Tests for Random Coefficient Variation in Vector Autoregressive Models

Dante Amengual, Gabriele Fiorentini and Enrique Sentana

A chapter in Essays in Honour of Fabio Canova, 2022, vol. 44B, pp 1-35 from Emerald Group Publishing Limited

Abstract: The authors propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions (VAR). They additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Their Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Their procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients.

Keywords: Gross domestic product; gross domestic income; Hessian matrix; information matrix test; outer product of the score; C32; C52; E01 (search for similar items in EconPapers)
Date: 2022
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Related works:
Working Paper: Tests for random coefficient variation in vector autoregressive models (2021)
Working Paper: Tests for random coefficient variation in vector autoregressive models (2021)
Working Paper: Tests for random coefficient variation in vector autoregressive models (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532022000044b001

DOI: 10.1108/S0731-90532022000044B001

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