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.

这项研究测试了由条件异方差和/或自相关回归误差或条件不相关同方误差引起的三明治形式渐近协方差矩阵。通过这样做，我们使经验研究者可以通过假设可能错误指定的误差分布模型来估计拟最大似然估计量的渐近协方差矩阵。因此，我们通过扩展Cho and White中的方法来提供测试方法（在：Chang Y，Fomby T，Park JY（编辑）计量经济学的进展：纪念Peter CB Phillips的论文。EmeraldGroup Publishing Limited，西约克郡，2014年）和Cho和Phillips（J Econ 202：45–56，2018a）来检测异方差和/或自相关回归误差对渐近协方差矩阵的影响。尤其是，我们建立了顺序测试程序以实现我们的目标。我们通过仿真对测试统计量的理论进行了确认，并将其应用于能源价格增长率数据中，以进行说明。在这里，我们还应用测试方法来测试完全正确的模型假设。