Paper s contribution (1)

Transcription

1 Discussion of The Timing of Monetary Policy Shocks by Giovanni Olivei and Silvana Tenreyro Marc Giannoni Columbia University and NBER NBER s Universities Research Conference Cambridge, December 9, 2005 Paper s contribution (1) Attempt to establish an empirical fact IRFsof y, π, to MP shock depend on quarter during which shock occurs: If MP in Q1: large and rapid response of y If MP in Q2: large response of y; dies out faster If MP in Q3, Q4: smaller response of y Small differences in response of π See Fig. 10, 11 1

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3 Paper s contribution (2) Propose a theoretical explanation: Wages staggered, but not uniformly across quarters If shock in Q4, and wages and prices reset in Q1: potentially large effect on W, P, but little effect on y If shock in Q1, and wages and prices reset in Q1: No effect on W, P, but large effect on y Provide evidence using popular model (CEE, 2005) augmented with different prob. of resetting wages Conceptually Is this important? Potentially yes: relevant for conduct of MP, for forecasting Quantitatively Responses of y seem sensibly different Response of π: less clear 3

4 My take on the paper Empirical fact seems to be there although some issues Explanation due to wage staggering? Interesting and plausible but I am not convinced yet (tested very indirectly) Discussion s outline Some issues: VAR consistent with model? (In)stability Is story really about non uniform wage staggering? Model Calibration Some suggestions 4

5 Is VAR consistent with Model? (1) Model Y t+2 Y t+1 v t L(q t ) E t R t+1 = M(q t ) R t + N(q t ) e t Z t+1 Z t Y t = [y t, π t w t ] determined at t-1 Z t = [Y t, E t-1 Y t+1, R t-1, ] determ. at t-1 or before One equation: R t = (1-ρ)(a π E t-1 π t+1 + a y y t ) + ρr t-1 + e t why E t-1 π t+1 and not E t π t+1? Is VAR consistent with Model? (2) Model solution (state space) Y t = D(q t ) Z t R t = F (q t )Z t + e t Z t+1 = G(q t ) Z t + H(q t ) [ v t ] [ e t ] Approximated by VAR 5

6 Is VAR consistent with Model? (3) Approximation of state space with VAR? Ok for me, but big debate (CKM vs. Christiano et al.) Variables included in VAR Why use Y t = [y t, π t ] in VAR? wages = central variable in model; should be in VAR omitted variable bias? D s g s : constant. Why? Inconsistent with model (should correspond to F (q t )) How should we interpret MP shocks v tp? Issue: identification of MP shocks ==> Estimated IRFs affected! Monetary Policy Shocks OT: test for stability of distribution of v across quarters Cannot reject stability (low power?) Test for mean of v tp? Some evidence of stability I estimated RF policy rule Computed means of MP shocks by quarter v p q1, v p q3, v p q4 > 0 but v p q2 > 0 t-test v p q1= v p q2 rejected at 95% ==> Should have policy rule coefficients depend on q t 6

7 (In)stability of VAR and Policy Rule VAR and RF policy rule estimated on 1966:1-2002:4 sample But strong evidence of parameter instability (Stock-Watson 96, Cogley-Sargent, Boivin-Giannoni 2003, Davig-Leeper 2005) BG 03 similar VAR Heteroskedasticity-robust stability test: rejects stability at 99% (based on Bai-Lumsdaine-Stock, 1998) Stability of policy rule: rejected strongly (p<0.01) Does Instability affect IRFs? Oh yes! Output response to MP shock Note: changes in IRFs can be fully explained by change in MP rule 7

8 Is story really about non uniform wage staggering? (1) In Model: Wages are changed every quarter either re-optimized or indexed to past inflation Story is clearly more complex than described Might be useful to show in simple model without indexing how different prob. of re-optimizing wages affect IRFs Is story really about non uniform wage staggering? Calibration OT document: New Engl. firms: take decisions re compensation in Q4 changes in comp. become effective in Q1. Other surveys: pay changes take place at beg. of FY Firms in Russel 3000 index: BUT calibrate model s.t. 64% begin FY in Q1 20% of W changes in Q1 16% begin FY in Q2 7% of W changes in Q2 7% begin FY in Q3 33% of W changes in Q3 13% begin FY in Q4 40% of W changes in Q4 Most changes in W at beg. of Q1 8

9 Is story really about non uniform wage staggering? Calibration Anecdotal evidence: Most changes in W at beg. Of Q1 Few changes in W in Q4 Intuitive explanation for IRFs: If MP in Q1: large and rapid response of y If MP in Q3, Q4: smaller response of y But model explains this fact by calibrating model s.t. twice as many changes in wages occur in Q4 than in Q1! Conclusion Nice paper Evidence that IRFs of y differ by quarters Evidence that IRFs of π differ by quarters weak Wage staggering as an explanation? Nice idea, plausible But I am not convinced yet 9

10 Suggestions VAR: add wages reduce omitted variable bias should provide more direct evidence for explanation check that evidence holds in subsamples (pre-, post-80) Model: look at mechanism in simple model (no wage indexing, investment.) and/or estimate model parameters based on matching IRFs, and check that estimated prob. Of changing wages match with anecdotal evidence. 10

11 Monetary Policy Shocks 12 x 10 3 Monetary Policy Shocks: 1966:1 2002:2 10 Q1 Q2 Q3 Q