Tests for random coefficient variation in vector autoregressive models

Dante Amengual, Gabriele Fiorentini and Enrique Sentana

No 2021_18, Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

Abstract: We propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions. We additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Our Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Our 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: GDP; GDI; Hessian matrix; Information matrix test; Outer product of the score (search for similar items in EconPapers)
JEL-codes: C32 C52 E01 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2021-10
New Economics Papers: this item is included in nep-cwa, nep-mac and nep-ore
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https://labdisia.disia.unifi.it/wp_disia/2021/wp_disia_2021_18.pdf First version, 2021-10 (application/pdf)

Related works:
Chapter: Tests for Random Coefficient Variation in Vector Autoregressive Models (2022)
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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