A weighted sieve estimator for nonparametric time series models with nonstationary variables

Chaohua Dong, Oliver Linton, Bin Peng

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We study a class of nonparametric regression models that includes deterministic time trends and both stationary and nonstationary stochastic processes (whose shocks are allowed to be mutually correlated). We propose a unified approach to estimation based on the weighted sieve method to tackle the issue of unbounded support of the covariates. This approach improves on the existing technology in terms of some key regularity conditions such as moment conditions and the α-mixing coefficients for the stationary process. We establish self-normalized central limit theorems for the sieve estimator and other related quantities. Monte Carlo simulation confirms the theoretical results. We use our methodology to study the effect of CO2 and solar irradiance on global sea level rise.

Original languageEnglish
Pages (from-to)909-932
Number of pages24
JournalJournal of Econometrics
Volume222
Issue number2
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Nonparametric regression
  • Nonstationary variable
  • Sieve estimation
  • Stationary variable
  • Time trend
  • Unbounded support
  • Weighted least squares

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