1. The Impact of Uncertainty Shocks: Firm-Level Estimation and a 9/11 Simulation Nick Bloom (Stanford & NBER) April 2007

2. 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 9/11 Enron Gulf War II Implied Volatility Actual Volatility Afghanistan JFK assassinated Cuban missile crisis Cambodia, Kent State OPEC I Franklin National Annualized standard deviation (%) Vietnam build-up

3. 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)

4. 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. OPEC II Monetary cycle turning point Black Monday 3 Gulf War I Asian Crisis Russian & LTCM Default September 11 4 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

5. 20012002 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

6. “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 2 nd 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 27 th

7. Motivation Major shocks have 1 st and 2 nd moments effects Policymakers believe both matter – is this right? –Lots of work on 1 st moment shocks –Much less work on 2 nd moment shocks Closest work probably Bernanke (1983, QJE) –Predicts wave like effect of uncertainty flucatuations I confirm, quantify & estimate this work

8. 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 2 nd 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

9. 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

10. Estimation Model Results Shock Simulations

11. Firm Model outline Model has 3 main components Net revenue function, R Labor & capital “adjustment costs”, C Stochastic processes, E[ ] Firms problem = max E[ Σ t (R t –C t ) / (1+r) t ]

12. 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.

13. 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 Y 1-a-b =A (1-1/e) B a=α(1-1/e), b=β(1-1/e)

14. Revenue function (2) Firms can freely adjust hours but pay an over/under time premium W 1 and w 2 chosen so hourly wage rate is lowest at a 40 hour week Net Revenue = Gross Revenue - Wages

15. “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 capitalLabor and Capital Convex 1 Traditional Euler and Tobin’s Q models Shapiro (1986); Hall (2004), Merz and Yashiv (2003) Convex 1 and Non-Convex 2 Abel & Eberly (1999); Cooper & Haltiwanger (2003); Cooper, Haltiwanger and Willis (2004) 1 Convex typically quadratic adjustment costs 2 Non-convex typically fixed cost or partial irreversibility

16. 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

17. “Adjustment costs” (2) 1 period (month) time to build Exogenous labor attrition rate δ L and capital depreciation rate δ K Relative capital price is AR(1) stochastic

18. Stochastic processes – the “first moment” “Demand conditions” combines a macro and a firm random walk 1 st MOMENT SHOCK The macro process is common to all firms The firm process is idiosyncratic Assumes firm and macro uncertainty move together - consistent with the data for large shocks (i.e. Campbell et al. 2001)

19. Stochastic processes – the “second moment” 2 nd MOMENT SHOCK Uncertainty is AR(1) process with infrequent jumps σ σ =σ * so shocks roughly double average σ 2 t (note σ Z is much smaller) Prob(S t =1) is 1/60, so one shock expected every 5 years

20. 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..)

21. 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 Simplified value function Note: I is gross investment, E is gross hiring/firing and H is hours

22. 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

23. “Demand Conditions”/Labor: Ln(Y/L) “Demand Conditions”/Capital: Ln(Y/K) Example hiring/firing and investment thresholds Inaction Fire Invest Disinvest Hire “Real options” type effects

24. High and low uncertainty thresholds Low uncertainty High uncertainty Larger “real options” at higher uncertainty “Demand Conditions”/Labor: Ln(Y/L) “Demand Conditions”/Capital: Ln(Y/K)

25. 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

26. 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

27. 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 Y F and Y M are the firm and macro processes as before Φ U 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

28. 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

29. Estimation Model Results Shock Simulations

30. 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

31. Simulated Method of Moments estimation SMM minimizes distance between actual & simulated moments Efficient W is inverse of variance-covariance of (Ψ A - Ψ S (Θ)) Lee & Ingram (1989) show under the null W= (Ω(1+1/κ)) -1 –Ω is VCV of Ψ A, bootstrap estimated –κ simulated/actual data size, I use κ=10 actual data moments simulated moments weight matrix

32. Pre-determined parameters Parameter:Value:Source: α (capital coefficient)1/3Prod function estimation β (labor coefficient)2/3Prod function estimation δ K (capital depreciation)10%Depreciation estimates δ L (labor quit rate)10%Matched to capital w 1 (wage parameter)1/310 employees per unit w 2 (wage parameter)7e-0640 hour working week γ (wage parameter)2.5Overtime share 27% μ (demand drift)5%Compustat average growth ε (demand elasticity)-350% mark-up p k * (capital price process)1Normalized to unity ρ p k (capital price process)0.12NBER 4-digit industry data σ p k (capital price process)0.27NBER 4-digit industry data σ* (uncertainty process)0.29Firm level share returns vol σ σ (uncertainty process)0.29Macro shock doubles σ t ρ σ (uncertainty process)0.421.5 month shock half-life θ F (uncertainty process)1.13Firm/macro returns vol θ U (uncertainty process)0.34Local unit/firm employment vol

