1( 11M

ttMt

Mt WσAA ),~N(W M

t 10The macro process is common to all firms

Fti

Mtti AAA ,,

The firm process is idiosyncratic

)1( ,1,1,,F

tittiFti

Fti WσAA ),~N(W F

ti 10,

Assumes firm & macro uncertainty move together (consistent with results on the 3rd slide and Table 1)

Stochastic processes – the “second moment”

},{ HLt σσσ

Uncertainty modelled for simplicity as a two state Markov chain

σH = 2×σL so high uncertainty twice the

‘baseline’ low value (from Figure 1)

σL σH

σL 35/36 1/36

σH 0.29 0.71

With the following monthly transition matrix

Defined so on average (from Figure 1):

• σH occurs once every 3 years

• σH has a 2 month half-life

The optimisation problem

Simplify by solving out 1 state and 1 control variable– Homogenous degree 1 in (A,K,L) so normalize by K– Hours are flexible so pre-optimize out

Value function

),),1)((),1)((,(1

1

),,,,,(),,,(max),,,,(,,

KL

HEI

IKELdAAVEr

EIHKLACHKLARKLAV

Simplified value function

),),1)((,(1

)1)(1(

),,,(~

),(~

max),,,(,

LK

ei

eldaaQEr

i

eilaClaRlaQ

Note: I is gross investment, E is gross hiring/firing and H is hours

Solving the model

• Analytical methods for broad characterisation:

– Unique value function exists

– Value function is strictly increasing and continuous in (A,K,L)

– Optimal hiring, investment & hours choices are a.e. unique

• Numerical methods for precise values for any parameter set

“Business Conditions”/Labor: Ln(A/L)

“Bu

sin

ess

Co

nd

itio

ns”

/Cap

ital

: L

n(A

/K)

Example hiring/firing and investment thresholds

InactionFire

Invest

Disinvest

Hire

High and low uncertainty thresholds

Low uncertainty

High uncertainty

Larger “Real option” values at higher uncertainty (≈7.5% rise in hurdle rate)

“Bu

sin

ess

Co

nd

itio

ns”

/Cap

ital

: L

n(A

/K)


6

4

2

0

8Distribution of units between the thresholds


Hir

ing

/Fir

ing

rat

e(s

olid

bla

ck li

ne)

Distribution of units

Distrib

utio

n o

f un

its(d

ashed

red lin

e)

Hiring region

Firing region

Inactionregion

Note: Plotted for low uncertainty, high drift and the most common capital/labor (K/L) ratio.

Taking the model to real micro data

• Model predicts many “lumps and bumps” in investment and hiring

• See this in truly micro data – i.e. GMC bus engine replacement

– But (partially) hidden in plant and firm data by cross-sectional and temporal aggregation

• Address this by building cross-sectional and temporal aggregation into the simulation to consistently estimate on real data

Including cross-sectional aggregation

• Assume firms owns large number of units (lines, plants or markets)

• Units demand process combines macro, firm and unit shock

where AF and AM are the firm and macro processes as before

• Simplifying assumptions following approach of Bertola & Caballero (1994), Caballero & Engel (1999), and Abel & Eberly (2002)

– Assume unit-level optimization (managers optimize own “P&L”)

– Links across units in same firm all due to common shocks

UFM AAAA

),~N(WWAA Ut

Utt

Ut

Ut 10 )1( 11

Including temporal aggregation

• Shocks and decisions typically at higher frequency than annually

• Limited survey evidence suggests monthly frequency most typical

• Model at monthly underlying frequency and aggregate up to yearly

Estimation

Model

Results

Shock Simulations

Estimation overview

• Need to estimate all 23 parameters in the model– 9 Revenue Function parameters

• production, elasticity, wage-functions, discount, depreciation and quit rates

– 6 “Adjustment Cost” parameters• labor and capital quadratic, partial irreversibility and fixed costs

– 8 Stochastic Process parameters• “demand conditions”, uncertainty and capital price process

• No closed form so use Simulated Method of Moments (SMM)– In principle could estimate every parameter– But computational power restricts SMM parameter space

• So (currently) estimate 10 key parameters & predefine the rest remaining 13 from the data and literature

Simulated Method of Moments estimation

• SMM minimizes distance between actual & simulated moments

• Efficient W is inverse of variance-covariance of (ΨA - ΨS (Θ))

