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Forecasting macroeconomic time series with locally adaptive signal extraction

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  • Giordani, Paolo
  • Villani, Mattias

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.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Pages: 312-325

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Handle: RePEc:eee:intfor:v:26:y::i:2:p:312-325

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

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

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References

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  1. 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.
  2. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, EconWPA.
  3. 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.
  4. Clive W.J. Granger & Namwon Hyung, 2013. "Occasional Structural Breaks and Long Memory," Annals of Economics and Finance, Society for AEF, vol. 14(2), pages 739-764, November.
  5. 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.
  6. 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.
  7. 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.
  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. 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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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 1218, Federal Reserve Bank of Cleveland.
  2. Liu, Yuelin & Morley, James, 2014. "Structural evolution of the postwar U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 42(C), pages 50-68.
  3. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
  4. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, 08.
  5. 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.
  6. 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.

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