The Impact of Uncertainty Shocks:Firm-Level Estimation and a 9/11 Simulation
Nick Bloom (Stanford & NBER)
April 2007
010
20
30
40
50
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Year
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 2004. 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 affecting the Black Monday month. Un-capped value for the Black Monday month is 58.2.
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
Stock market volatility appears to proxy uncertainty
• Political uncertainty correlated with stock market volatility(Mei & Guo 2002, Voth 2002, Wolfers and Zitewitz, 2006)
• Professional forecaster spread over GDP growth correlated 0.437 with stock market volatility (bi-annual, Livingstone)
• Cross-sectional industry TFP growth spread correlated 0.429 with stock market volatility (annual, NBER)
• Common factor of exchange rate, oil price and interest rate volatility correlated 0.423 with stock market vol. (monthly)
Monthly stock market levels
Note: S&P500 monthly index from 1986 to 1962. Real de-trended by deflating by monthly “All urban consumers” price index, converting to logs, removing the time trend, and converting back into levels. The coefficient (s.e.) on years is 0.070 (0.002), implying a real average trend growth rate of 7.0% over the period.
050
100
150
Real
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Year
OPEC II
Monetary cycle turning point
Black Monday3
Gulf War I
Asian Crisis
Russian & LTCMDefault
September 114
WorldCom & Enron
Gulf War II
Afghanistan
JFK assassinated
Cuban missile
crisis
Cambodia, Kent State
Vietnam build up
OPEC I, Arab-Israeli War
Franklin National financial crisis
2001 2002
9/11
Frequency of word “uncertain” in FOMC minutes
The FOMC discussed uncertainty a lot after 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
The FOMC also believed uncertainty mattered
“Because the attack significantly heightened uncertainty it appears that some households and some business would enter a wait-and-see mode….They are putting capital spending plans on hold”
FOMC member Michael Moskow, November 27th
Motivation
• Major shocks have 1st and 2nd moments effects
• Policymakers believe both matter – is this right?– Lots of work on 1st moment shocks– Much less work on 2nd moment shocks
• Closest work probably Bernanke (1983, QJE)– Predicts wave like effect of uncertainty flucatuations
• I confirm, quantify & estimate this work
Stage 1: Build and estimate structural model of the firm• Standard model augmented with
– time varying uncertainty– mix of labor and capital adjustment costs
• Estimate on firm data by Simulated Method of Moments
Stage 2: Simulate stylized 2nd moment shock (micro to macro)• Generates rapid drop & rebound in
– Hiring, investment & productivity growth• Confirm robustness to GE, risk-aversion, and AC estimates
Stage 3: Compare to empirical evidence, and show reasonable fit• VAR results show volatility shocks cause a rapid drop and
rebound in output (and employment)• 9/11 event study shows drop & rebound against expectations,
plus a drop and rebound in cross-sectional investment activity
Summary of the paper
Time out…
Two things that I tried to do:• Start with some kind of big picture, and also use a graph
• Provide a summary of where I am going and what the results will be
This risks are this is quite long – sometimes this can take while to talk through. If lots of early questions come up take some of them but also be discplined and simply move on
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 ]
Time out…
I put the previous slide in just to settle people down – it is obvious to most people (hence need to be fast) but useful as a guide.
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
Y is “demand conditions”, where
Y1-a-b=A(1-1/e)Ba=α(1-1/e), b=β(1-1/e)
)( HLAKQ
eBQP /1
baba HLKYPQ )(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
LHwwPQHLKYR )1(),,,( 21
Net Revenue = Gross Revenue - Wages
“Adjustment costs” (1)
Active literature with range of approaches, e.g.
