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Vector Autoregressions and Reduced Form Representations of DSGE Models

Author

Listed:
  • Federico Ravenna
Abstract
Dynamic Stochastic General Equilibrium models are often tested against empirical VARs or estimated by minimizing the distance between the model's and the VAR impulse response functions. These methodologies require that the data-generating process consistent with the DSGE theoretical model has a VAR representation. This paper discusses the assumptions needed for a finite-order VAR(p) representation of any subset of a DSGE model variables to exist. When a VAR(p) is only an approximation to the true VAR, the paper shows that the truncated VAR(p) may return largely incorrect estimates of the impulse response function. The results do not hinge on an incorrect identification strategy or on small sample bias. But the bias introduced by truncation can lead to bias in the identification of the structural shocks. Identification strategies that are equivalent in the true VAR representation perform differently in the approximating VAR.
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Suggested Citation

  • Federico Ravenna, 2005. "Vector Autoregressions and Reduced Form Representations of DSGE Models," 2005 Meeting Papers 841, Society for Economic Dynamics.
    • Handle: RePEc:red:sed005:841
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    References listed on IDEAS

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    More about this item

    Keywords

    Vector Autoreregression; Dynamic Stochastic General Equilibrium Model; Kalman Filter; Business Cycle Shocks;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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