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Focused information criterion for locally misspecified vector autoregressive models

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  • Jan Lohmeyer
  • Franz Palm
  • Hanno Reuvers
  • Jean-Pierre Urbain

Abstract

This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspecification. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model fit. Any (sufficiently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspecified parameters. In particular, we show that the inability to consistently estimate locally misspecified parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims.

Suggested Citation

  • Jan Lohmeyer & Franz Palm & Hanno Reuvers & Jean-Pierre Urbain, 2019. "Focused information criterion for locally misspecified vector autoregressive models," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 763-792, August.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:7:p:763-792
    DOI: 10.1080/07474938.2017.1409410
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    Cited by:

    1. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.

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