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VAR(MA), what is it good for? more bad news for reduced-form estimation and inference

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Abstract
It is common practice to use reduced-form vector autoregression (VAR) models, or more generally vector autoregressive moving average (VARMA) models, to characterize the dynamics in observed data and to identify innovations to the macroeconomy in some economically meaningful way. We demonstrate that neither approach|VAR or VARMA|are suitable reduced form guides to \reality", if reality were induced by some underlying structural DSGE model. We conduct such a thought experiment across a wide class of DSGE structures that imply particular VARMA data generating processes (DGPs). We find that with the typical small samples for macroeconomic data, the MA component of the fitted VARMA models is close to being non-identified. This in turn leads to an order reduction when identifying the lag structures of the VARMA models. As a result, VARMA models barely show any advantage over VARs using realistic sample sizes. However, the VAR remains a truly misspecified approximation. The VAR's performance deteriorates, in contrast to the VARMA's, as we enlarge the sample size generated from the true DGPs.

Suggested Citation

  • Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2014. "VAR(MA), what is it good for? more bad news for reduced-form estimation and inference," Working Papers 2014-14, University of Tasmania, Tasmanian School of Business and Economics.
    • Handle: RePEc:tas:wpaper:18749
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    File URL: http://eprints.utas.edu.au/18749/1/2014-14_Yao.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    VARMA; VAR; DSGE; impulse response analysis;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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