Variance Decomposition Analysis for Nonlinear Economic Models

Maksim Isakin and Phuong V. Ngo

Oxford Bulletin of Economics and Statistics, 2020, vol. 82, issue 6, 1362-1374

Abstract: In this paper, we propose a new method called the total variance method and algorithms to compute and analyse variance decomposition for nonlinear economic models. We provide theoretical and empirical examples to compare our method with the only existing method called generalized forecast error variance decomposition (GFEVD). We find that the results from the two methods are different when shocks are multiplicative or interacted in nonlinear models. We recommend that when working with nonlinear models researchers should use the total variance method in order to see the importance of indirect variance contributions and to quantify correctly the relative variance contribution of each structural shock.

Date: 2020
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