A Fixed-bandwidth View of the Pre-asymptotic Inference for Kernel Smoothing with Time Series Data

Min Seong Kim, Yixiao Sun and Jingjing Yang

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

Abstract: This paper develops robust testing procedures for nonparametric kernel methods in the presence of temporal dependence of unknown forms. Based on the fixed-bandwidth asymptotic variance and the pre-asymptotic variance, we propose a heteroskedasticity and autocorrelation robust (HAR) variance estimator that achieves double robustness --- it is asymptotically valid regardless of whether the temporal dependence is present or not, and whether the kernel smoothing bandwidth is held constant or allowed to decay with the sample size. Using the HAR variance estimator, we construct the studentized test statistic and examine its asymptotic properties under both the fixed-smoothing and increasing-smoothing asymptotics. The fixed-smoothing approximation and the associated convenient t-approximation achieve extra robustness --- it is asymptotically valid regardless of whether the truncation lag parameter governing the covariance weighting grows at the same rate as or a slower rate than the sample size. Finally, we suggest a simulation-based calibration approach to choose smoothing parameters that optimize testing oriented criteria. Simulation shows that the proposed procedures work very well in finite samples.

Keywords: Social and Behavioral Sciences; heteroskedasticity and autocorrelation robust variance; calibration; fixed-smoothing asymptotics; fixed-bandwidth asymptotics; kernel density estimator; local polynomial estimator; t-approximation; testing-optimal smoothing-parameters choice; temporal dependence (search for similar items in EconPapers)
Date: 2016-01-04
New Economics Papers: this item is included in nep-ets
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Related works:
Journal Article: A fixed-bandwidth view of the pre-asymptotic inference for kernel smoothing with time series data (2017)
Working Paper: A Fixed-bandwidth View of the Pre-asymptotic Inference for Kernel Smoothing with Time Series Data (2015)
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