Look at convex & non-convex adjustment costs for both labor and capital
Labor or capital Labor and Capital
Convex1 Traditional Euler and Tobin’s Q models
Shapiro (1986); Hall (2004), Merz and Yashiv (2003)
Convex1 and Non-Convex2
Abel & Eberly (1999); Cooper & Haltiwanger (2003); Cooper, Haltiwanger and Willis (2004)
1 Convex typically quadratic adjustment costs2 Non-convex typically fixed cost or partial irreversibility
Time out…
The prior slide is controversial in some places (there is a lot of work in this area and not everyone agrees). So in advance of any important presentation:
1) Workout who will be your audience. Spend time looking at each persons page on the web-site – for a typical seminar this takes me about 3 or 4 hours (and I will already know some of the people as well)
2) Use this to make sure your presentation is correctly styled
“Adjustment costs” (2)
• 1 period (month) time to build
• Exogenous labor attrition rate δLand capital depreciation rate δK
• Relative capital price is AR(1) stochastic
tp
Kt
K
p
Kt
Kt Uσ)p(pρpp KK 1
*1 ),~N(U t 10
Stochastic processes – the “first moment”
“Demand conditions” combines a macro and a firm random walk
)1(1Mtt
Mt
Mt WσμYY ),~N(W M
t 10
1st MOMENT SHOCK
The macro process is common to all firms
Fti
Mtti YYY ,,
The firm process is idiosyncratic
)1( ,1,,Ftit
FFti
Fti WσYY ),~N(W F
ti 10,
Assumes firm and macro uncertainty move together - consistent with the data for large shocks (i.e. Campbell et al. 2001)
Stochastic processes – the “second moment”
2nd MOMENT SHOCK
tσZtσtt SσZσ)σ(σρσσ 1*
1
}10{ ,~St
Uncertainty is AR(1) process with infrequent jumps
• σσ=σ* so shocks roughly double average σ2t (note σZ is much smaller)
• Prob(St=1) is 1/60, so one shock expected every 5 years
)1,0(~NZ t
Time out…
Be animated when explaining your work
Also be enthusiastic – if you are not no-one else will be!
Never self criticise your work – for example say (“this is very boring, only a nerd would do this” etc..)
The optimisation problem is tough
Simplify by solving out 1 state and 1 control variable– Homogenous degree 1 in (Y,K,L) so normalize by K– Hours are flexible so pre-optimize out
Value function
),),1)((),1)((,(1
1
),,,,,,(),,,(max),,,,(,,
KKL
K
HEI
K
pIKELdYYVEr
pEIHKLYCHKLYRpKLYV
Simplified value function
),),1)((,(1
)1)(1(
),,,,(~
),(~
max),,,(,
KL
K
K
ei
K
peldyyQEr
i
peilyClyRplyQ
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 (Y,K,L)
– Optimal hiring, investment & hours choices are a.e. unique
• Numerical methods for precise values for any parameter set
“Demand Conditions”/Labor: Ln(Y/L)
“Dem
and
Co
nd
itio
ns”
/Cap
ital
: L
n(Y
/K)
Example hiring/firing and investment thresholds
InactionFire
Invest
Disinvest
Hire
“Real options” type effects
High and low uncertainty thresholds
Low uncertainty
High uncertainty
Larger “real options” at higher uncertainty
“Demand Conditions”/Labor: Ln(Y/L)
“Dem
and
Co
nd
itio
ns”
/Cap
ital
: L
n(Y
/K)
Time out…
Figures work well – these graphs are always much nicer to present then the theory and help get the message across
Be creative in preparing your presentation and try to think how you can graphically display any complex results
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 YF and YM are the firm and macro processes as before
ΦU is relative unit uncertainty
• Simplifying to solve following broad approach of Bertola & Caballero (1994), Caballero & Engel (1999), and Abel & Eberly (1999)
– Assume unit-level optimization (managers optimize own “P&L”)
– Links across units in same firm all due to common shocks
UFM YYYY
),~N(UUYY tttUU
tUt 10 )1(1
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 overview
• Need to estimate all 20 parameters in the model– 8 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
