Learning and heterogeneity in GDP and inflation forecasts

Lahiri, Kajal and Sheng, Xuguang (2009): Learning and heterogeneity in GDP and inflation forecasts. Published in: International Journal of Forecasting No. 26 (2010): pp. 265-292.

Abstract

We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.

• Item Type: MPRA Paper
• Language: English
• Keywords: Bayesian learning, Public information, Panel data, Forecast disagreement, Forecast horizon; Content function; Forecast efficiency; GDP; Inflation targeting
• Subjects: E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General

References

Acemoglu, D., V. Chernozhukov and M. Yildiz, 2006. Learning and disagreement in an uncertain world. NBER working paper No. 12648.

Aumann, R., 1987. Correlated equilibrium as an expression of Bayesian rationality. Econometrica 55, 1-18.

Batchelor, R., 2007. Bias in macroeconomic forecasts. International Journal of Forecasting 23, 189-203.

Beechy, M.J., B.K. Johannsen, and A.T. Levin, 2007. Are long-run inflation expectations anchored more firmly in the Euro area than in the United States? Board of Governors of the Federal Reserve System, CEPR Discussion Paper No. DP6536.

Benerjee, A. and M. Marcellino, 2006. Are there any reliable leading indicators for US inflation and GDP growth? International Journal of Forecasting 22, 137-151. Blix, M., J. Wadefjord, U. Wienecke, and M. Adahl, 2001.

How good is the forecasting performance of major institutions? Economic Review 3, 38-68.

Boero, G., J. Smith, and K.F. Wallis, 2008. Uncertainty and disagreement in economic prediction: the Bank of England Survey of External Forecasters. Economic Journal 118, 1107-1127.

Bowles, C., R. Friz, V. Genre, G. Kenny, A. Meyler, and T. Rautanen, 2007. The ECB Survey of Professional Forecasters (SPF): a review after eight years’ experience. European Central Bank occasional paper series, No. 59.

Capistrán, C. and A. Timmermann, 2008. Disagreement and biases in inflation expectations. Forthcoming in Journal of Money, Credit and Banking.38 Carroll, C., 2003.

Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics 118, 269-298. Chen, Q. and W. Jiang, 2006.

Analysts’ weighting of private and public Information. Review of Financial Studies 19, 319-355.

Clements, M.P., 1995. Rationality and the role of judgment in macroeconomic forecasting. Economic Journal 105, 410-420. Clements, M.P., 1997.

Evaluating the rationality of fixed-event forecasts. Journal of Forecasting 16, 225-239.

Clements, M.P. and N. Taylor, 2001. Robust evaluation of fixed-event forecast rationality. Journal of Forecasting 20, 285-295.

Diebold, F.X. and L. Kilian, 2001. Measuring predictability: Theory and macroeconomic applications. Journal of Applied Econometrics 16, 657-669. Dominitz, J. and C.F. Manski, 2005.

Measuring and interpreting expectations of equity returns. NBER working paper No. 11313.Döpke, J. and U. Fritsche, 2006.

When do forecasters disagree? An assessment of German growth and inflation forecast dispersion. International Journal of Forecasting 22, 125-135.Ehrbeck, T. and R. Waldmann, 1996.

Why are professional forecasters biased? Agency versus behavioral explanations. Quarterly Journal of Economics 61, 21-40.El-Gamal, M. and D.M. Grether, 1995.

Are people Bayesian? Uncovering behavioral strategies. Journal of the American Statistical Association 90, 1137-1145.

Galbraith, J.W., 2003. Content horizons for univariate time-series forecasts. International Journal of Forecasting 19, 43-55.39 Galbraith, J.W. and G. Tkacz, 2007.

Forecast content and content horizons for some important macroeconomic time series. Canadian Journal of Economics 40, 935-953.Granger, C.W.J., 1996.

Can we improve the perceived quality of economic forecasts? Journal of Applied Econometrics 11, 455-473.Gregory, A.W., G.W. Smith, and J. Yetman, 2001.

Testing for forecast consensus.Journal of Business and Economic Statistics 19, 34-43.Gregory, A.W. and J. Yetman, 2004.

The evolution of consensus in macroeconomic forecasting. International Journal of Forecasting 20, 461-473.

Gürkaynak, R., A. Levin, A. Marder, and E. Swanson, 2007. Inflation targeting and the anchoring of inflation expectations in the Western Hemisphere. Economic Review, Federal Reserve Bank of San Francisco, 25-47.

Harris, M. and A. Raviv, 1993. Differences of opinion make a horse race. Review of Financial Studies 6, 473-506.Isiklar, G. and K. Lahiri, 2007.

