State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models
Essays in Honour of Fabio Canova
ISBN: 978-1-80382-636-3, eISBN: 978-1-80382-635-6
Publication date: 16 September 2022
Abstract
This chapter investigates the impact of different state correlation assumptions for out-of-sample performance of unobserved components (UC) models with stochastic volatility. Using several measures of US inflation the author finds that allowing for correlation between inflation’s trend and cyclical (or gap) components is a useful feature to predict inflation in the short run. In contrast, orthogonality between such components improves the out-of-sample performance as the forecasting horizon widens. Accordingly, trend inflation from orthogonal trend-gap UC models closely tracks survey-based measures of long-run inflation expectations. Trend dynamics in the correlated-component case behave similarly to survey-based nowcasts. To carry out estimation, an efficient algorithm which builds upon properties of Toeplitz matrices and recent advances in precision-based samplers is provided.
Keywords
Acknowledgements
Acknowledgements
For comments and suggestions, I would like to thank Fabio Canova, Joshua Chan, Siddhartha Chib, Juan Dolado, Catherine Forbes, Alfred Haug, Jan Jacobs, James Morley, Rodney Strachan and Tomasz Wozniak. I also thank seminar and conference participants at the 3rd Forecasting at Central Banks Conference, 2018 NBER SBIES, 2016 Australasia Meeting of the Econometric Society, the 2nd Workshop of the Australasian Macroeconomic Society, the 2nd Continuing Education in Macroeconometrics Workshop at the University of Tasmania, the Australian National University and the 28th PhD in Economics and Business Conference at the University of Queensland. The views expressed in this chapter are mine and do not necessarily reflect the position of the Bank of Canada.
Citation
Uzeda, L. (2022), "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models", Dolado, J.J., Gambetti, L. and Matthes, C. (Ed.) Essays in Honour of Fabio Canova (Advances in Econometrics, Vol. 44A), Emerald Publishing Limited, Leeds, pp. 25-53. https://doi.org/10.1108/S0731-90532022000044A003
Publisher
:Emerald Publishing Limited
Copyright © 2022 Luis Uzeda