Forecasting Macroeconomic Variables Under Model Instability

REFERENCES

• Amisano, G., and Giacomini, R. (2007), “Comparing Density Forecasts via Weighted Likelihood Ratio Tests,” Journal of Business & Economic Statistics, 25, 177–190.
   Web of Science ®Google Scholar
• Andrews, D. W. K., and Monahan, J. D.1992An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix EstimatorEconometrica60953–966
   Web of Science ®Google Scholar
• Banbura, M., Giannone, D., and Reichlin, L. (2010), “Large Bayesian Vector Auto Regressions,” Journal of Applied Econometrics, 25, 71–92.
   Web of Science ®Google Scholar
• Bauwens, L., Koop, G., Korobilis, D., and Rombouts, J.V. (2014), “The Contribution of Structural Break Models to Forecasting Macroeconomic Series,” Journal of Applied Econometrics, 30596--620.
   Web of Science ®Google Scholar
• Chauvet, M., and Potter, S. (2013), “Forecasting Output,” in Handbook of Economic Forecasting ( Vol. 2, Part A), eds. G. Elliott and A. Timmermann, Amsterdam: North Holland, pp. 141–194.
   Google Scholar
• Chib, S. (1998), “Estimation and Comparison of Multiple Change-Point Models,” Journal of Econometrics, 86, 221–241.
   Web of Science ®Google Scholar
• Clark, T.E. (2011), “Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility,” Journal of Business & Economic Statistics, 29, 327–341.
   Web of Science ®Google Scholar
• Clark, T.E., and Ravazzolo, F. (2015), “Macroeconomic Forecasting Performance Under Alternative Specifications of Time-Varying Volatility,” Journal of Applied Econometrics, 30, 551--575.
   Web of Science ®Google Scholar
• Cooley, T.F., and Prescott, E.C. (1973), “An Adaptive Regression Model,” International Economic Review, 14, 364–371.
   Google Scholar
• Elliott, G., and Müller, U.K. (2006), “Efficient Tests for General Persistent Time Variation in Regression Coefficients,” The Review of Economic Studies, 73, 907–940.
   Web of Science ®Google Scholar
• Faust, J., and Wright, J.H. (2013), “Forecasting Inflation,” in Handbook of Economic Forecasting ( Vol. 2, Part A), eds. G. Elliott and A. Timmermann, Amsterdam: North Holland, pp. 2–56.
   Google Scholar
• Frühwirth-Schnatter, S. (2001), “Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models,” Journal of the American Statistical Association, 96, 194–209.
   Web of Science ®Google Scholar
• Frühwirth-Schnatter, S. (2006), Finite Mixture and Markov Switching Models, New York: Springer.
   Google Scholar
• Geweke, J., and Amisano, G. (2010), “Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns,” International Journal of Forecasting, 26, 216–230.
   Web of Science ®Google Scholar
• ——— (2011), “Optimal Prediction Pools,” Journal of Econometrics, 164, 130–141.
   Web of Science ®Google Scholar
• Giacomini, R., and Rossi, B. (2009), “Detecting and Predicting Forecast Breakdowns,” The Review of Economic Studies, 76, 669–705.
   Web of Science ®Google Scholar
• Hall, S.G., and Mitchell, J. (2007), “Combining Density Forecasts,” International Journal of Forecasting, 23, 1–13.
   Web of Science ®Google Scholar
• Hamilton, J.D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica, 57, 357–384.
   Web of Science ®Google Scholar
• Kass, R.E., and Raftery, A.E. (1995), “Bayes Factors,” Journal of the American Statistical Association, 90, 773–795.
   Web of Science ®Google Scholar
• Koop, G. (2003), Bayesian Econometrics, West Sussex: Wiley.
   Google Scholar
• McConnell, M.M., and Perez-Quiros, G. (2000), “Output Fluctuations in the United States: What has Changed Since the Early 1980’s?” American Economic Review, 90, 1464–1476.
   Web of Science ®Google Scholar
• Pesaran, M.H., Pettenuzzo, D., and Timmermann, A. (2006), “Forecasting Time Series Subject to Multiple Structural Breaks,” The Review of Economic Studies, 73, 1057–1084.
   Web of Science ®Google Scholar
• Primiceri, G.E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy,” The Review of Economic Studies, 72, 821–852.
   Web of Science ®Google Scholar
• Rossi, B. (2013), “Advances in Forecasting Under Instability,” in Handbook of Economic Forecasting ( Vol. 2, Part B), eds. G. Elliott and A. Timmermann, Amsterdam: North Holland, pp. 1203–1324
   Google Scholar
• Rossi, B., and Sekhposyan, T. (2015), “Forecast Rationality Tests in the Presence of Instabilities, With Applications to Federal Reserve and Survey Forecasts,” Journal of Applied Econometrics.
   Google Scholar
• Stock, J.H., and Watson, M.W. (1996), “Evidence on Structural Instability in Macroeconomic Time Series Relations,” Journal of Business & Economic Statistics, 14, 11–30.
   Web of Science ®Google Scholar
• ——— (1999), “Forecasting Inflation,” Journal of Monetary Economics, 44, 293–335.
   Web of Science ®Google Scholar
• ——— (2003), “Forecasting Output and Inflation: The Role of Asset Prices,” Journal of Economic Literature, 41, 788–829.
   Web of Science ®Google Scholar
• ——— (2007), “Why Has U.S. Inflation Become Harder to Forecast?” Journal of Money, Credit and Banking, 39, 3–33.
   Web of Science ®Google Scholar
• Welch, I., and Goyal, A. (2008), “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” Review of Financial Studies, 21, 1455–1508.
   Web of Science ®Google Scholar
• Zellner, A. (1986), “On Assessing Prior Distributions and Bayesian Regression Analysis With g Prior Distributions,” in Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, eds. P. Goel and A. Zellner, Amsterdam: North-Holland, pp. 233–243.
   Google Scholar