JinSeo.Cho - Testing for the Sandwich-Form Covariance Matrix of the Quasi-Maximum Likelihood Estimator

<Source Information> Lijuan Huo and Jin Seo Cho (2021): Test, 30, 293--317.

<Abstract> This study tests for the sandwich-form asymptotic covariance matrices entailed by conditional heteroskedastic and/or autocorrelated regression errors or conditionally uncorrelated homoskedastic errors. In doing so, we enable the empirical researcher to estimate the asymptotic covariance matrix of the quasi-maximum likelihood estimator by a proper estimator by supposing a possibly misspecified model for error distribution. Accordingly, we provide test methodologies by extending the approaches in Cho and White (2014) and Cho and Phillips (2018a) to detect the influence of heteroskedastic and/or autocorrelated regression errors on the asymptotic covariance matrix. In particular, we establish a sequential testing procedure to achieve our goal. We affirm the theory on our test statistics through simulation and apply the test statistics to energy price growth rate data for illustrative purposes; here, we also apply our test methodology to test the fully correct model hypothesis.

<Paper Download> JSCHO_COSPIMT_04-07-20.pdf

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