Locally time-varying parameter regression

I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters...

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Bibliographic Details
Published inEconometric reviews Vol. 43; no. 5; pp. 269 - 300
Main Author He, Zhongfang
Format Journal Article
LanguageEnglish
Published New York Taylor & Francis 27.05.2024
Taylor & Francis Ltd
Subjects
Bayesian shrinkage
Econometrics
economic time series
Innovations
MCMC
Parameters
Regression models
TVP
Online AccessGet full text
ISSN0747-4938
1532-4168
DOI10.1080/07474938.2024.2330127

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Summary:I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters to mix over different frequencies of parameter changes in a data driven way and permits great flexibility while achieving model parsimony. A convenient strategy is discussed to infer if each coefficient is static or dynamic and, if dynamic, how frequent the parameter change is. An MCMC scheme is developed for model estimation. The performance of the proposed approach is illustrated in studies of both simulated and real economic data.
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ISSN:0747-4938
1532-4168
DOI:10.1080/07474938.2024.2330127