Fan, G.L., Liang, H.Y. and Wang, J.F. (2013) Statistical Inference for Partially Time-Varying Coefficient Errors-in-Variables Models. Journal of Statistical Planning and Inference, 143, 505-519.
https://doi.org/10.1016/j.jspi.2012.08.017

has been cited by the following article:

• TITLE: Partial Time-Varying Coefficient Regression and Autoregressive Mixed Model

  AUTHORS: Hui Li, Zhiqiang Cao

  KEYWORDS: Regression and Autoregressive, Time Series, Partial Time-Varying Coefficient, Local Polynomial

  JOURNAL NAME: Open Journal of Statistics, Vol.13 No.4, August 11, 2023

  ABSTRACT: Regression and autoregressive mixed models are classical models used to analyze the relationship between time series response variable and other covariates. The coefficients in traditional regression and autoregressive mixed models are constants. However, for complicated data, the coefficients of covariates may change with time. In this article, we propose a kind of partial time-varying coefficient regression and autoregressive mixed model and obtain the local weighted least-square estimators of coefficient functions by the local polynomial technique. The asymptotic normality properties of estimators are derived under regularity conditions, and simulation studies are conducted to empirically examine the finite-sample performances of the proposed estimators. Finally, we use real data about Lake Shasta inflow to illustrate the application of the proposed model.