Bayesian Bandwidth Estimation In Nonparametric Time-Varying Coefficient Models

Tingting Cheng (), Jiti Gao and Xibin Zhang ()

No 3/15, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this paper, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time-varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown parameters involved in the error density. A Monte Carlo simulation study shows that (i) the proposed Bayesian estimators for bandwidths and parameters in the error density have satisfactory finite sample performance; and (ii) our proposed Bayesian approach achieves better performance in estimating the bandwidths than the normal reference rule and cross-validation. Moreover, we apply our proposed Bayesian bandwidth estimation method for the time-varying coefficient models that explain Okun's law and the relationship between consumption growth and income growth in the US. For each model, we also provide calibrated parametric forms of the time-varying coefficients.

Keywords: Local constant estimator; bandwidth; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C14 C15 (search for similar items in EconPapers)
Pages: 45
Date: 2015
New Economics Papers: this item is included in nep-ecm, nep-mfd and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Bayesian Bandwidth Estimation in Nonparametric Time-Varying Coefficient Models (2019)
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