4.1 Article

Testing for the sandwich-form covariance matrix of the quasi-maximum likelihood estimator

期刊

TEST
卷 30, 期 2, 页码 293-317

出版社

SPRINGER
DOI: 10.1007/s11749-020-00719-x

关键词

Information matrix equality; Sandwich-form covariance matrix; Heteroskedasticity-consistent covariance matrix estimator; Heteroskedasticity and autocorrelation-consistent covariance matrix estimator

资金

  1. Ministry of Education in the Republic of Korea
  2. National Research Foundation of Korea [NRF-2018S1A5A2A01035256]
  3. National Natural Science Foundation of China [71803009]
  4. MOE Project of Humanities and Sciences [17YJC790057]
  5. National Research Foundation of Korea [2018S1A5A2A01035256] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

This study tests the sandwich-form asymptotic covariance matrices under conditionally heteroskedastic and/or autocorrelated regression errors, providing a testing methodology to detect their influence. A sequential testing procedure is established to achieve the research goal, confirming the theory through simulation and empirical data.
This study tests for the sandwich-form asymptotic covariance matrices entailed by conditionally 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 supposing a possibly misspecified model for error distribution. Accordingly, we provide test methodologies by extending the approaches in Cho and White (in: Chang Y, Fomby T, Park JY (eds) Advances in econometrics: essays in honor of Peter CB Phillips. Emerald Group Publishing Limited, West Yorkshire, 2014) and Cho and Phillips (J Econ 202:45-56, 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.

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