The Impact of Uncertainty Shocks Nick Bloom Stanford

1
The Impact of Uncertainty ShocksNick Bloom
(Stanford NBER)October 2008
2
Monthly US stock market volatility
Black Monday
Credit crunch
6.16 10.00 0 0.42 0.25 0 0.1 0.16
9/11
Russia LTCM
Enron
Franklin National
Cambodia,Kent State
Gulf War II
Monetary turning point
Asian Crisis
JFK assassinated
OPEC I
Afghanistan
Cuban missile crisis
Gulf War I
Annualized standard deviation ()
Vietnam build-up
OPEC II
Actual Volatility
Implied Volatility
Note CBOE VXO index of implied volatility, on
a hypothetical at the money SP100 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 SP500 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
3
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)

4
Stock market volatility is also quite distinct
from stock market levels (shown log-detrended
below)
6.16 10.00 0 0.76 0.25 0 0.1 0.16
Detrended stock market levels correlated with
monthly volatility at -0.340
9/11
Russia LTCM
JFK assassinated
Asian Crisis
Cuban missile crisis
Enron
Vietnam build-up
Gulf War II
Cambodia,Kent State
Credit crunch
Franklin National
OPEC I
Black Monday
Gulf War I
Afghanistan
Monetary turning point
OPEC II
Note SP500 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.
5
But do these uncertainty shocks matter
empirically?

• Want to look at the average impact of an
  uncertainty shock
• Estimate a monthly orthogonal VAR
• log(SP 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

6
Bars denote the 16 uncertainty shocks in the VAR
Annualized standard deviation ()
Actual Volatility
Implied Volatility
Shocks selected as those 2 SD above the HP
filtered trend. VAR run on data until 2007 (so
credit crunch not covered)
7
VAR estimate of the impact of an uncertainty shock
Industrial Production
impact
Response to an uncertainty shock
Response to 1 shock to the Federal Funds Rate
Months after the shock
Employment
impact
Response to an uncertainty shock
Response to 1 shock to the Federal Funds Rate
Months after the shock
Note results robust to different variable
inclusion, ordering detrending (see appendix
figures A1 to A3 ). Dotted lines are /- one
standard-error bands
8
Policy makers also appeared to talk a lot more
about uncertainty after one recent shock 9/11
Frequency of word uncertain in FOMC minutes
9/11
2001
2002
Source count of uncertain/count all words in
minutes posted on http//www.federalreserve.gov/fo
mc/previouscalendars.htm2001
9
And they appeared to believe uncertainty mattered
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 2001Federal Open Market Committee

10
Policymakers also worried about uncertainty from
the credit crunch
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

11

• 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)

12
Summary of the paper

• 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

13
Model
Estimation
Results
Shock Simulations
14
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 (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)

15
Firm Model outline
Net revenue function, R
Model has 3 main components
Labor capital adjustment costs, C
Stochastic processes, E
Firms problem max E St(RtCt) / (1r)t
16
Revenue function (1)

• Cobb-Douglas Production
• A is productivity, K is capital
• L is workers, H is hours, aß1
• Constant-Elasticity Demand
• B is the demand shifter
• Gross Revenue
• A is business conditions
  where A1-a-bA(1-1/e)B aa(1-1/e),
  bß(1-1/e)

17
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

Net Revenue Gross Revenue - Wages
18
Allow for three types of adjustment costs (1)

• Quadratic
• C(I,K) aKK(I/K)2 where IGross investment,
  aK0
• C(E,L) aLL(E/L)2 where EGross hiring/firing,
  aL0
• Partial irreversibility
• C(I,K) bIIgt0 sIIlt0 where bs0
• C(E,L) hEEgt0 - fEElt0 where h0, f0
• Fixed costs
• C(I,K) FCKPQI?0 where FCK0
• C(E,L) FCLPQE?0 where FCL0

19
Adjustment costs (2)

• Assume 1 period (month) time to build
• Exogenous labor attrition rate dL and capital
  depreciation rate dK
• Baseline dLdK10 (annualized value)
• Robustness with dK10 and dL20