• Lee & Ingram (1989) show under the null W= (Ω(1+1/κ))-1

– Ω is VCV of ΨA, bootstrap estimated

– κ simulated/actual data size, I use κ=25

)]([)]'([minˆ

SASA W

actual data

moments

simulated moments

weight matrix

The 13 pre-determined parametersParameter: Value: Source:

α (capital coefficient) 1/3 Capital share in output

e (demand elasticity) 4 33% mark-up (also try 20% mark-up)

w1 (wage parameter) 0.8 Hourly wage minimized at 40 hour week

w2 (wage parameter) 2.4e-9 Arbitrary scaling parameter

σH (uncertainty shock size) 2 Doubles baseline (also try 1.5 and 3)

πσL,H 1/36 Shock every 3-years

πσH,H 0.71 Shocks 2-month half-life (also try 1 & 6)

(μH+μL)/2 0.02 Average annual real growth rate of sales is 2% (gap between μH-μL is estimated)

πμL,H πμ

H,L Firm-growth matrix assumed symmetric (πμ

L,H is estimated)

δK (capital depreciation) 0.1 10% annualized capital depreciation

δL (labor quit rate) 0.1 For numerical speed (also try δL=0.2)

r (long-run discount rate) 6.5% Long run US average (King & Rebelo, 1999)

N (units per firm) 250 Chosen for complete aggregation (also try N=25 and N=1)

Data is firm-level from Compustat

• 20 year panel 1981 to 2000

• Large firms (>500 employees, mean 4,500)

– Focus on most aggregated firms

– Minimize entry and exit

• Final sample 2548 firms with 22,950 observations

Estimation

Model

Results

Shock Simulations

Estimation results (table 3)

•Top half shows the parameter estimates

• Bottom half shows sales, investment and hiring moments

Too much for 1 page so focus on adjustment cost only in main specification

Large capital resale loss & moderate fixed costs. No quadratic investment costs.

Moderate per person hiring/firing costs & large fixed costs. No quadratic hiring costs.

Adjustment cost estimates identified by:• skewed investment rates (no disinvestment)• moderate investment dynamics (some auto-correlation)• weak employment dynamics and wide cross-sectional spread

Results for estimations on restricted models

Capital “adjustment costs” only• Fit is moderately worse• Seems best approximation if using just one factor

Labor “adjustment costs” only• Labor moments fit are fine, Capital moments fit is bad• So OK for approximating labor data

Quadratic “adjustment costs” only• Poor overall fit (too little skew and too much dynamics)• But industry and aggregate data little/no skew and more

dynamics• So OK for approximating more aggregated data

No temporal or cross-sectional aggregation• Estimate much lower fixed costs and higher quadratic costs

Robustness

• Table 4 runs some robustness checks of the different predetermined parameter estimates

• Makes some difference, but broad findings and simulations appear reasonably robust

Estimation

Model

Results

Shock Simulations

Simulating 2nd moment uncertainty shocks

Simulate an economy with 1000 units

– Allow the model to run for 10 years

– Set σt=σH in month 1 of year 11

Repeat this 25,000 times and take the mean (to average over first-moment macro shocks)

Run the initial thought experiment of just a second moment shock

– Will add 1st moment shocks, but leave out initially for clarity

The second moment shock in the simulationU

nce

rtai

nty

(σ

t)A

vera

ge

σt

(no

rmal

ized

to

1 o

n p

re-

sho

ck d

ate)

Month (normalized to 0 for month of shock)

The simulation has no first moment shock


Un

cert

ain

ty (

σt)

Ag

gre

gat

e A

t (b

usi

nes

s co

nd

itio

ns)

(no

rmal

ized

to

1 o

n s

ho

ck d

ate)

Actual

De-trended

Aggregate labor drops, rebounds and overshoots


Ag

gre

gat

e L

t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

_sh

ock

dat

e)

Splitting out the uncertainty and volatility effects


Ag

gre

gat

e L

t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

_sh

ock

dat

e)

Baseline (both effects)

‘Volatility effect’ only

‘Uncertainty effect’ only

6

4

2

0

8Distribution of units [slide copied from earlier]


Hir

ing

/Fir

ing

rat

e(s

olid

bla

ck li

ne)

Distribution of units

Distrib

utio

n o

f un

its(d

ashed

red lin

e)

Hiring region

Firing region

Inactionregion

Notes: The hiring response and unit-level density for low uncertainty (σL), high-drift (μH) and the most common capital/labor (K/L) ratio.

