Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR(∞) Models

24 Pages Posted: 30 Nov 2013

See all articles by Atsushi Inoue

Atsushi Inoue

Southern Methodist University

Lutz Kilian

University of Michigan at Ann Arbor - Department of Economics; Centre for Economic Policy Research (CEPR)

Date Written: May 2002

Abstract

It is common to conduct bootstrap inference in vector autoregressive (VAR) models based on the assumption that the underlying data‐generating process is of finite‐lag order. This assumption is implausible in practice. We establish the asymptotic validity of the residual‐based bootstrap method for smooth functions of VAR slope parameters and innovation variances under the alternative assumption that a sequence of finite‐lag order VAR models is fitted to data generated by a VAR process of possibly infinite order. This class of statistics includes measures of predictability and orthogonalized impulse responses and variance decompositions. Our approach provides an alternative to the use of the asymptotic normal approximation and can be used even in the absence of closed‐form solutions for the variance of the estimator. We illustrate the practical relevance of our findings for applied work, including the evaluation of macroeconomic models.

Suggested Citation

Inoue, Atsushi and Kilian, Lutz, Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR(∞) Models (May 2002). International Economic Review, Vol. 43, Issue 2, pp. 309-331, 2002. Available at SSRN: https://ssrn.com/abstract=2361551 or http://dx.doi.org/10.1111/1468-2354.t01-1-00016

Atsushi Inoue

Southern Methodist University ( email )

Dallas, TX 75275
United States

Lutz Kilian

University of Michigan at Ann Arbor - Department of Economics ( email )

611 Tappan Street
Ann Arbor, MI 48109-1220
United States
734-764-2320 (Phone)
734-764-2769 (Fax)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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