Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction
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) contaminated by high frequency noise. The distribution of the innovations in both noise and signal is robustly modeled using mixtures of normals. The mean of the process and the variances of the signal and noise are allowed to shift suddenly or gradually at unknown locations and number 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 used to forecast six quarterly US and Swedish macroeconomic series. We conclude that (i) allowing for infrequent and large shifts in mean while imposing normal iid errors often leads to erratic forecasts, (ii) such shifts/breaks versions of the model can forecast well if robustified by allowing for non-normal errors and time varying variances, (iii) infrequent and large shifts in error variances outperform smooth and continuous shifts substantially when it comes to interval coverage, (iv) for point forecasts, robust time varying specifications improve slightly upon fixed parameter specifications on average, but the relative performances can differ sizably in various sub-samples, v) for interval forecasts, robust versions that allow for infrequent shifts in variances perform substantially and consistently better than time invariant specifications.
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- Giordani, Paolo & Kohn, Robert, 2008.
"Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 26, pages 66-77, January.
- Giordani, Paolo & Kohn, Robert, 2006. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Working Paper Series 196, Sveriges Riksbank (Central Bank of Sweden).
- Gary Koop & Simon M. Potter, 2007.
"Prior Elicitation in Multiple Change-point Models,"
Working Paper Series
17-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
- Gary Koop & Simon M. Potter, 2004. "Prior Elicitation in Multiple Change-point Models," Discussion Papers in Economics 04/26, Department of Economics, University of Leicester.
- Gary M. Koop & Simon M. Potter, 2004. "Prior elicitation in multiple change-point models," Staff Reports 197, Federal Reserve Bank of New York.
- Granger, Clive W.J. & Hyung, Namwon, 1999.
"Occasional Structural Breaks and Long Memory,"
University of California at San Diego, Economics Working Paper Series
qt4d60t4jh, Department of Economics, UC San Diego.
- Marie Bessec & Othman Bouabdallah, 2005.
"What causes the forecasting failure of Markov-Switching models? A Monte Carlo study,"
- Bessec Marie & Bouabdallah Othman, 2005. "What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-24, June.
- James H. Stock & Mark W. Watson, 2007.
"Why Has U.S. Inflation Become Harder to Forecast?,"
Journal of Money, Credit and Banking,
Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
- James H. Stock & Mark W. Watson, 1994.
"Evidence on Structural Instability in Macroeconomic Time Series Relations,"
NBER Technical Working Papers
0164, National Bureau of Economic Research, Inc.
- Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
- James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
- Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
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