Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309999 
Year of Publication: 
2024
Series/Report no.: 
Working Paper No. 985
Publisher: 
Queen Mary University of London, School of Economics and Finance, London
Abstract: 
This paper explores a semiparametric version of a time-varying regression, where a subset of the regressors have a fixed coefficient and the rest a time-varying one. We provide an estimation method and establish associated theoretical properties of the estimates and standard errors in extended for heterogeneity regression space. In particular, we show that the estimator of the fixed regression coefficient preserves the parametric rate of convergence, and that, despite of general heterogenous environment, the asymptotic normality property for components of regression parameters can be established and the estimators of standard errors have the same form as those given by White (1980). The theoretical properties of the estimator and good finite sample performance are confirmed by Monte Carlo experiments and illustrated by an empirical example on forecasting.
Subjects: 
structural change
time-varying parameters
non-parametric estimation
JEL: 
C13
C14
C50
Document Type: 
Working Paper

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