Threshold effect in varying coefficient models with unknown heteroskedasticity

This paper extends the threshold regression to threshold effect in varying coefficient model. We allow for either cross-section or time series observations. Estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the re...

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Veröffentlicht in:Computational statistics 2024-05, Vol.39 (3), p.1165-1181
Hauptverfasser: Zhang, Yuanqing, Ai, Chunrong, Feng, Yaqin
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description This paper extends the threshold regression to threshold effect in varying coefficient model. We allow for either cross-section or time series observations. Estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. The distribution of threshold estimates is found to be non-standard. Under some sufficient conditions, we show that the proposed estimator for regression slopes is root-n consistent and asymptotically normally distributed, and that the proposed estimator for the varying coefficient is consistent and also asymptotically normal distributed but at a rate slower than root-n. Consistent estimators for the asymptotic variances of the proposed estimators are provided. Monte Carlo simulations are presented to assess the performance of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the relationship between environmental regulation and regional technological innovation study.
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subjects Asymptotic properties
Coefficients
Data analysis
Economic Theory/Quantitative Economics/Mathematical Methods
Estimates
Estimators
Mathematics and Statistics
Monte Carlo simulation
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Regression
Statistics
Time series
title Threshold effect in varying coefficient models with unknown heteroskedasticity
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