Monte Carlo Analysis of Forecast Error Variance Decompositions under Alternative Model Identification Schemes

Anna Staszewska-Bystrova

doi:10.18778/0208-6018.338.07

Authors

Anna Staszewska-Bystrova University of Łódź, Faculty of Economics and Sociology, Chair of Econometric Models and Forecasts, http://orcid.org/0000-0002-3941-4986

DOI:

https://doi.org/10.18778/0208-6018.338.07

Keywords:

forecast error variance decomposition, structural vector autoregressive model, long-run restrictions, short-run restrictions

Abstract

The goal of the paper is to investigate the estimation precision of forecast error variance decomposition (FEVD) based on stable structural vector autoregressive models identified using short‑run and long‑run restrictions. The analysis is performed by means of Monte Carlo experiments. It is demonstrated that for processes with roots close to one, selected FEVD parameters can be estimated more accurately using recursive restrictions on the long‑run multipliers than under recursive restrictions on the impact effects of shocks. This finding contributes to the discussion of pros and cons of using alternative identification schemes by providing counterexamples for the notion that short‑run identifying restrictions lead to smaller estimation errors than long‑run restrictions.

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