Forecasting Macroeconomic Time Series with Locally Adaptive Signal Extraction

Riksbank Research Paper Series No. 65

Sveriges Riksbank Working Paper Series No. 234

23 Pages Posted: 8 Mar 2010

See all articles by Paolo Giordani

Paolo Giordani

Sveriges Riksbank - Research Division

Mattias Villani

Sveriges Riksbank - Research Division; Stockholm University - Department of Statistics

Date Written: October 2009

Abstract

We introduce a non-Gaussian dynamic mixture model for macroeconomic forecasting. The locally adaptive signal extraction and regression (LASER) model is designed to capture relatively persistent AR processes (signal) which are contaminated by high frequency noise. The distributions of the innovations in both noise and signal are modeled robustly using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift either suddenly or gradually at unknown locations and unknown numbers of times. The model is then capable of capturing movements in the mean and conditional variance of a series, as well as in the signal-to-noise ratio. Four versions of the model are estimated by Bayesian methods and used to forecast a total of nine quarterly macroeconomic series from the US, Sweden and Australia. We observe that allowing for infrequent and large parameter shifts while imposing normal and homoskedastic errors often leads to erratic forecasts, but that the model typically forecasts well if it is made more robust by allowing for non-normal errors and time varying variances. Our main finding is that, for the nine series we analyze, specifications with infrequent and large shifts in error variances outperform both fixed parameter specifications and smooth, continuous shifts when it comes to interval coverage.

Keywords: Bayesian inference, Forecast evaluation, Regime switching, State space modeling, Dynamic mixture models

Suggested Citation

Giordani, Paolo and Villani, Mattias, Forecasting Macroeconomic Time Series with Locally Adaptive Signal Extraction (October 2009). Sveriges Riksbank Working Paper Series No. 234. Available at SSRN: https://ssrn.com/abstract=1551203 or http://dx.doi.org/10.2139/ssrn.1551203

Paolo Giordani (Contact Author)

Sveriges Riksbank - Research Division ( email )

S-103 37 Stockholm
Sweden

HOME PAGE: http://www.riksbank.com/templates/Page.aspx?id=22013

Mattias Villani

Sveriges Riksbank - Research Division ( email )

S-103 37 Stockholm
Sweden

HOME PAGE: http://www.riksbank.com/research/villani

Stockholm University - Department of Statistics ( email )

Universitetsvägen 10
Stockholm, Stockholm SE-106 91
Sweden

HOME PAGE: http://www.riksbank.com/research/villani

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