20
Stochastic processes the first moment
Business conditions combines a macro and a firm
random walk
The macro process is common to all firms
The firm process is idiosyncratic
Assumes firm macro uncertainty move together
(consistent with results on the 3rd slide and
Table 1)
21
Stochastic processes the second moment
Uncertainty modelled for simplicity as a two
state Markov chain
sH 2sL so high uncertainty twice the
baseline low value (from Figure 1)
With the following monthly transition matrix

• Defined so on average (from Figure 1)
• sH occurs once every 3 years
• sH has a 2 month half-life

22
The optimisation problem
Value function
Note I is gross investment, E is gross
hiring/firing and H is hours

• 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

Simplified value function
23
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

24
Example hiring/firing and investment thresholds
Invest
Business Conditions/Capital Ln(A/K)
Hire
Inaction
Fire
Disinvest
Business Conditions/Labor Ln(A/L)
25
High and low uncertainty thresholds
Larger Real option values at higher uncertainty
(7.5 rise in hurdle rate)
Low uncertainty
Business Conditions/Capital Ln(A/K)
High uncertainty
Business Conditions/Labor Ln(A/L)
26
Distribution of units between the thresholds
Distribution of units
Hiring region
Hiring/Firing rate(solid black line)
Distribution of units(dashed red line)
Firing region
Inactionregion
Business Conditions/Labor Ln(A/L)
Note Plotted for low uncertainty, high drift and
the most common capital/labor (K/L) ratio.
27
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

28
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 PL)
• Links across units in same firm all due to common
  shocks

29
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

30
Model
Estimation
Results
Shock Simulations
31
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

32
Simulated Method of Moments estimation

• SMM minimizes distance between actual simulated
  moments
• Efficient W is inverse of variance-covariance of
  (?A - ?S (T))
• Lee Ingram (1989) show under the null W
  (O(11/?))-1
• O is VCV of ?A, bootstrap estimated
• ? simulated/actual data size, I use ?25

actual data moments
simulated moments
weight matrix
33
The 13 pre-determined parameters
34
Data is firm-level from Compustat

• 20 year panel 1981 to 2000
• Large firms (gt500 employees, mean 4,500)
• Focus on most aggregated firms
• Minimize entry and exit
• Final sample 2548 firms with 22,950 observations

35
Model
Estimation
Results
Shock Simulations
36

• 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

37
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

38
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

39
Robustness

• Table 4 runs some robustness checks of the
  different predetermined parameter estimates
• Makes some difference, but broad findings and
  simulations appear reasonably robust

40
Model
Estimation
Results
Shock Simulations
41
Simulating 2nd moment uncertainty shocks

• Run the initial thought experiment of just a
  second moment shock
• Will add 1st moment shocks, but leave out
  initially for clarity

• Simulate an economy with 1000 units
• Allow the model to run for 10 years
• Set stsH in month 1 of year 11
• Repeat this 25,000 times and take the mean (to
  average over first-moment macro shocks)

42
The second moment shock in the simulation
Uncertainty (st)
Average st (normalized to 1 on pre- shock date)
Month (normalized to 0 for month of shock)
43
The simulation has no first moment shock
Actual
Aggregate At (business conditions) (normalized
to 1 on shock date)
Uncertainty (st)
De-trended
Month (normalized to 0 for month of shock)
44
Aggregate labor drops, rebounds and overshoots
Aggregate Lt (de-trended normalized to 1 on
pre_shock date)
Month (normalized to 0 for month of shock)
45
Splitting out the uncertainty and volatility
effects
Volatility effect only
Baseline (both effects)
Aggregate Lt (de-trended normalized to 1 on
pre_shock date)
Uncertainty effect only
Month (normalized to 0 for month of shock)
46
Distribution of units slide copied from earlier
Distribution of units
Hiring region
Hiring/Firing rate(solid black line)
Distribution of units(dashed red line)
Firing region
Inactionregion
Business Conditions/Labor Ln(A/L)
Notes The hiring response and unit-level density
for low uncertainty (sL), high-drift (µH) and the
most common capital/labor (K/L) ratio.
47
Aggregate capital drops, rebounds and overshoots
Average Kt (de-trended normalized to 1 on
pre-shock date)
Month (normalized to 0 for month of shock)
48
Aggregate TFP growth also slows and rebounds
Definition TFPt ?Li,tAi,t / ?Li,t
Total
TFP growth () (TFPt1-TFPt)/TFPt
Reallocation
Within
Month before the shock
Month after the shock
Hiring/Firing rate
Hiring/Firing rate
Log(Ai,t/Li,t)
Log(Ai,t/Li,t)
49
So de-trended TFP levels drop, rebound overshoot
Solow TFPt Aggregate Output/Factor Share
Weighted Inputs
Solow TFPt (de-trended normalized to 1 on
pre-shock date)
Month (normalized to 0 for month of shock)
50
Output also drops and rebounds

