Asymptotic F test in regressions with observations collected at high frequency over long span
Journal of Econometrics(2022)
摘要
This paper proposes tests of linear hypotheses when the variables may be continuoustime processes with observations collected at a high sampling frequency over a long span. Utilizing series long run variance (LRV) estimation in place of the traditional kernel LRV estimation, we develop easy-to-implement and more accurate F tests in both stationary and nonstationary environments. The nonstationary environment accommodates exogenous regressors that are general semimartingales. Endogenous regressors are allowed in a nonstationary environment similar to cointegration models in the usual discrete-time setting. The F tests can be implemented in exactly the same way as in the discrete-time setting. The F tests are, therefore, robust to the continuoustime or discrete-time nature of the data. Simulations demonstrate the improved size accuracy and competitive power of the F tests relative to existing continuous-time testing procedures and their improved versions. The F tests are of practical interest as recent work by Chang et al. (2021) demonstrates that traditional inference methods can become invalid and produce spurious results when continuous-time processes are observed on finer grids over a long span. & COPY; 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC
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关键词
C12,C13,C22
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