Semiparametric transition models
Cizek,Pavel Koo,Chao Hui
Cizek,Pavel
Koo,Chao Hui
Abstract
A new semiparametric time series model is introduced - the semiparametric transition (SETR) model - that generalizes the threshold and smooth transition models by letting the transition function to be of an unknown form. Estimation is based on a combination of the (local) least squares estimations of the transition function and regression parameters. The asymptotic behavior for the regression coefficient estimator of the SETR model is established, including its oracle property. Monte Carlo simulations demonstrate that the proposed estimator is more robust to the form of the transition function than parametric threshold and smooth transition methods and more precise than varying coefficient estimators.
Description
Publisher Copyright: © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.
Date
2022-10
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Local linear estimation, nonlinear time series, semiparametric estimation, regime-switching models, REGRESSION-MODELS, COEFFICIENT, NONLINEARITIES, C13 - Estimation: General, C14 - Semiparametric and Nonparametric Methods: General, C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
Citation
Cizek, P & Koo, C H 2022, 'Semiparametric transition models', Econometric Reviews, vol. 41, no. 4, pp. 400-415. https://doi.org/10.1080/07474938.2021.1957281
License
info:eu-repo/semantics/openAccess