• 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

Average Output(de-trended normalized to 1 on
pre-shock date)
Month (normalized to 0 for month of shock)
51
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

52
VAR estimated impact of an uncertainty shock on
prices
Federal Funds rate( points change)
CPI ( change)
impact
Wages ( change)
Months after the shock

• Approximate this in the simulation by assuming
  that when stsH
• Interest rates 1.1 lower
• Prices of capital and output 0.5 lower
• Wages 0.3 lower
• Firms expect this since incorporated into the
  modelCertainly not exact! Simply guidance on
  possible GE effect

53
Pseudo GE effects have little very short-run
impact

• GE impact initially small due to cautionary
  effect of uncertainty
• Thresholds move out with high st, so not
  responsive
• As st falls back down GE effects have more bite

Also suggests limited very short-run response to
policy stimulus after shocks
Pseudo GE
Average Outputt (de-trended normalized to 1 on
pre-shock date)
Partial Equilibrium
Month (normalized to 0 for month of shock)
54
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

55
Adding first moment shocks
Second moment shock only
First and second moment shock
Average Outputt (de-trended normalized to 1 on
pre-shock date)
First moment shock only
Month (normalized to 0 for month of shock)
56
Different predetermined parameters
N1
Average Outputt (de-trended normalized to 1 on
pre-shock date)
N25
20 markup
20 labor attrition
Month (normalized to 0 for month of shock)
57
Different types of adjustment costs
Fixed costs only
Partial irreversibilities only
Average Outputt (de-trended normalized to 1 on
pre-shock date)
Quadratic only
Month (normalized to 0 for month of shock)
58
Different sizes of uncertainty shocks
Larger (sH3sL)
Baseline (sH2sL)
Smaller (sH1.5sL)
Average Outputt (de-trended normalized to 1 on
pre-shock date)
Month (normalized to 0 for month of shock)
59
Different durations of uncertainty shocks
Shorter lived(1 month half-life)
Baseline(2 month half-life)
Longer live(6 month half-life)
Average Outputt (de-trended normalized to 1 on
pre-shock date)
Month (normalized to 0 for month of shock)
60
A FINAL HISTORICAL DIGRESSION (not really part of
the paper)
61
The Great Depression was notable for very high
volatility
The Great Depression
Recession of 1937
Oil coal strike
Banking panic
9/11
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 Poors
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.
62
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, Moodys 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)

63
END OF DIGRESSION
64
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
65
BACK-UP
66
THE 9/11 POLICY VERDICT
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 questionFOMC Minutes,
  November 6th 2001

67
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
• st is uncertainty
• So correctly highlight importance of GE, but on a
  different issue

68
Also need to deal with aggregation

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

Aggregation across units
Aggregation across lines of capital
standard deviation/mean of growth rates (US firm
data)
Aggregation across time
69
VAR robustness of industrial production plots
Shock definitions
Shocks dated by first month
Actual volatility series
impact
Terror, War and Oil shocks only
Shocks scaled by actual volatility
Months after the shock
Trivariate (shocks, employment production)
Variables ordering
Reverse trivariate (production, employment
shocks)
impact
Bivariate (shocks and production)
Months after the shock
70
VAR robustness of industrial production plots
Detrending
Monthly HP (HP129,600)
impact
Linear (HP8)
Baseline (no detrending)
High frequency (HP1296)
Months after the shock
Oil, credit spread and yield curve
impact
Baseline
Baseline plus Moody Aaa and Baa rates
Baseline plus oil prices
Months after the shock
71
And they appeared to believe uncertainty mattered
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

As with the recent sub-prime shock
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
72
Credit Crunch A Plot of Daily Stock Market
Volatility
Credit Crunch
Updated October 27th
Russian LTCMDefault
WorldCom Enron
9/11
Gulf War II
Implied Volatility on the SP 100 ()
Asian Crisis
Gulf War I
Year