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
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
Months after the shock
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)
“Business Conditions”/Labor: Ln(A/L)
6
4
2
0
8Distribution of units between the thresholds
“Business Conditions”/Labor: Ln(A/L)
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
Month (normalized to 0 for month of 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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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]
“Business Conditions”/Labor: Ln(A/L)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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)
Months after the shock
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Month (normalized to 0 for month of shock)
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
Months after the shock
% i
mp
act
Months after the shock
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
Months after the shock
Detrending
Monthly HP (HP=129,600)
High frequency (HP=1296)
Baseline (no detrending)
% i
mp
act
Months after the shock
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