– 6 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 6 adjustment cost parameters & pre-determine the rest 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=10
)]([)]'([minˆ
SASA W
actual data
moments
simulated moments
weight matrix
Pre-determined parametersParameter: Value: Source:
α (capital coefficient) 1/3 Prod function estimation
β (labor coefficient) 2/3 Prod function estimation
δK (capital depreciation) 10% Depreciation estimates
δL (labor quit rate) 10% Matched to capital
w1 (wage parameter) 1/3 10 employees per unit
w2 (wage parameter) 7e-06 40 hour working week
γ (wage parameter) 2.5 Overtime share 27%
μ (demand drift) 5% Compustat average growth
ε (demand elasticity) -3 50% mark-up
pk* (capital price process) 1 Normalized to unity
ρpk (capital price process) 0.12 NBER 4-digit industry data
σpk (capital price process) 0.27 NBER 4-digit industry data
σ* (uncertainty process) 0.29 Firm level share returns vol
σσ (uncertainty process) 0.29 Macro shock doubles σt
ρσ (uncertainty process) 0.42 1.5 month shock half-life
θF (uncertainty process) 1.13 Firm/macro returns vol
θU (uncertainty process) 0.34 Local unit/firm employment vol
Data is firm-level from Compustat
• 10 year panel 1991 to 2000 to “out of sample” simulate 9/11
• Large continuing manufacturing firms (>500 employees, mean 4,500)
– Focus on most aggregated firms
– Minimize entry and exit
• Final sample 579 firms with 5790 observations
Note: This methodogly enables use of public firm data, avoiding the
need to access the LRD, but relies on representativeness of public data
see (Davis, Haltiwanger, Jarmin and Miranda, 2006)
Time out…
Sad but true – for the job-market you need a little bit of algebra. Not loads, but a couple of slides somewhere with greek letters and curly deltas…
If this really is inappropriate put it in the appendix – at least people flicking through your paper will see this
Actual SMM EstimateLabor hire/fire costs (PI) 4.9 weeks wages
Labor fixed costs (FC) 2.4 weeks revenue
Labor quadratic costs (QD) 0
Capital resale cost (PI) 42.1% price capital
Capital fixed costs (FC) 0.3 weeks revenue
Capital quadratic costs (QC) 4.74 of K*(I/K)2
Std (ΔL/L) 0.197 0.234
Skew (ΔL/L) 0.213 0.437
Corr (ΔL/L)t, (ΔL/L)t-2 0.111 0.106
Corr (ΔL/L)t, (I/K)t-2 0.102 0.152
Corr (ΔL/L)t, (ΔS/S)t-2 0.137 0.174
Std (I/K) 0.141 0.146
Skew (I/K) 1.404 1.031
Corr (I/K)t, (ΔL/L)t-2 0.139 0.207
Corr (I/K)t, (I/K)t-2 0.305 0.318
Corr (I/K)t, (ΔS/S)t-2 0.210 0.325
Labor estimationmoments
Capital estimationmoments
“Adjustment cost” estimates
Closer match between left and right columns of moments means a better fit
TABLE 2
Results for estimations on restricted models
Capital “adjustment costs” only
• Fit is only moderately worse
• Both capital & labor moments reasonable
• So capital ACs and pK dynamics approximate labor ACs
Labor “adjustment costs” only
• Labor moments fit is fine
• Capital moments fit is bad (too volatile & low dynamics)
• 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
Robustness - measurement error (ME)
• Labor growth data contains substantial ME from
– Combination full time, part-time and seasonal workers
– Rounding of figures
– First differencing to get ΔL/L
• Need to correct in simulations to avoid bias
• I estimate ME using a wage equation and find 11%
– Hall (1989) estimates comparing IV & OLS & finds 8%
• So I build 11% ME into main SMM estimators
– Also robustness test without any ME and find larger FCL
Robustness – volatility measurement
• Volatility process calibrated by share returns volatility
– But could be concerns over excess volatility due to “noise”
• Jung & Shiller (2002) suggest excess volatility more macro problem
• Vuolteenaho (2002) finds “cash flow” drives 5/6 of S&P500 relative returns
• Use 5/6 relative S&P500 returns variance and results robust
