He, Zhongfang (2009): Forecasting output growth by the yield curve: the role of structural breaks.
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Abstract
This paper proposes a new structural-break vector autoregressive (VAR) model for predicting real output growth by the nominal yield curve information. We allow for the possibility of both in-sample and out-of-sample breaks in parameter values and use information in historical regimes to make inference on out-of-sample breaks. A Bayesian estimation and forecasting procedure is provided which accounts for the uncertainty of structural breaks and model parameters. We discuss dynamic consistency when forecasting recursively with structural break models, which has been ignored in the existing literature, and provide a solution. Applied to monthly US data from 1964 to 2006, we find strong evidence of structural breaks in the predictive relation between the yield curve and output growth in late 1979 and early 1983. The short rate has more predictive power for output growth than the term spread before 1979 while the term spread becomes more significant since the breakof 1983. Incorporating the possibility of structural breaks improves out-of-sample forecasts of output growth from 1 to 12 months ahead.
Item Type: | MPRA Paper |
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Original Title: | Forecasting output growth by the yield curve: the role of structural breaks |
Language: | English |
Keywords: | Vector Autoregressive Model; Structural Break; Forecast; Output Growth; Yield Curve, Chib Model, MCMC, Bayesian |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General |
Item ID: | 28208 |
Depositing User: | Zhongfang He |
Date Deposited: | 19 Jan 2011 12:36 |
Last Modified: | 28 Sep 2019 12:00 |
References: | Ang, A., and G. Bekaert (2002): “Regime Switches in Interest Rates,” Journal of Business and Economic Statistics, 20, 163–182. Ang, A., M. Piazzesi, and M. Wei (2006): “What Does the Yield Curve Tell Us About GDP Growth?,” Journal of Econometrics, 131, 359–403. Bansal, R., and H. Zhou (2002): “Term Structure of Interest Rates with Regime Shifts,” Journal of Finance, 57, 1997–2043. Chen, N. (1991): “Financial Investment Opportunities and the Macroeconomy,” Journal of Finance, 46, 529–554. Chib, S. (1995): “Marginal Likelihood from the Gibbs Sampler,” Journal of the American Statistical Association, 90, 1313–1321. Chib, S. (1998): “Estimation and Comparison of Multiple Change Point Models,” Journal of Econometrics, 86, 221–241. (2001): “Markov Chain Monte Carlo Methods: Computation and Inference,” in Handbook of Econometrics, ed. by Heckman, and Leamer. Elsevier Science. Chib, S., and I. Jeliazkov (2001): “Marginal Likelihood from the Metropolis-Hasting Output,” Journal of the American Statistical Association, 96, 270–281. Davis, P., and G. Fagan (1997): “Are Financial Spreads Useful Indicators of Future Inflation and Output Growth in EU Countries?,” Journal of Applied Econometrics, 12, 701–714. Estrella, A., and G. Hardouvelis (1991): “The Term Structure as a Predictor of Real Economic Activity,” Journal of Finance, 46, 555–576. Estrella, A., and F. Mishkin (1997): “The Predictive Power of the Term Structure of Interest Rates in Europe and the United States: Implications for the European Central Bank,” European Economic Review, 41, 1375–1401. 27 (1998): “Predicting U.S. Recessions: Financial Variables as Leading Indicators,” Review of Economics and Statistics, 80, 45–61. Estrella, A., A. Rodrigues, and S. Schich (2003): “How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States,” Review of Economics and Statistics, 85, 629–644. Fama, E., and R. Bliss (1987): “The Information in Long-Maturity Forward Rates,” American Economic Review, 77, 680–692. Fruhwirth-Schnatter, S. (1995): “Bayesian Model Discrimination and Bayes Factor for Linear Gaussian State Space Models,” Journal of the Royal Statistical Society, Series B, 57, 237–246. (2004): “Estimating Marginal Likelihoods for Mixture and Markov Switching Models Using Bridge Sampling Techniques,” The Econometrics Journal, 7, 143–167. Gelfand, A., and D. Dey (1994): “Bayesian Model Choice: Asymptotics and Exact Calculations,” Journal of The Royal Statistical Society, B, 56, 501–514. Geweke, J. (2005): Contemporary Bayesian Econometrics and Statistics. John Wiley and Sons Ltd. Geweke, J., and C. Whiteman (2005): “Bayesian Forecasting,” in Handbook of Economic Forecasting, ed. by G. Elliott, C. Granger, and A. Timmermann, vol. forthcoming. Giacomini, R., and B. Rossi (2006): “How Stable is the Forecasting Performance of the Yield Curve for Output Growth?,” Oxford Bulletin of Economics and Statistics, 68, 783–795. Giordani, P., and R. Kohn (2006): “Efficient Bayesian Inference for Multiple Change- Point and Mixture Innovation Models,” Sveriges Riksbank Working Paper 196. Gray, S. F. (1996): “Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process,” Journal of Financial Economics, 42, 27–62. 