Maheu, John and Song, Yong (2012): A new structural break model with application to Canadian inflation forecasting.
Download (482kB) | Preview
This paper develops an efficient approach to model and forecast time-series data with an unknown number of change-points. Using a conjugate prior and conditional on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. Regime duration can be modelled as a Poisson distribution. An new efficient Markov Chain Monte Carlo sampler draws the parameters as one block from the posterior distribution. An application to Canada inflation time series shows the gains in forecasting precision that our model provides.
|Item Type:||MPRA Paper|
|Original Title:||A new structural break model with application to Canadian inflation forecasting|
|Keywords:||multiple change-points; regime duration; inflation targeting; predictive density; MCMC|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Yong Song|
|Date Deposited:||24. Feb 2012 14:19|
|Last Modified:||14. Sep 2015 05:23|
Casella, G. and Robert, C.P. Rao-Blackwellisation of sampling schemes. Biometrika, 83(1): 81, 1996.
Chib, S. Calculating posterior distributions and modal estimates in Markov mixture models*1. Journal of Econometrics, 75(1):79-97, 1996.
Clark, Todd E. and McCracken, Michael W. Averaging forecasts from vars with uncertain instabilities. Journal of Applied Econometrics, 25(1):5-29, 2010.
Engle, R.F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4): 987-1007, 1982.
Engle, R.F. Estimates of the Variance of US Inflation Based upon the ARCH Model. Journal of Money, Credit and Banking, 15(3):286-301, 1983.
Freedman, Charles. Monetary policy formulation: The process in canada. Business Economics, pages 52-56, 2001.
Geweke, J. and Amisano, G. Comparing and evaluating Bayesian predictive distributions of asset returns. International Journal of Forecasting, 2010.
Geweke, John and Jiang, Yu. Inference and prediction in a multiple-structural-break model. Journal of Econometrics, 163(2):172-185, 2011.
Giordani, Paolo, Kohn, Robert, and van Dijk, Dick. A unied approach to nonlinearity, structural change, and outliers. Journal of Econometrics, 137(1):112-133, 2007.
Kass, R.E. and Raftery, A.E. Bayes factors. Journal of the American Statistical Association, 90(430):773-795, 1995.
Koop, G. and Potter, S.M. Estimation and forecasting in models with multiple breaks. Review of Economic Studies, 74(3):763-789, 2007.
Liu, Chun and Maheu, John M. Are there structural breaks in realized volatility? Journal of Financial Econometrics, 6(3):326-360, 2008.
Maheu, J.M. and Gordon, S. Learning, forecasting and structural breaks. Journal of Applied Econometrics, 23(5):553-583, 2008.
Maheu, J.M. and McCurdy, T.H. How useful are historical data for forecasting the long-run equity return distribution? Journal of Business and Economic Statistics, 27(1):95-112, 2009.
Pesaran, M.H., Pettenuzzo, D., and Timmermann, A. Forecasting time series subject to multiple structural breaks. Review of Economic Studies, 73(4):1057-1084, 2006.
Stock, J.H. and Watson, M.W. Evidence on structural instability in macroeconomic time series relations. Journal of Business & Economic Statistics, 14(1):11-30, 1996.
Wang, J. and Zivot, E. A Bayesian time series model of multiple structural changes in level, trend, and variance. Journal of Business & Economic Statistics, 18(3):374-386, 2000.