Working Paper: NBER ID: w14430
Authors: V. V. Chari; Patrick J. Kehoe; Ellen R. McGrattan
Abstract: The central finding of the recent structural vector autoregression (SVAR) literature with a differenced specification of hours is that technology shocks lead to a fall in hours. Researchers have used this finding to argue that real business cycle models are unpromising. We subject this SVAR specification to a natural economic test by showing that when applied to data generated from a multiple-shock business cycle model, the procedure incorrectly concludes that the model could not have generated the data as long as demand shocks play a nontrivial role. We also test another popular specification, which uses the level of hours, and show that with nontrivial demand shocks, it cannot distinguish between real business cycle models and sticky price models. The crux of the problem for both SVAR specifications is that available data necessitate a VAR with a small number of lags and, when demand shocks play a nontrivial role, such a VAR is a poor approximation to the model's infinite order VAR.
Keywords: Structural VARs; Business Cycle Theory; Technology Shocks; Non-technology Shocks; Impulse Responses
JEL Codes: C32; C51; E13; E2; E3; E32; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technology shocks (D89) | hours worked (J22) |
| non-technology shocks (E39) | hours worked (J22) |
| technology shocks + non-technology shocks (E39) | hours worked (J22) |
| SVAR procedure (C32) | misleading conclusion about technology shocks and hours worked (J29) |
| small number of lags (C22) | poor approximation of model's infinite-order VAR (C32) |
| non-technology shocks account for 50% of output fluctuations (E39) | negative impact of technology shocks on hours worked (F66) |