28 Hamilton, J., and D. Kim (2002): “A Re-Examination of the Predictability of Economic Activity using the Yield Spread,” Journal of Money, Credit and Banking, 34, 340–360. Hamilton, J. D. (1988): “Rational-expectations econometric analysis of changes in regime: An investigation of the term structure of interest rates,” Journal of Economic Dynamics and Control, 12, 385–423. Harvey, C. R. (1989): “The Real Term Structure and Consumption Growth,” Journal of Financial Economics, 22, 305–333. He, Z., and J. Maheu (2008): “Real Time Detection of Structural Breaks in GARCH Models,” Working Paper 336, Department of Economics, University of Toronto. Kadiyala, R., and S. Karlsson (1997): “Numerical Methods for Estimation and Inference in Bayesian VAR Models,” Journal of Applied Econometrics, 12, 99–132. Kim, C., J. Morley, and C. Nelson (2005): “The structural breaks in the equity premium,” Journal of Business and Economic Statistics, 23, 181–191. Koop, G. (2003): Bayesian Econometrics. Wiley, Chichester, England. Koop, G., and S. Potter (2007): “Estimation and Forecasting in Models with Multiple Breaks,” Review of Economic Studies, 74, 763–789. Laurent, R. (1988): “An Interest Rate-Based Indiator of Monetary Policy,” Federal Reserve Bank of Chicago Economic Perspectives, 12, 3–14. (1989): “Testing the Spread,” Federal Reserve Bank of Chicago Economic Perspectives, 13, 22–34. Litterman, R. (1980): “A Bayesian Procedure for Forecasting with Vector Autoregression,” Mimeo, Massachusetts Institute of Technology. (1986): “Forecasting with Bayesian Vector Autoregression: Five Years of Experience,” Journal of Business and Economic Statistics, 4, 25–38. 29 Liu, C., and J. Maheu (2008): “Are There Structural Breaks in Realized Volatility ?,” Journal of Financial Econometrics, 6, 326–360. Lutkepohl, H. (2006): New Introduction to Multiple Time Series Analysis. Springer. Maheu, J., and T. McCurdy (2007): “How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution ?,” Journal of Business and Economic Statistics, forthcoming. Maheu, J. M., and S. Gordon (2008): “Learning, Forecasting and Structural Breaks,” Journal of Applied Econometrics, 23, 553–583. McCulloch, R., and R. Tsay (1993): “Bayesian inference and prediction for mean and variance shifts in autoregressive time series,” Journal of the American Statistical Association, 88, 968–978. Meng, X., and W. Wong (1996): “Simulating Ratios of Normalizing Constants via a Simple Identity,” Statistical Sinica, 6, 831–860. Miazhynskaia, T., and G. Dorffner (2006): “A Comparison of Bayesian Model Selection Based on MCMC with an Application to GARCH-Type Models,” Statistical Papers, 47, 525–549. Newton, M. A., and A. Raftery (1994): “Approximate Bayesian inference by the weighted likelihood bootstrap (with Discussion).,” Journal of The Royal Statistical Society, B, 56, 3–48. Pastor, L., and R. F. Stambaugh (2001): “The Equity Premium and Structural Breaks,” Journal of Finance, 4, 1207–1231. Pesaran, H., D. Pettenuzzo, and A. Timmermann (2006): “Forecasting Time Series Subject to Multiple Structural Breaks,” Review of Economic Studies, 73, 1057– 1084. Plosser, C., and G. Rouwenhorst (1994): “International Term Structure and Real Economic Growth,” Journal of Monetary Economics, 33, 133–156. Scott, S. L. (2002): “Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century,” Journal of the American Statistical Association, 97(457), 337–351. Stock, J., and M. Watson (1989): “New Indexes of Coincident and Leading Economic Indicators,” in NBER Macroeconomics Annual, ed. by O. Blanchard, and S. Fisher, pp. 352–394. Stock, J. H., and M. W. Watson (1999): “Forecasting Inflation,” Journal of Monetary Economics, 44, 293–335. Stock, J. H., and M. W. Watson (2003): “Forecasting Output and Inflation: The Role of Asset Prices,” Journal of Economic Literature, 41(3), 788–829. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/28208 |