Asymptotic F test in Regressions with Observations Collected at High Frequency over Long Span

Daniel Pellatt and Yixiao Sun

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

Abstract: This paper proposes tests of linear hypotheses when the variables may be continuous-time 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 endogenous regressors that are general semimartinglales. The F tests can be implemented in exactly the same way as in the usual discrete-time setting. The F tests are, therefore, robust to the continuous-time 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. (2018) 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.

Keywords: Social and Behavioral Sciences; continuous time model; F distribution; high frequency regression; long run variance estimation (search for similar items in EconPapers)
Date: 2020-10-29
New Economics Papers: this item is included in nep-ecm and nep-ets
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