How far ahead can we forecast? Evidence from crosscountry surveys. International Journal of Forecasting 23, 167-187. Isiklar, G., K. Lahiri and P. Loungani, 2006.

How quickly do forecasters incorporate news? Evidence from cross-country surveys. Journal of Applied Econometrics 21, 703-725.Johnson, D.J., 2002.

The effect of inflation targeting on the behavior of expected inflation: evidence from a 11 country panel. Journal of Monetary Economics 49,1521-1538.Jonung, L., 1981.

Perceived and expected rates of inflation in Sweden. American Economic Review 71, 961-968.40 Kandel, E. and N. Pearson, 1995.

Differential interpretation of public signals and trade in speculative markets. Journal of Political Economy 103, 831-872.Kandel, E. and B.Z. Zilberfarb, 1999.

Differential interpretation of information in inflation forecasts. Review of Economics and Statistics 81, 217-226. Lahiri, K. and F. Liu, 2006.

Modeling multi-period inflation uncertainty using a panel of density forecasts. Journal of Applied Econometrics 21, 1199-1219.

Lahiri, K. and F. Liu, 2007. On the estimation of forecasters’ loss functions using density forecasts. Paper presented at the 27th International Symposium on Forecasting, New York.

Lahiri, K. and X. Sheng, 2008. Evolution of forecast disagreement in a Bayesian learning model, Journal of Econometrics 144, 325-340.

Lamont, O., 2002. Macroeconomic forecasts and microeconomic forecasters. Journal of Economic Behavior and Organization 48, 265-280.Laster, D., P. Bennett, and I.S. Geoum, 1999.

Rational bias in macroeconomic forecasts.Quarterly Journal of Economics 114, 293-318.Lucas, R.E., 1973.

Some international evidence on inflation-output tradeoffs. American Economic Review 63, 326-334.Maddala, G.S., 1991.

Survey data on expectations: What have we learnt? In M. Nerlove (Ed.), Issues in Contemporary Economics, Vol. 2, Macmillan I.E.A., 319-344.Mankiw, N.G. and R. Reis, 2006.

Pervasive stickiness. American Economic Review 96,164-169. Mankiw, N.G. and R. Reis, and J. Wolfers, 2003.

Disagreement about inflation expectations. NBER Macroeconomics Annual 2003.41 Mishkin, F.S., 2007.

Inflation dynamics. NBER working paper No. 13147.Morris, S. and H.S. Shin, 2006. Inertia of forward-looking epectations. American Economic Review 96, 152-157.Nordhaus, W., 1987.

Forecasting efficiency: Concepts and applications. Review of Economics and Statistics 69, 667-674.Öller, L-E. and B. Barot, 2000.

The accuracy of European growth and inflation forecasts. International Journal of Forecasting 16, 293-315. Ottaviani, M. and P.N. Sorensen, 2006.

The strategy of professional forecastings. Journal of Financial Economics 81, 441-466. Pesaran, M.H., 2006.

Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967-1012. Patton, A.J. and A. Timmermann, 2007.

Learning in real time: Theory and empirical evidence from the term structure of survey forecasts. Working paper, Department of Economics, UCSD.Souleles, N., 2004.

Expectations, heterogeneous forecast errors, and consumption: Micro evidence from the Michigan consumer sentiment surveys. Journal of Money, Credit and Banking 36, 39-72.Stock, J.H. and M.W. Watson, 2003.

Forecasting output and inflation: the role of asset prices. Journal of Economic Literature 41, 788-829.Stock, J.H. and M.W. Watson, 2007.

Why has U.S. inflation become harder to forecast? Journal of Money, Credit and Banking 39, 3-33.Tversky, A. and D. Kahneman, 1974.

Judgment under uncertainty: heuristics and biases. Science 185, 1124-1131.42 Varian, H., 1989.

Differences of opinion in financial markets. In C. Stone (Ed.), Financial Risk: Theory, Evidence and Implications. Kluwer: Boston, 3-37.Zarnowitz, V. and L.A. Lambros, 1987.

Consensus and uncertainty in economic prediction. Journal of Political Economy 95, 591-621.Zarnowitz, V. and P. Braun, 1993.

Twenty-two years of NBER-ASA quarterly economic outlook surveys: aspects and comparisons of forecasting performance. In J.H. Stock and M.W. Watson (Eds.) Business Cycles, Indicators and Forecasting. University of Chicago Press and NBER.Zellner, A., 1988. Optimal information processing and Bayes’ theorem. American Statistician 42, 278-284.

Zellner, A., 2002. Information processing and Bayesian analysis. Journal of Econometrics 107, 41-50.