Technology Shocks and Aggregate Fluctuations: How Well Does the RBC - PowerPoint PPT Presentation

�✁ ✂ ☞ ☛ ☛ ✡ ✠ � ✟ ✆ ✆ ✞ ✁ ✝ ✄ ✆ ✄ ✁ ✄☎ Technology Shocks and Aggregate Fluctuations: How Well Does the RBC Model Fit Postwar U.S. Data? by Jordi Gali and Pau Rabanal Comments by Ellen R. McGrattan, Minneapolis Fed

Overview of Gali-Rabanal • Part I: Survey of structural VAR literature ◦ RBC models don’t fit postwar data well Evidence: Hours fall in response to positive technology shock ◦ Technology shocks don’t play central role for business cycles Evidence: Contribution of these shocks quantitatively small • Part II: Results of Sticky price/wage/habit Model ◦ Demand factors are main force for business cycles 1

The Structural VAR • Application of Blanchard and Quah (1989): X t = C ( L ) e t = C 0 e t + C 1 e t − 1 + C 2 e t − 2 + ... ◦ X t = [∆ Log labor productivity, ∆ Log hours] ′ ◦ e t = [‘technology shock’, ‘demand shock’] ′ • Identifying restrictions: ◦ Eee ′ = I , ◦ (1,2) element of C (1) = 0 ⇒ demand shocks have no long-run effect on labor productivity 2

The Evidence that dooms RBC theory Evidence for Nonfarm Business Sector 1 Response to Technology Shock 0.8 Labor Productivity 0.6 0.4 0.2 0 Hours -0.2 -0.4 0 2 4 6 8 10 12 • Technology shocks drive down hours! RBC Theory predicts opposite. 3

VAR results robust to data choice Evidence for GDP and Total Hours 1 Response to Technology Shock 0.8 0.6 Labor Productivity 0.4 0.2 Hours 0 -0.2 -0.4 0 2 4 6 8 10 12 4

A Simple Check on Structural VAR Methodology • What if “data” come from RBC model? ◦ Take plain vanilla RBC model ◦ Simulate time series from model ◦ Apply GR methodology to data from model ◦ Compare GR impulse responses with true responses GR technology with true technology 5

Specific Procedure • Use RBC model with 4 shocks: ◦ Technology = TFP ◦ Government Spending ◦ Labor wedge such that static f.o.c. holds ( U L /U C =(1 − τ ) w ) ◦ Investment wedge such that dynamic Euler equation holds • Estimate vector stochastic process for these shocks using US data 6

Results of Simple Check • Facts from my RBC model: ◦ Positive technology shocks imply hours up ◦ Significant contribution of technology to output spectrum (40%) • GR methodology applied to data from my RBC model: ◦ Positive technology shocks imply hours down! ◦ Insignificant contribution of technology to output spectrum 7

Don’t Get Out What You Put In 0.06 GR TFP 0.04 Output .28 .84 Percent Deviations 0.02 Hours − .12 .43 0 Correlations -0.02 -0.04 GR Technology TFP -0.06 1960 1970 1980 1990 2000 8

Why do GR get it so wrong? • GR identification assumes ◦ primitive shocks uncorrelated ◦ two shocks only (“demand” and “technology”) ◦ certain stationarity restrictions ◦ the data are AR(p), p=4? 9

Why do GR get it so wrong? • GR identification assumes ◦ primitive shocks uncorrelated ◦ two shocks only (“demand” and “technology”) ◦ certain stationarity restrictions ◦ the data are AR(p), p=4? But I didn’t and usually don’t! 10

Why do GR get it so wrong? • GR identification assumes ◦ primitive shocks uncorrelated ◦ two shocks only (“demand” and “technology”) ◦ certain stationarity restrictions ◦ the data are AR(p), p=4? But I didn’t and usually don’t! Problems with structural VARs well known (Cooley and Dwyer, J. Econometrics 1998) 11

Time to Move On • VAR literature surveyed is stuck in the past • Ignores 20 years of work since Kydland and Prescott (1982) • Recent research looking to model sources of variation in TFP Now let’s turn to factors that GR think are important.... 12

After RBC models • GR use VAR results to argue for a “triple-sticky” model: ◦ Adding sticky prices, sticky wages, habit persistence ◦ Subtracting investment, government spending, and net exports! • Is point of triple-sticky model to have a role for money? ◦ Nominal rigidities let money shocks have real effects ◦ Habit persistence extends effects • Is money the important demand shock? 13

Monetary Shocks Play Trivial Role GDP Relative to Trend Inflation Relative to Mean 8 10 6 8 Predicted Predicted Actual Actual 4 6 Percent Deviations Percent Deviations 2 4 0 2 -2 0 -4 -2 -6 -4 -8 1980 1984 1988 1992 1996 1980 1984 1988 1992 1996 Predicted series from sticky model with monetary shocks from US data 14

Big Gap Left for Unobserved Factors Contribution of Shocks to Total Variance Tech. Money Pref. Price Wage Shocks Shocks Shocks Markup Markup Output growth 22.3 4.8 57.1 8.0 7.1 Inflation 6.1 27.1 36.3 13.7 14.7 Hours 0.8 0.4 70.0 17.6 9.6 � �� � Demand factors that matter 15

A Recap 16

A Recap • Claim: RBC models don’t fit postwar data well 17

A Recap • Claim: RBC models don’t fit postwar data well Counter: My RBC model does fit postwar data well GR methodology fails simple test 18

A Recap • Claim: RBC models don’t fit postwar data well Counter: My RBC model does fit postwar data well GR methodology fails simple test • Claim: Technology shocks don’t play central role for business cycles 19

A Recap • Claim: RBC models don’t fit postwar data well Counter: My RBC model does fit postwar data well GR methodology fails simple test • Claim: Technology shocks don’t play central role for business cycles Counter: In my RBC model, technology shocks do play central role GR methodology fails simple test 20

A Recap • Claim: RBC models don’t fit postwar data well Counter: My RBC model does fit postwar data well GR methodology fails simple test • Claim: Technology shocks don’t play central role for business cycles Counter: In my RBC model, technology shocks do play central role GR methodology fails simple test • Claim: Demand factors (not money!) are main force for fluctuations 21

A Recap • Claim: RBC models don’t fit postwar data well Counter: My RBC model does fit postwar data well GR methodology fails simple test • Claim: Technology shocks don’t play central role for business cycles Counter: In my RBC model, technology shocks do play central role GR methodology fails simple test • Claim: Demand factors (not money!) are main force for fluctuations Counter: Just shifting black box from technology to preferences 22