33. 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)

34. 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

35. Estimation Model Results Shock Simulations

36. ActualSMM Estimate Labor 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)0.1970.234 Skew (ΔL/L)0.2130.437 Corr (ΔL/L) t, (ΔL/L) t-2 0.1110.106 Corr (ΔL/L) t, (I/K) t-2 0.1020.152 Corr (ΔL/L) t, (ΔS/S) t-2 0.1370.174 Std (I/K)0.1410.146 Skew (I/K)1.4041.031 Corr (I/K) t, (ΔL/L) t-2 0.1390.207 Corr (I/K) t, (I/K) t-2 0.3050.318 Corr (I/K) t, (ΔS/S) t-2 0.2100.325 Labor estimation moments Capital estimation moments “Adjustment cost” estimates Closer match between left and right columns of moments means a better fit TABLE 2

37. Results for estimations on restricted models Capital “adjustment costs” only Fit is only moderately worse Both capital & labor moments reasonable So capital ACs and p K 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

38. 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 FC L

39. 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

40. Time out… The last two slides I have typically do not present – I skip them having thought in advance they are less important

41. Estimation Model Results Shock Simulations

42. Simulating 2 nd moment uncertainty shocks To recap the uncertainty process is as follows Simulation of macro shock sets S t =1 for one period (and Z t ≡0) σ σ = σ *, so shocks doubles average σ 2 t (from initial graph) Prob(S t =1) is 1/60, so shocks every 5 years (from initial graph) Run the thought experiment of just a second moment shock –Will add 1 st moment shocks, but leave out initially for clarity

43. Simulation uncertainty macro “impulse” Month Uncertainty (σ t ) uncertainty shock Run model monthly with 100,000 firms for 5 years to get steady state then hit with uncertainty shock

44. Net hiring rate Percentiles of firm net hiring rates (%) Aggregate net hiring rate (%) Month uncertainty shock 99 th Percentile Month 95 th Percentile 5 th Percentile 1 st Percentile Net hiring rate

45. Investment rate Firm percentiles of gross investment rates (%) Macro gross investment rate (%) Month uncertainty shock Investment rate 99 th Percentile Month 95 th Percentile 5 th Percentile 1 st Percentile

46. Productivity & hiring, period after shock Productivity growth Month uncertainty shock Productivity (logs) Productivity growth rate (%) Productivity (logs) Productivity & hiring, period before shock Gross hiring rate Total Between Within Cross

47. GDP loss from uncertainty shock Estimate very rough magnitude of GDP loss, noting Only from temporary 2 nd moment shock (no 1 st moment effects) Ignores GE (will discuss shortly) so only look at first few months First 2 monthsFirst 4 monthsFirst 6 months Input Factors0.300.741.16 TFP (reallocation)0.070.110.14 Total0.370.851.30 Rough GDP loss from an uncertainty shock (% of annual value) Reasonable size – uncertainty effects wipes out growth for ½ half year

48. Highlights importance identifying 1 st & 2 nd moment components of shocks Prod. growth Month Investment rate After a 1 st moment shock expect standard U-shape downturn, bottoming out after about 6-18 months After a 2 nd moment shock everything drops – just like a 1 st moment shock - but then bounces back within 1 month To distinguish try using: (i) volatility indicators; (ii) plant spread; to help distinguish Hiring rate

49. Robustness – Risk aversion Month Investment rate Earlier results assumed firms risk-neutrality Re-simulate with an “ad-hoc” risk correction where r t = a + bσ t –Calibrated so that increases average (r) by 2.5% uncertainty shock risk-averse risk-neutral

50. 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 HiringHiring DistributionProductivity Aggregate HiringHiring DistributionProductivity

51. 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.

52. Month Investment rate Earlier results 2 nd moment shock only ~ thought experiment But shocks typically have 1 st and 2 nd moment component Re-simulate assuming –2 nd moment shock (doubles uncertainty as before) –1 st moment shock (-5% ≈ 1 years growth) 1 st & 2 nd moment shock 2 nd moment shock Robustness – Combined 1 st and 2 nd moment shock

53. How does the simulation fit against actual data? Estimate VAR on monthly data 1962-2006 Look at 9/11 as an event study

54. 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 (∞)

55. VAR baseline impact of an uncertainty shock % impact 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 % impact 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

