Explanations of the inconsistencies in survey respondents’ forecasts
Clements, Michael P. (2008) Explanations of the inconsistencies in survey respondents’ forecasts. Working Paper. Coventry: University of Warwick, Department of Economics. (Warwick economic research papers.
WRAP_Clements_twerp_870.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://www2.warwick.ac.uk/fac/soc/economics/resear...
A comparison of the point forecasts and the central tendencies of probability distributions of inflation and output growth of the SPF indicates that the point forecasts are sometimes optimistic relative to the probability distributions. We consider and evaluate a number of possible explanations for this finding, including the degree of uncertainty concerning the future, computational costs, delayed updating, and asymmetric loss. We also consider the relative accuracy of the two sets of forecasts.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||H Social Sciences > HB Economic Theory|
|Divisions:||Faculty of Social Sciences > Economics|
|Library of Congress Subject Headings (LCSH):||Econometric models, Economic forecasting, Economic surveys, Estimation theory|
|Series Name:||Warwick economic research papers|
|Publisher:||University of Warwick, Department of Economics|
|Place of Publication:||Coventry|
|Date:||14 July 2008|
|Number of Pages:||36|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|References:||Andersen, T. G., and Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accuate forecasts. International Economic Review, 39, 885�905. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modelling and forecasting realized volatility. Econometrica, 71, 579�625. Carroll, C. D. (2003). Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics, 118, 269�298. Christo¤ersen, P. F. (1998). Evaluating interval forecasts. International Economic Review, 39, 841�862. Christo¤ersen, P. F., and Diebold, F. X. (1997). Optimal prediction under asymmetric loss. Econo- metric Theory, 13, 808�817. Clements, M. P. (1995). Rationality and the role of judgement in macroeconomic forecasting. Economic Journal, 105, 410�420. Clements, M. P. (2008). Internal consistency of survey respondents� forecasts: Evidence based on the Survey of Professional Forecasters. In Castle, J. L., and Shephard, N. (eds.), The Methodology and Practice of Econometrics: Oxford University Press. Forthcoming. Clements, M. P., and Harvey, D. I. (2006). Forecast encompassing tests and probability forecasts. Working paper, Department of Economics, University of Warwick. Croushore, D. (1993). Introducing: The Survey of Professional Forecasters. Federal Reserve Bank of Philadelphia Business Review, November/December, 3�13. Croushore, D. (2006). Forecasting with real-time macroeconomic data. In Elliott, G., Granger, C., and Timmermann, A. (eds.), Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pp. 961�982: Elsevier, Horth-Holland. Croushore, D., and Stark, T. (2001). A real-time data set for macroeconomists. Journal of Econo- metrics, 105, 111�130. Diebold, F. X., Gunther, T. A., and Tay, A. S. (1998). Evaluating density forecasts: With applica- tions to �nancial risk management. International Economic Review, 39, 863�883. Diebold, F. X., Hahn, J. Y., and Tay, A. S. (1999a). Multivariate density forecast evaluation and calibration in �nancial risk management: High frequency returns on foreign exchange. Review of Economics and Statistics, 81, 661�673. Diebold, F. X., Tay, A. S., and Wallis, K. F. (1999b). Evaluating density forecasts of in�ation: The Survey of Professional Forecasters. In Engle, R. F., and White, H. (eds.), Cointegration, Causality and Forecasting: A Festschrift in Honour of Clive Granger, pp. 76�90. Oxford: Oxford University Press. Ehrbeck, T., and Waldmann, R. (1996). Why are professional forecasters biased? agency versus behavioral explanations. The Quarterly Journal of Economics, 111(1), 21�40. Elliott, G., Komunjer, I., and Timmermann, A. (2005a). Biases in macroeconomic forecasts: Irra- tionality or asymmetric loss. Journal of the European Economic Association. Forthcoming. Elliott, G., Komunjer, I., and Timmermann, A. (2005b). Estimation and testing of forecast ratio- nality under �exible loss. Review of Economic Studies, 72, 1107�1125. Elliott, G., and Timmermann, A. (2004). Optimal forecast combinations under general loss func- tions and forecast error distributions. Journal of Econometrics, 122, 47�79. Engelberg, J., Manski, C. F., and Williams, J. (2007). Comparing the point predictions and subjec- tive probability distributions of professional forecasters. Journal of Business and Economic Statistics. Forthcoming. Evans, G. W., and Honkapohja, S. (2001). Learning and Expectations in Macroeconomics. Prince- ton: Princeton University Press. García, J. A., and Manzanares, A. (2007). Reporting biases and survey results: evidence from European professional forecasters. ECB Working Paper No. 836, European Central Bank, Frankfurt. Giacomini, R., and Komunjer, I. (2005). Evaluation and Combination of Conditional Quantile Forecasts. Journal of Business and Economic Statistics, 23, 416 �431. Giordani, P., and Söderlind, P. (2003). In�ation forecast uncertainty. European Economic Review, 74, 1037�1060. Granger, C. W. J. (1969). Prediction with a generalized cost of error function. Operations Research Quarterly, 20, 199�207. Granger, C. W. J., White, H., and Kamstra, M. (1989). Interval forecasting: An analysis based upon ARCH-quantile estimators. Journal of Econometrics, 40, 87�96. Kandel, E., and Pearson, N. (1995). Di¤erential interpretation of information and trade in specu- lative markets. Journal of Political Economy, 103, 831�872. Kandel, E., and Zilberfarb, B. Z. (1999). Di¤erential interpretation of information in in�ation forecasts. The Review of Economics and Statistics, 81, 217�226. Lahiri, K., and Sheng, X. (2007). Evolution of forecast disagreement in a bayesian learning model. Discussion paper. Manuscript, University at Albany - SUNY. Laster, D., Bennett, P., and Geoum, I. S. (1999). Rational bias in macroeconomic forecasts. The Quarterly Journal of Economics, 114(1), 293�318. Mankiw, N. G., and Reis, R. (2002). Sticky information versus sticky prices: a proposal to replace the new keynesian phillips curve. Quarterly Journal of Economics, 117, 1295�1328. Mankiw, N. G., Reis, R., andWolfers, J. (2003). Disagreement about in�ation expectations. mimeo, National Bureau of Economic Research, Cambridge MA. Mankiw, N. G., and Shapiro, M. D. (1986). News or noise: An analysis of GNP revisions. Survey of Current Business (May 1986), US Department of Commerce, Bureau of Economic Analysis, 20�25. Nordhaus, W. D. (1987). Forecasting e¢ ciency: Concepts and applications. Review of Economics and Statistics, 69, 667�674. Ottaviani, M., and Sorensen, P. N. (2006). The strategy of professional forecasting. Journal of Financial Economics, 81, 441�466. Patton, A. J., and Timmermann, A. (2007). Testing forecast optimality under unknown loss. Journal of the American Statistical Association, 102, 1172�1184. Pesaran, M. H., and Weale, M. (2006). Survey expectations. In Elliott, G., Granger, C., and Tim- mermann, A. (eds.), Handbook of Economic Forecasting, Volume 1. Handbook of Economics 24, pp. 715�776: Elsevier, Horth-Holland. Romer, C. D., and Romer, D. H. (2000). Federal Reserve information and the behaviour of interest rates. American Economic Review, 90, 429�457. Stekler, H. O. (2002). The rationality and e¢ ciency of individuals�forecasts. In Clements, M. P., and Hendry, D. F. (eds.), A Companion to Economic Forecasting, pp. 222�240: Oxford: Blackwells. Zellner, A. (1986). Biased predictors, rationality and the evaluation of forecasts. Economics Letters, 21, 45�48.|
Actions (login required)