Aggregate capital drops, rebounds and overshoots


Ave

rag

e K

t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

Aggregate TFP growth also slows and reboundsT

FP

gro

wth

(%

)

(TF

Pt+

1-T

FP

t)/T

FP

t Total

Reallocation

Within

Hir

ing

/Fir

ing

rat

e

Hir

ing

/Fir

ing

rat

e

Log(Ai,t/Li,t)

Month before the shock Month after the shock

Definition: TFPt = ∑Li,tAi,t / ∑Li,t

Log(Ai,t/Li,t)

So de-trended TFP levels drop, rebound & overshoot


So

low

TF

Pt

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)Solow TFPt = Aggregate Output/Factor Share Weighted Inputs

Output also drops and rebounds


Ave

rag

e O

utp

ut

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e) Matches up well to the VAR estimates for industrial production:

• Six-month U-shaped drop in activity

• Lowest point about 2% below trend

• Longer-run overshoots

Interestingly, looks like 1st moment shock

Robustness - General Equilibrium effects

• Could run GE approximating the cross-sectional distribution of firms (i.e. Kahn and Thomas, 2003)

– But need another program loop, so much slower – so choice:(i) estimating ACs (in PE), or

(ii) doing GE (with calibrated ACs)

– Estimated ACs first and do full GE later (in work with Max Floetotto and Nir Jaimovich)

• But, can get a first indication of the likely short-run impact of GE by feeding in prices after uncertainty shocks estimated using VAR

-1-.

50

.5

0 6 12 18 24 30 36year

% i

mp

act

Federal Funds rate(% points change)


CPI (% change)

VAR estimated impact of an uncertainty shock on prices

Wages (% change)

Approximate this in the simulation by assuming that when σt=σH

– Interest rates 1.1% lower

– Prices of capital and output 0.5% lower

– Wages 0.3% lower

Firms expect this since incorporated into the model

Certainly not exact! Simply guidance on possible GE effect

‘Pseudo GE’ effects have little very short-run impact


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

Pseudo GE

Partial Equilibrium

GE impact initially small due to ‘cautionary’ effect of uncertainty

• Thresholds move out with high σt, so not responsive

• As σt falls back down GE effects have more bite

Also suggests limited very short-run response to policy stimulus after shocks

Finish with some other robustness experiments

• Combined 1st and 2nd moment shocks

• Different predetermined parameters

• Different assumptions on adjustment costs

• Different sizes of uncertainty shocks

• Different durations of uncertainty shocks

Adding first moment shocks


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

First and second moment shock

Second moment shock only

First moment shock only

Different predetermined parameters


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

20% labor attrition

N=1

N=25 20% markup

Different types of adjustment costs


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

Partial irreversibilities only

Quadratic only

Fixed costs only

Different sizes of uncertainty shocks


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

Larger (σH=3×σL)

Baseline (σH=2×σL)

Smaller (σH=1.5×σL)

Different durations of uncertainty shocks


Ave

rag

e O

utp

ut t

(de-

tren

ded

& n

orm

aliz

ed t

o 1

on

pre

-sh

ock

dat

e)

Longer live(6 month half-life)

Baseline(2 month half-life)

Shorter lived(1 month half-life)

A FINAL HISTORICAL DIGRESSION(not really part of the paper)

030

60

90

1880 1890 1900 1910 1920 1930 1940 1950 1960Year

9/11

The Great Depression was notable for very high volatility

Note: Volatility of the daily returns index from “Indexes of United States Stock Prices from 1802 to 1987” by Schwert (1990). Contains daily stock returns to the Dow Jones composite portfolio from 1885 to 1927, and to the Standard and Poor’s composite portfolio from 1928 to 1962. Figures plots monthly returns volatilities calculated as the monthly standard-deviation of the daily index, with a mean and variance normalisation for comparability following exactly the same procedure as for the actual volatility data from 1962 to 1985 in figure 1.

The Great Depression

Recession of 1937

Oil & coal strike

Banking panic

Did uncertainty play a role in the Great Depression?