56. Categorizing exogenous volatility shocks OPEC II Monetary turning point Black Monday* Gulf War I Asian Crisis Russia & LTCM 9/11 Enron Gulf War II Implied Volatility Actual Volatility Afghanistan JFK assassinated Cuban missile crisis Cambodia, Kent State OPEC I Franklin National Annualized standard deviation (%) Vietnam build up Shocks classification:“Oil”“Terror”“War”“Economic” Arguably exogenous

57. VAR robustness to different shock definitions Trivariate (industrial production, log employment and volatlity) Bivariate (industrial production and volatility) Months after the shock % production impact 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 % production impact

58. How does the simulation fit against actual data? Estimate VAR on monthly data 1962-2006 Look at 9/11 as an event study

59. Quarterly Investment (% contribution to real GDP growth) 2 9/11 did generate a rapid drop and rebound Quarterly 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 CES0500000001 2 BEA National Income and Product Accounts, Contributions to % change in real Gross Domestic Product, seasonally adjusted at annual rates, from Table 1.1.2 3 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 23 rd August 2001 3

60. 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. 9/11 Cross sectional standard deviation of investment rates 1 Investment rate histogram, 2001 Q3 (before 9/11) …and investment rates appeared to compress Investment rate histogram, 2002 Q1 (after 9/11) 9/11

61. A QUICK HISTORICAL DIGRESSION (not really part of the paper)

62. 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

63. 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)

64. END OF DIGRESSION

65. 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….

66. 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

67. Current extension in progress Build GE model by approximating cross-sectional distribution. Should help with a number of business-cycle issues, in particular: Lack of negative TFP shocks - 2 nd moment shocks mimic these (especially after detrending) Drop on impact for TFP shocks - 1 st moment shocks raise uncertainty when the shock first hits (dynamic inference) Instability of VARs without 2 nd moment controls Also model link between volatility and growth – less reallocation (which drives about ½ to ¾ of TFP growth) at higher uncertainty

68. BACK-UP

69. 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)

70. Quarterly Investment (% contribution to real GDP growth) 2 Forecasters also roughly predicted drop & rebound Quarterly 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 CES0500000001 2 BEA National Income and Product Accounts, Contributions to % change in real Gross Domestic Product, seasonally adjusted at annual rates, from Table 1.1.2 3 Federal Reserve Bank of Philadelphia “Survey of Professional Forecasters” average of 33 economic forecasters, www.phil.frb.org/file/spf/survq301.html Forecast of 23 rd August 2001 3 Forecast of 14 th November 2001

71. Partial Irreversibility (PI)Labor Capital Quadratic (QD) Labor Capital 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“Adjustment Cost”Factor Partial Irreversibility (PI)Labor Capital Quadratic (QD) Labor Capital Fixed (FC) Labor Capital

72. Source: Romer (1992, JEH) Rise in volatility Fall in volatility Banking panics GNP growth in the Great Depression

73. 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

74. 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 6 th 2001 THE POLICY VERDICT

75. Firm level volatility after 9/11 90 th Percentile 75 th Percentile 50 th Percentile 10 th Percentile 25 th 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

76. Auto-regressive σ t approximated by Markov-chain σ=8%σ=17%σ=25%σ=38%σ=76% σ=8%0.6450.2490.0840.0200.002 σ=17%0.2490.3610.2550.1150.020 σ=25%0.0840.2550.3210.2550.084 σ=38%0.0200.255 0.3610.249 σ=76%0.0020.0200.0840.2490.645 Tauchen & Hussey (1991) to define 5-point space and transition matrix - Normal times (S t =0) calibrated from firm share returns volatility σ=8%σ=17%σ=25%σ=38%σ=76% σ=8%0.0010.0080.0330.1320.825 σ=17%0.000 0.0070.993 σ=25%0.000 0.0010.999 σ=38%0.000 1.000 σ=76%0.000 1.000 - Shock period (S t =1) calibrated to double uncertainty

77. Robustness- general equilibrium effects (2) Thomas (2002) and Veracierto (2002) suggest GE important –In particular they find under GE M t is a BC variable like labor, or capital Y t is aggregate productivity/demand NC is some non-convex cost –But I look at σ t is uncertainty So correctly highlight importance of GE, but on a different issue

78. Also need to deal with aggregation % annual zero investment episodes (UK Firm and Plant data) QuarterlyYearly Sales6.782.97 Investment1.180.84 standard deviation/mean of growth rates (US firm data) StructuresEquipmentVehiclesTotal Firms5.90.1n.a.0.1 Establishments46.83.221.21.8 Single plants53.04.323.62.4 Small single plants57.65.624.43.2 Aggregation across units Aggregation across time Aggregation across lines of capital

79. 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 % GDP01 Q101 Q201 Q301 Q402 Q102 Q202 Q302 Q4 Budget surplus1.10.5-1.8-1.3-3.3-3.7 -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