Abstract
In practical applications, lots of data such as sequentially collected economic data often exhibit some evident dependence. This paper studies the varying-coefficient regression models with different smoothing variables when the data form a stationaryα-mixing sequence. Both the averaged and integrated estimators of coefficient functions are proposed. The asymptotic normalities of the proposed averaged and integrated estimators are also established.
Highlights
Regression analysis is one of the most mature and widely applied branches of statistics
This paper studies the varying-coefficient regression models with different smoothing variables when the data form a stationary αmixing sequence
Various regression models have been studied by many authors; e.g., Liang and Fan [1] studied Berry-Esseen type bounds of estimators in a semiparametric model with linear process errors; Fan, Liang, and Xu [2] considered empirical likelihood confidence regions for heteroscedastic partial linear model and so on
Summary
Regression analysis is one of the most mature and widely applied branches of statistics. The varying-coefficient regression models on which different coefficient functions share different variables have been given less attention because the local polynomial technique is not adequate. Discrete Dynamics in Nature and Society is defined by averaging its initial value on these variables which the coefficient function does not share; Yang [10] proposed estimators for this model under random right censoring case by using mean-preserving transformation and established their asymptotic properties. Fan et al [11] studied the varying-coefficient errors-in-variables models when the data form a stationary α-mixing sequence of random variables.
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