– Find slightly higher adjustment costs
Time out…
The last two slides I have typically do not present – I skip them having thought in advance they are less important
Simulating 2nd moment uncertainty shocks
To recap the uncertainty process is as follows
Simulation of macro shock sets St=1 for one period (and Zt≡0)
• σσ = σ*, so shocks doubles average σ2
t (from initial graph)
• Prob(St=1) is 1/60, so shocks every 5 years (from initial graph)
Run the thought experiment of just a second moment shock
– Will add 1st moment shocks, but leave out initially for clarity
tσZtσtt SσZσ)σ(σρσσ 1*
1
}10{ ,~St)1,0(~NZ t
Simulation uncertainty macro “impulse”
Month
Un
cert
ain
ty (
σt)
uncertainty shock
Run model monthly with 100,000 firms for 5 years to get steady state then hit with uncertainty shock
Net
hir
ing
rat
e
Percentiles of firm net hiring rates (%)
Aggregate net hiring rate (%)
Month
uncertainty shock
99th Percentile
Month
95th Percentile
5th Percentile
1st Percentile
Net
hir
ing
rat
e
Inve
stm
en
t ra
te
Firm percentiles of gross investment rates (%)
Macro gross investment rate (%)
Month
uncertainty shock
Inve
stm
en
t ra
te
99th Percentile
Month
95th Percentile
5th Percentile
1st Percentile
Productivity & hiring,period after shock
Pro
du
ctiv
ity
gro
wth
Month
uncertainty shock
Productivity (logs)
Productivity growth rate (%)
Productivity (logs)
Productivity & hiring,period before shock
Gro
ss h
irin
g r
ate
Gro
ss h
irin
g r
ate
Total
Between
Within
Cross
GDP loss from uncertainty shock
Estimate very rough magnitude of GDP loss, noting
• Only from temporary 2nd moment shock (no 1st moment effects)
• Ignores GE (will discuss shortly) so only look at first few months
First 2 months First 4 months First 6 months
Input Factors 0.30 0.74 1.16
TFP (reallocation) 0.07 0.11 0.14
Total 0.37 0.85 1.30
Rough GDP loss from an uncertainty shock (% of annual value)
Reasonable size – uncertainty effects wipes out growth for ½ half year
Highlights importance identifying 1st & 2nd moment components of shocks
Pro
d.
gro
wth
Month
Inve
stm
ent
rate
After a 1st moment shock expect standard U-shape downturn, bottoming out after about 6-18 months
After a 2nd moment shock everything drops – just like a 1st moment shock- but then bounces back within 1 month
To distinguish try using:(i) volatility indicators; (ii) plant spread;to help distinguish
Hir
ing
rat
e
Robustness – Risk aversion
Month
In
vest
men
t ra
te
• Earlier results assumed firms risk-neutrality
• Re-simulate with an “ad-hoc” risk correction where rt = a + bσt
– Calibrated so that increases average (r) by 2.5%
uncertainty shock
risk-averse
risk-neutral
Robustness – Adjustment costs estimation• Need some non-convex costs - nothing with convex ACs only
• Robust to type non-convex ACs (Dixit (1993) and Abel & Eberly (1996) show thresholds infinite derivate AC at AC≈0 )
PI=10%, all other AC=0
FC=1%, all other AC=0
Aggregate Hiring Hiring Distribution Productivity
Aggregate Hiring Hiring Distribution Productivity
Robustness - General Equilibrium effects• Could run GE approximating the cross-sectional distribution of firms
– But need another program loop, so much slower – so choice:(i) estimating ACs, or (ii) doing GE
– Estimate ACs as probably more sensitive to this and do GE later
• Less sensitive to GE for two reasons
– Uncertainty shocks very rapid and big, but wages and prices “sticky” at monthly frequency and interest rates bounded at zero
• Uncertainty shock adds 6% to 10% to hurdle rates, but after 9/11 interest rates fell by only 1.75%
– Drop & rebound probably optimal with GE anyway as correct factor allocation unclear, expensive to change so pause is good
• Sim (2007) estimates simple GE version and finds impact temporary uncertainty shocks reduced by ½ by GE, but still large.
