Uniform and Lp convergences for nonparametric continuous time regressions with semiparametric applications
Ruijun Bu,
Jihyun Kim and
Bin Wang
Journal of Econometrics, 2023, vol. 235, issue 2, 1934-1954
Abstract:
We obtain uniform and Lp convergence rates of kernel type nonparametric estimators for the instantaneous conditional mean and variance functions of continuous time regressions, where the regressor is assumed to be a general recurrent diffusion. Our asymptotics are developed under a general set-up, with a shrinking sampling interval and an increasing time span, and without the stationarity assumption. Based on our convergence results, we develop a semiparametric inferential procedure for continuous time predictive regressions. In particular, a robust semiparametric likelihood ratio test for linear predictability is proposed, with its limit distribution established. We also apply our convergence results to obtain the asymptotics of a semiparametric maximum likelihood estimator of the drift of recurrent diffusions. In our simulation study, we examine the finite sample performance of our robust test against several existing tests in the literature. An empirical illustration is presented to test the predictability of the excess returns of two major stock indices using the commonly used dividend–price ratio and earnings–price ratio as the predictor.
Keywords: Recurrent diffusion; Kernel estimation; Uniform convergence; Lp convergence; Non/semiparametric model; Predictability; Robust inference (search for similar items in EconPapers)
JEL-codes: C14 C22 C58 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:235:y:2023:i:2:p:1934-1954
DOI: 10.1016/j.jeconom.2023.02.006
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