Hypothesis tests with a repeatedly singular information matrix

Enrique Sentana, Dante Amengual and Xinyue Bei

No 14415, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We study score-type tests in likelihood contexts in which the nullity of the information matrix under the null is larger than one, thereby generalizing earlier results in the literature. Examples include multivariate skew normal distributions, Hermite expansions of Gaussian copulas, purely non-linear predictive regressions, multiplicative seasonal time series models and multivariate regression models with selectivity. Our proposal, which involves higher order derivatives, is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. We conduct extensive Monte Carlo exercises that study the finite sample size and power properties of our proposal and compare it to alternative approaches.

Keywords: Generalized extremum tests; Higher-order identifiability; Likelihood ratio test; Non-gaussian copulas; Predictive regressions; Skew normal distributions (search for similar items in EconPapers)
JEL-codes: C12 C22 C34 C46 C58 (search for similar items in EconPapers)
Date: 2020-02
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (3)

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Working Paper: Hypothesis Tests with a Repeatedly Singular Information Matrix (2020)
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