In this paper, we write the timevarying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the timevariation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
Original language  English 

Journal  arXiv 

Publication status  Published  23 Oct 2019 

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@article{a4cc90cc77e8492da79c843556ba439c,
title = "Fast and Flexible Bayesian Inference in Timevarying Parameter Regression Models",
abstract = "In this paper, we write the timevarying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the timevariation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.",
keywords = "econ.EM, stat.CO",
author = "Niko Hauzenberger and Florian Huber and Gary Koop and Luca Onorante",
year = "2019",
month = "10",
day = "23",
language = "English",
}
TY  JOUR
T1  Fast and Flexible Bayesian Inference in Timevarying Parameter Regression Models
AU  Hauzenberger, Niko
AU  Huber, Florian
AU  Koop, Gary
AU  Onorante, Luca
PY  2019/10/23
Y1  2019/10/23
N2  In this paper, we write the timevarying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the timevariation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
AB  In this paper, we write the timevarying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the timevariation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
KW  econ.EM
KW  stat.CO
M3  Article
ER 