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Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction

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Author Info

  • Giordani, Paolo

    () (Research Department, Central Bank of Sweden)

  • Villani, Mattias

    (Research Department, Central Bank of Sweden)

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) 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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Bibliographic Info

Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 234.

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Length: 26 pages
Date of creation: 01 Oct 2009
Date of revision:
Handle: RePEc:hhs:rbnkwp:0234

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Related research

Keywords: Bayesian inferene; Foreast evaluation; Regime swithing; State-space modeling; Dynamic Mixture models;

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References

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  1. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, EconWPA.
  2. 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.
  3. 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.
  4. 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.
  5. 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).
  6. 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.
  7. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
  8. 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.
  9. 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.
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Cited by:
  1. 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.
  2. 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.
  3. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
  4. 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.

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