Month
In
vest
men
t ra
te
• Earlier results 2nd moment shock only ~ thought experiment
• But shocks typically have 1st and 2nd moment component
• Re-simulate assuming– 2nd moment shock (doubles uncertainty as before)– 1st moment shock (-5% ≈ 1 years growth)
1st & 2nd moment shock
2nd moment shock
Robustness – Combined 1st and 2nd moment shock
How does the simulation fit against actual data?
• Estimate VAR on monthly data 1962-2006
• Look at 9/11 as an event study
Estimate an orthogonal VAR
Shock-measure:
• Baseline: (1/0) measure for 16 shocks on figure, dated max month
• Robustness: Actual value, first month, & oil/war/terror shocks only
Variables & ordering:
• Baseline: log(industrial production), log(employment), inflation, hours, interest rates, volatility and log(stock-market levels)
• Robustness: use smaller data sets and different orderings
Detrending:
• Baseline: HP filter with smoothing parameter of 144,000
• Robustness: More smoothing (1440) and linear detrending (∞)
-1-.
50
.51
0 6 12 18 24 30 36year
-2-1
01
0 6 12 18 24 30 36year
VAR baseline impact of an uncertainty shock%
im
pac
t
Notes: VAR Cholesky orthogonalized impulse response functions estimated on monthly data from July 1963 to July 2005 using 12 lags. Dotted lines in top and bottom figures are one standard error bands around the response to a volatility shock indicator, coded as a 1 for the 15 labelled shocks in Figure 1, and 0 otherwise. Variables (in order) are log industrial production, log employment, hours, inflation, federal funds rate, log stock market levels and the volatility shock indicator. All data detrended using a Hodrick-Prescott filter with smoothing parameter of 14400
Months after the shock
Response to 1% shock to the Federal Funds Rate
% i
mp
act
Months after the shock
Response to 20% shock to volatility
Response to 1% shock to the Federal Funds Rate
Response to 20% shock to volatility
Industrial Production
Employment
010
20
30
40
50
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005Year
Categorizing exogenous volatility shocks
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
Shocks classification: “Oil” “Terror” “War” “Economic”
Arguably exogenous
-1.5
-1-.
50
.51
0 6 12 18 24 30 36year
-3
-2
-1
01
0 6 12 18 24 30 36year
VAR robustness to different shock definitions
Trivariate (industrial production, log employment and volatlity)
Bivariate (industrial production and volatility)
Months after the shock
Months after the shock
% p
rod
uct
ion
im
pac
t
Trivariate in reverse order (volatlity, log employment and industrial production)
Notes: VAR Cholesky orthogonalized impulse response functions estimated on monthly data from July 1963 to July 2005 using 12 lags. All data detrended using a Hodrick-Prescott filter with smoothing parameter of 14400. In top panel variables (in order) are log industrial production, log employment, hours, inflation, federal funds rate, log stock market levels and the volatility indicator. The volatility indicator used is different for each plot as follows: “actual volatility” is the de-trended series itself, “shocks scaled by actual volatility” uses the 16 shocks but scales these by their actual de-trended level, “shocks dated by first month” uses the 16 events with the timing defined by their first month, and “terror, war and oil shocks only” uses a 1/0 indicator for just the 10 shocks defined as terror, war or oil related. In the bottom panel the standard volatility indicator is used (a 1/0 for each of the 16 shocks in Figure 1 timed by the peak volatility month) but the variable sets and ordering var as noted.
Terror, War & Oil shocks
Actual volatility series
Shocks dated first month
Shocks scaled by volatility
% p
rod
uct
ion
im
pac
t
How does the simulation fit against actual data?
