Highly Irregular Serial Correlation Tests

Dante Amengual (amengual@cemfi.es), Xinyue Bei (xinyue.bei@duke.edu) and Enrique Sentana
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Dante Amengual: CEMFI, Centro de Estudios Monetarios y Financieros, https://www.cemfi.es/
Xinyue Bei: Duke University, https://duke.edu/

Working Papers from CEMFI

Abstract: We develop tests for neglected serial correlation when the information matrix is repeatedly singular under the null. Specifically, we consider white noise against a multiplicative seasonal AR model, and a local-level model against a nesting UCARIMA one. Our proposals, which involve higher-order derivatives, are asymptotically equivalent to the likelihood ratio test but only require estimation under the null. Remarkably, we show that our proposed tests effectively check that certain autocorrelations of the observations are 0, so their asymptotic distribution is standard. We conduct Monte Carlo exercises that study their finite sample size and power properties, comparing them to alternative approaches.

Keywords: Generalized extremum tests; higher-order identifiability; likelihood ratio test. (search for similar items in EconPapers)
JEL-codes: C12 C22 C32 C52 (search for similar items in EconPapers)
Date: 2023-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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