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Are structural VARs with long-run restrictions useful in developing business cycle theory?

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  • 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 and show that when applied to data 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 require a VAR with a small number of lags and such a VAR is a poor approximation to the model’s VAR. ; Formerly titled: A critique of structural VARs using business cycle theory ; Originally Working paper no. 631

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Paper provided by Federal Reserve Bank of Minneapolis in its series Staff Report with number 364.

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Date of creation: 2007
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Handle: RePEc:fip:fedmsr:364

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  1. Matching Theory and Data: Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models
    by Christian Zimmermann in NEP-DGE blog on 2009-09-27 01:45:04
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