• Estimate VAR on monthly data 1962-2006
• Look at 9/11 as an event study
-1000-500
0500
1000
forecast/dem
pq1/dem
pq
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year
forecast dempq1
dempq
-5
-2.5
02.5
5
forecast/z/G
ross priv
ate dom
estic
in
vestm
ent
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year
forecast z
Gross private domestic investment
Quarterly Investment (% contribution to real GDP growth) 2
9/11 did generate a rapid drop and reboundQuarterly Net Hiring (total private, thousands) 1
9/11
1 BLS Current Employment Statistics survey, Total private employees (1000s), seasonally adjusted, quarterly net change, from series CES05000000012 BEA National Income and Product Accounts, Contributions to % change in real Gross Domestic Product, seasonally adjusted at annual rates, from Table 1.1.23 Federal Reserve Bank of Philadelphia “Survey of Professional Forecasters” average of 33 economic forecasters, www.phil.frb.org/file/spf/survq301.html
Lowest quarterly value since 1980
Lowest quarterly value since 1982
Forecast of 23rd August 20013
Forecast of 23rd August 20013
1 Compustat quarterly investment rates (%). Numerator equals plant, property and equipment purchases less resales, plus net change in inventories; denominator equals total stock of net fixed assets plus inventories averaged over the current and prior quarter. Balanced panel of all 375 publicly quoted manufacturing firms with at least $20m average sales and complete quarterly data from 1990 to 2005. The standard deviation (SD) of quarterly investment has been normalized at the quarterly level based on the pre-2001 SD of investment.
9/11Cross sectional standard deviation of investment rates1
Investment rate histogram,2001 Q3 (before 9/11)
…and investment rates appeared to compress
Investment rate histogram,2002 Q1 (after 9/11)
68
10
12
14
1990 1995 2000 2005Year
9/11
01
02
03
04
05
06
0
Pe
rcen
t
-100 -50 0 50 100ninv
01
02
03
04
05
06
0
Pe
rcen
t
-100 -50 0 50 100ninv
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)
Time out…
Doing this is risky, but probably OK for this paper. I put this up as people really engaged with the bigger picture and historical context. Again graphs….
Conclusions
• Uncertainty spikes after major economic & political shocks
• Estimation and simulation predicts rapid drop & rebound– For VAR appears to roughly match actual data– This time profile looks different from a levels shock
• Suggests policy makers try to distinguish levels & uncertainty effects– Financial volatility (VXO) and compression of firm activity
• Working on parameter estimations in current paper, and into GE with Nir Jaimovich
Current extension in progress
Build GE model by approximating cross-sectional distribution. Shouldhelp with a number of business-cycle issues, in particular:
• Lack of negative TFP shocks - 2nd moment shocks mimic these (especially after detrending)
• Drop on impact for TFP shocks - 1st moment shocks raise uncertainty when the shock first hits (dynamic inference)
• Instability of VARs without 2nd moment controls
Also model link between volatility and growth – less reallocation (which drives about ½ to ¾ of TFP growth) at higher uncertainty
Base my model as much as possible on literature
Investment• Firm: Guiso and Parigi (1999), Abel
and Eberly (1999) and Bloom, Bond and Van Reenen (2006), 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)
Simulation estimation• Cooper and Ejarque (2001),
Cooper and Haltiwanger (2003), and Cooper, Haltiwanger and Willis (2004)
Real Options & Adjustment costs• Abel and Eberly (1994), Abel and
Eberly (1996), Caballero & Leahy (1996), and Eberly & Van Mieghem (1997)
• MacDonald and Siegel (1986), Pindyck (1988) and Dixit (1989)
-5
-2.5
02.5
5
forecast/forecast1/G
ross priv
ate dom
estic
in
vestm
ent
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year
