Forecasting macroeconomic time series with locally adaptive signal extraction
AbstractWe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Journal of Forecasting.
Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Contact details of provider:
Web page: http://www.elsevier.com/locate/ijforecast
Bayesian inference Forecast evaluation Regime switching State space modeling Dynamic mixture models;
Other versions of this item:
- Giordani, Paolo & Villani, Mattias, 2009. "Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction," Working Paper Series 234, Sveriges Riksbank (Central Bank of Sweden).
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Bouabdallah, Othman & Bessec, Marie, 2005. "What causes the forecasting failure of Markov-switching models ? A Monte Carlo study," Open Access publications from UniversitÃ© Paris-Dauphine urn:hdl:123456789/6064, Université Paris-Dauphine.
- 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.
- 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 Time Series Relations," NBER Technical Working Papers 0164, National Bureau of Economic Research, Inc.
- James H. Stock & Mark W. Watson, 2006.
"Why Has U.S. Inflation Become Harder to Forecast?,"
NBER Working Papers
12324, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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 M. Koop & Simon M. Potter, 2004. "Prior elicitation in multiple change-point models," Staff Reports 197, Federal Reserve Bank of New York.
- 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.
- 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).
- 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.
- Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
- Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2013.
"Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility,"
CEPR Discussion Papers
9312, C.E.P.R. Discussion Papers.
- Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.
- Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," SÃ©rie Textos para DiscussÃ£o (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
- Bulkley, George & Giordani, Paolo, 2011. "Structural breaks, parameter uncertainty, and term structure puzzles," Journal of Financial Economics, Elsevier, vol. 102(1), pages 222-232, October.
- Todd E. Clark & Francesco Ravazzolo, 2012.
"The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility,"
1218, Federal Reserve Bank of Cleveland.
- Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wendy Shamier).
If references are entirely missing, you can add them using this form.