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Priors about Observables in Vector Autoregressions

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  • Marek Jarocinski
  • Albert Marcet

Abstract

Standard practice in Bayesian VARs is to formulate priors on the autore- gressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. Our proposal is to use prior infor- mation on observables systematically. We show how this kind of prior can be used under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a large number of parameters. We prove various convergence theorems for the algorithm. Using examples from the VAR literature, we show how priors on observables can address a priori weaknesses of standard priors, serving as a cross check and an alternative formulation.

Suggested Citation

  • Marek Jarocinski & Albert Marcet, 2013. "Priors about Observables in Vector Autoregressions," Working Papers 684, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:684
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    References listed on IDEAS

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    Cited by:

    1. Weale, Martin & Wieladek, Tomasz, 2016. "What are the macroeconomic effects of asset purchases?," Journal of Monetary Economics, Elsevier, vol. 79(C), pages 81-93.

    More about this item

    Keywords

    vector autoregression; Bayesian estimation; prior about observables; inverse problem; monetary policy shocks;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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