Korobilis, D (2017) Quantile regression forecasts of inflation under model uncertainty. International Journal of Forecasting, 33 (1). pp. 11-20. DOI https://doi.org/10.1016/j.ijforecast.2016.07.005
Korobilis, D (2017) Quantile regression forecasts of inflation under model uncertainty. International Journal of Forecasting, 33 (1). pp. 11-20. DOI https://doi.org/10.1016/j.ijforecast.2016.07.005
Korobilis, D (2017) Quantile regression forecasts of inflation under model uncertainty. International Journal of Forecasting, 33 (1). pp. 11-20. DOI https://doi.org/10.1016/j.ijforecast.2016.07.005
Abstract
This paper examines the performance of Bayesian model averaging (BMA) methods in a quantile regression model for inflation. Different predictors are allowed to affect different quantiles of the dependent variable. Based on real-time quarterly data for the US, we show that quantile regression BMA (QR-BMA) predictive densities are superior and better calibrated compared to those from BMA in the traditional regression model. Additionally, QR-BMA methods compare favorably to popular nonlinear specifications for US inflation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian model averaging; Quantile regression; Inflation forecasts; Fan charts |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 23 Nov 2016 12:13 |
| Last Modified: | 30 Oct 2024 17:34 |
| URI: | http://repository.essex.ac.uk/id/eprint/17964 |
Available files
Filename: QR-BMA.pdf