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Müller looks at the problems of interval estimation and hypothesis testing in autocorrelated models from a frequency domain point of view. This leads to good insights as well as proposals for new m...
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Improving robust model selection tests for dynamic models
改进动态模型的稳健模型选择检验
We propose an improved model selection test for dynamic models using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria and estimation methods. Since our test statistic does not assume the exact form of a true model, the test is essentially non-parametric once competing models are estimated. For the unknown serial correlation in data, we use a…
A NEW ASYMPTOTIC THEORY FOR HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTS
异方差与自相关稳健检验的新渐近理论
A new first-order asymptotic theory for heteroskedasticity-autocorrelation (HAC) robust tests based on nonparametric covariance matrix estimators is developed. The bandwidth of the covariance matrix estimator is modeled as a fixed proportion of the sample size. This leads to a distribution theory for HAC robust tests that explicitly captures the choice of bandwidth and kernel. This contrasts with the traditional asymptotics (where the bandwidth increases more slowly than the…
Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation
使用不截断Bartlett核的异方差与自相关稳健标准误
In this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. We show that while such an HAC estimator is not consistent, asymptotically valid testing is still possible. We show that tests using the Bartlett kernel without truncation are exactly equivalent to recent HAC robust tests proposed by Kiefer, Vogelsang and Bunzel (2000, Econometrica, 68, pp 695-714).
Local Asymptotic Specification Error Analysis
局部渐近设定误差分析
An approximation to the inconsistency introduced by imposing an incorrect restriction on a parametric model is given. The approximation can be applied to estimators generated by optimizing any objective function satisfying certain regularity conditions. Examples given include analysis of misspecification in discrete choice and time-series models estimated by maximum likelihood, and in a nonlinear regression model. SPECIFICATION ERROR ANALYSIS in the linear regression model…