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Can Long-Run Restrictions Identify Technology Shocks?

Author

Listed:
  • Christopher J. Erceg

    (Federal Reserve Board,)

  • Luca Guerrieri

    (Federal Reserve Board,)

  • Christopher Gust

    (Federal Reserve Board,)

Abstract

Galí's innovative approach of imposing long-run restrictions on a vector autoregression (VAR) to identify the effects of a technology shock has become widely utilized. In this paper, we inves-tigate its reliability through Monte Carlo simulations using calibrated business cycle models. Overall, Galí's methodology appears to be fruitful: the impulse responses derived from the artificial data generally have the same sign and qualitative pattern as the true responses, and the approach can be informative in discriminating between alternative models. However, our results reveal some important quantitative shortcomings, including considerable estimation uncertainty about the impact of technology shocks on macroeconomic variables. More gen-erally, the conditions under which the methodology performs well appear considerably more restrictive than implied by the key identifying assumption. This underscores the importance of using economic models to guide in the implementation of the approach, in interpreting the results, and in assessing its limitations. (JEL: C32, E32, O33) Copyright (c) 2005 by the European Economic Association.

Suggested Citation

  • Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    • Handle: RePEc:tpr:jeurec:v:3:y:2005:i:6:p:1237-1278
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    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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