forecast forecast1Gross private domestic investment
-1000-500
0500
1000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year
forecast forecast1dempqQuarterly Investment (% contribution to real GDP growth) 2
Forecasters also roughly predicted drop & reboundQuarterly Net Hiring (total private, thousands) 1
9/11
1 BLS Current Employment Statistics survey, Total private employees (1000s), seasonally adjusted, quarterly net change, from series CES05000000012 BEA National Income and Product Accounts, Contributions to % change in real Gross Domestic Product, seasonally adjusted at annual rates, from Table 1.1.23 Federal Reserve Bank of Philadelphia “Survey of Professional Forecasters” average of 33 economic forecasters, www.phil.frb.org/file/spf/survq301.html
Forecast of 23rd August 20013
Forecast of 23rd August 20013
Forecast of 14th November 2001
Forecast of 14th November 2001
Partial Irreversibility (PI) LaborCapital
Quadratic (QD) LaborCapital
Fixed Labor
Capital
“Adjustment costs” (1)
hiring/firing cost per person
cost per unit capital resold
“rapid” hiring/firing more costly
“rapid” investment more costly
lump sum hire/fire cost
lump sum investment cost
Concept
FCQDPI ),,,( HLKYC
“Adjustment Cost” Factor
Partial Irreversibility (PI) LaborCapital
Quadratic (QD) LaborCapital
Fixed (FC) Labor
Capital
Source: Romer (1992, JEH)
Rise in volatility
Fall in volatility
Banking panics
GNP growth in the Great Depression
Approximating cross-sectional distributions
Number of ways to approximate cross sectional distributions, i.e.– Moments (Krussell and Smith)– Characteristics functions (Caballero and Engel)
I use bins exploiting the fact agents know distribution is bounded, i.e:
Capital/Demand (K/Y)
Actual distribution
Bin approximation
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 falling (2-year market rates fell be less than 1%)
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 POLICY VERDICT
Firm level volatility after 9/110
5010
0
2001.5 2002Year
sd10 sd25sd50 sd75sd90
90th Percentile
75th Percentile
50th Percentile
10th Percentile25th Percentile
9/11
Calculated from CRSP daily share returns volatility within each month of balanced panel of 1,052 firms in CRSP-Compustat matched sample with over 500 employees and full daily trading data from 1990 to 2003. 9/11 month volatility taken from the first trading day after the attack until the end of the month (the 9 trading days from 9/17/2001 until 9/28/2001).
Real 9/11 shock did actually shift distribution of returns volatility upwards
Monthly data
Actual Compustat firm level data
Auto-regressive σt approximated by Markov-chain
σ=8% σ=17% σ=25% σ=38% σ=76%
σ=8% 0.645 0.249 0.084 0.020 0.002
σ=17% 0.249 0.361 0.255 0.115 0.020
σ=25% 0.084 0.255 0.321 0.255 0.084
σ=38% 0.020 0.255 0.255 0.361 0.249
σ=76% 0.002 0.020 0.084 0.249 0.645
Tauchen & Hussey (1991) to define 5-point space and transition matrix
- Normal times (St=0) calibrated from firm share returns volatility
σ=8% σ=17% σ=25% σ=38% σ=76%
σ=8% 0.001 0.008 0.033 0.132 0.825
σ=17% 0.000 0.000 0.000 0.007 0.993
σ=25% 0.000 0.000 0.000 0.001 0.999
σ=38% 0.000 0.000 0.000 0.000 1.000
σ=76% 0.000 0.000 0.000 0.000 1.000
- Shock period (St=1) calibrated to double uncertainty
Robustness- general equilibrium effects (2)
• 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
24
68
2000 2001 2002 2003 2004 2005Year
3-year T-Bills ir
Source: Federal Reserve Board Statistical Release - http://www.federalreserve.gov/releases/H15/data.htm
2-year rate (T-Bill)
Federal Funds rate
9/11
Interest rates
% GDP 01 Q1 01 Q2 01 Q3 01 Q4 02 Q1 02 Q2 02 Q3 02 Q4
Budget surplus 1.1 0.5 -1.8 -1.3 -3.3 -3.7 -3.7 -4.3
…exc. personal tax -11.8 -12.5 -12.7 -13.4 -13.6 -13.7 -13.6 -13.9
Fiscal position ≈ flat 2001-02 excluding personal tax cuts
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