• Romer (1990) suggests uncertainty played a role in the initial 1929-1930 slump, which was propagated by the 1931 banking collapse

“during the last few weeks almost everyone held his plans in abeyance and waited for the horizon to clear”, Moody’s 12/16/1929

• In the model a GD sized persistent increase in uncertainty would also generate persistently slower productivity growth

• TFP “inexplicably” fell by 18% from 1929-33 (Ohanian, 2001)• Output “oddly” not shifted to low-cost firms (Bresnahan &

Raff, 1991)

END OF DIGRESSION

Conclusions

• Uncertainty appears to spike after major economic & political shocks

• VAR estimation suggest these cause a rapid drop and rebound in output and employment

• Estimation and simulation predicts a similar rapid drop & rebound

• Building a GE model with 1st and 2nd moment shocks, non-convex adjustment costs & many plants (with Jaimovich and Floetotto)– Motivation that all uncertainty proxies rise strongly in recessions– So possible that counter-cyclical uncertainty can address the

“where are the negative shocks?” critique of real-business cycles

Next steps

BACK-UP

Looks like the FOMC did the right thing after 9/11

• Pumped in liquidity to reduce uncertainty

• Did not cut interest rates much

– Cut Federal Funds Rates by 1.75%, but this was already predicted to fall by about 1.3% pre-9/11

Congress on the other hand was not so perfect…• “A key uncertainty in the outlook for investment spending was the

outcome of the ongoing Congressional debate relating to tax incentives for investment in equipment and software. Both the passage and the specific contents of such legislation remained in question”FOMC Minutes, November 6th 2001

THE 9/11 POLICY VERDICT

Robustness- general equilibrium effects

• Thomas (2002) and Veracierto (2002) suggest GE important

– In particular they find under GE

Mt is a BC variable like labor, or capital

Yt is aggregate productivity/demand

NC is some non-convex cost

– But I look at

σt is uncertainty

• So correctly highlight importance of GE, but on a different issue

t

t

d

dM

0

)(

dNC

dY

dMd

t

t

Also need to deal with aggregation

% annual zero investment episodes (UK Firm and Plant data)

Quarterly Yearly

Sales 6.78 2.97

Investment 1.18 0.84

standard deviation/mean of growth rates (US firm data)

Structures Equipment Vehicles Total

Firms 5.9 0.1 n.a. 0.1

Establishments 46.8 3.2 21.2 1.8

Single plants 53.0 4.3 23.6 2.4

Small single plants 57.6 5.6 24.4 3.2

Ag

greg

ation

across u

nits

Aggregation across time

Aggregation across lines of capital

-3-2

-10

1

0 6 12 18 24 30 36year

-1.5

-1-.

50

.51

0 6 12 18 24 30 36year

VAR robustness of industrial production plots%

im

pac

t


% i

mp

act


Shock definitions

Variables & ordering

Terror, War and Oil shocks only

Actual volatility series

Shocks dated by first month

Shocks scaled by actual volatility

Trivariate (shocks, employment & production)

Bivariate (shocks and production)

Reverse trivariate (production, employment & shocks)

-2-1

01

2

0 6 12 18 24 30 36year

-1-.

50

.51

1.5

0 6 12 18 24 30 36year

VAR robustness of industrial production plots%

im

pac

t


Detrending

Monthly HP (HP=129,600)

High frequency (HP=1296)

Baseline (no detrending)

% i

mp

act


Oil, credit spread and yield curve

Baseline

Baseline plus oil prices

Baseline plus Moody Aaa and Baa rates

Linear (HP=∞)

“The events of September 11 produced a marked increase in uncertainty ….depressing investment by fostering an increasingly widespread wait-and-see attitude about undertaking new investment expenditures”

FOMC minutes, October 2nd 2001

And they appeared to believe uncertainty mattered

“Financial market conditions have deteriorated, and tighter credit conditions and increased uncertainty have the potential to restrain economic growth going forward. ”

FOMC statement, August 17th 2007

As with the recent sub-prime shock

020

4060

80vo

l

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008ymd

Gulf War I

Asian Crisis

Russian & LTCMDefault 9/11

WorldCom & Enron

Gulf War II

Imp

lie

d V

ola

tili

ty o

n t

he

S&

P 1

00

(%

)Credit Crunch: A Plot of Daily Stock Market Volatility

Year

Credit Crunch

Updated October 27th

The Impact of Uncertainty Shocks Nick Bloom (Stanford & NBER) October 2008.

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Transcript of The Impact of Uncertainty Shocks Nick Bloom (Stanford & NBER) October 2008.