Home > Research > Publications & Outputs > Hierarchical shrinkage in time-varying paramete...

Research

Associated organisational unit

Links

Text available via DOI:

Keywords

View graph of relations

Hierarchical shrinkage in time-varying parameter models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Hierarchical shrinkage in time-varying parameter models. / Gonzalez Belmonte, Miguel Angel; Koop, Gary; Korobilis, Dimitris.
In: Journal of Forecasting, Vol. 33, No. 1, 01.2014, p. 80–94.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Gonzalez Belmonte, MA, Koop, G & Korobilis, D 2014, 'Hierarchical shrinkage in time-varying parameter models', Journal of Forecasting, vol. 33, no. 1, pp. 80–94. https://doi.org/10.1002/for.2276

APA

Gonzalez Belmonte, M. A., Koop, G., & Korobilis, D. (2014). Hierarchical shrinkage in time-varying parameter models. Journal of Forecasting, 33(1), 80–94. https://doi.org/10.1002/for.2276

Vancouver

Gonzalez Belmonte MA, Koop G, Korobilis D. Hierarchical shrinkage in time-varying parameter models. Journal of Forecasting. 2014 Jan;33(1):80–94. Epub 2013 Dec 19. doi: 10.1002/for.2276

Author

Gonzalez Belmonte, Miguel Angel ; Koop, Gary ; Korobilis, Dimitris. / Hierarchical shrinkage in time-varying parameter models. In: Journal of Forecasting. 2014 ; Vol. 33, No. 1. pp. 80–94.

Bibtex

@article{d6fdce91d1f04175b6ad78e1f9d5ff83,
title = "Hierarchical shrinkage in time-varying parameter models",
abstract = "In this paper, we forecast EU area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach",
keywords = "forecasting, hierarchical prior, Bayesian Lasso, time-varying parameters",
author = "{Gonzalez Belmonte}, {Miguel Angel} and Gary Koop and Dimitris Korobilis",
year = "2014",
month = jan,
doi = "10.1002/for.2276",
language = "English",
volume = "33",
pages = "80–94",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Hierarchical shrinkage in time-varying parameter models

AU - Gonzalez Belmonte, Miguel Angel

AU - Koop, Gary

AU - Korobilis, Dimitris

PY - 2014/1

Y1 - 2014/1

N2 - In this paper, we forecast EU area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach

AB - In this paper, we forecast EU area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: (i) time varying; (ii) constant over time; or (iii) shrunk to zero. The econometric methodology decides automatically to which category each coefficient belongs. Our empirical results indicate the benefits of such an approach

KW - forecasting

KW - hierarchical prior

KW - Bayesian Lasso

KW - time-varying parameters

U2 - 10.1002/for.2276

DO - 10.1002/for.2276

M3 - Journal article

VL - 33

SP - 80

EP - 94

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 1

ER -