Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Statistics
  • Help & Advice
University of Warwick

The Library

  • Login

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

Tools
- Tools
+ Tools

Clements, Michael P. (2012) Probability distributions or point predictions? Survey forecasts of US output growth and inflation. Working Paper. Coventry: Department of Economics, University of Warwick. (Warwick economics research paper series (TWERPS). (Unpublished)

[img]
Preview
Text (Working paper)
WRAP_Clements_twerp_976.pdf - Other

Download (761Kb) | Preview
Official URL: http://www2.warwick.ac.uk/fac/soc/economics/resear...

Abstract

We consider whether survey respondents' probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model, and whether they are well calibrated more generally. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.

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): Economic forecasting, Economic surveys
Series Name: Warwick economics research paper series (TWERPS)
Publisher: Department of Economics, University of Warwick
Place of Publication: Coventry
Date: 20 January 2012
Number: Number 976
Status: Not Peer Reviewed
Publication Status: Unpublished
Access rights to Published version: Open Access
References: Atkeson, A., and Ohanian, L. (2001). Are Phillips Curves useful for forecasting inflation?. Federal Reserve Bank of Minneapolis Quarterly Review, 25, 2-11. (1). Batchelor, R., and Dua, P. (1991). Blue Chip rationality tests. Journal of Money, Credit and Banking, 23, 692-705. Berkowitz, J. (2001). Testing density forecasts, with applications to risk management. Journal of Business and Economic Statistics, 19, 465-474. Boero, G., Smith, J., and Wallis, K. F. (2008a). Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of Economic Forecasters. International Journal of Forecasting, 24, 354-367. Boero, G., Smith, J., and Wallis, K. F. (2008b). Uncertainty and disagreement in economic pre-diction: the Bank of England Survey of Economic Forecasters. Economic Journal, 118, 1107-1127. Clements, M. P. (2009). 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. A Festschrift in Honour of David F. Hendry. Chapter 8, pp. 206-226. Oxford: Oxford University Press. Clements, M. P. (2010). Explanations of the Inconsistencies in Survey Respondents Forecasts. European Economic Review, 54, 536-549. Croushore, D. (1993). Introducing: The Survey of Professional Forecasters. Federal Reserve Bank of Philadelphia Business Review, November/December, 3-13. 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 financial risk management. International Economic Review, 39, 863-883. Doornik, J. A., and Hansen, H. (1994). A practical test for univariate and multivariate normality. Discussion paper, Nuffield College. Engelberg, J., Manski, C. F., and Williams, J. (2009). Comparing the point predictions and subjective probability distributions of professional forecasters. Journal of Business and Economic Statistics, 27, 30-41. Fixler, D. J., and Grimm, B. T. (2005). Reliability of the NIPA estimates of U.S. economic activity. Survey of Current Business, 85, 9-19. Fixler, D. J., and Grimm, B. T. (2008). The reliability of the GDP and GDI estimates. Survey of Current Business, 88, 16-32. Garcia, 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. Giordani, P., and Söderlind, P. (2003). Inflation forecast uncertainty. European Economic Review, 74, 1037-1060. Hall, S. G., and Mitchell, J. (2009). Recent developments in density forecasting. In Mills, T. C., and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Volume : Applied Econometrics, pp. 199-239: Palgrave MacMillan. Kandel, E., and Zilberfarb, B. Z. (1999). Differential interpretation of information in inflation forecasts. The Review of Economics and Statistics, 81, 217-226. Lahiri, K., and Sheng, X. (2008). Evolution of forecast disagreement in a Bayesian learning model. Journal of Econometrics, 144, 325-340. Lahiri, K., and Sheng, X. (2010). Learning and heterogeneity in GDP and inflation forecasts. International Journal of Forecasting, 26, 265-292. Landefeld, J. S., Seskin, E. P., and Fraumeni, B. M. (2008). Taking the pulse of the economy. Journal of Economic Perspectives, 22, 193-216. Manzan, S. (2011). Differential interpretation in the Survey of Professional Forecasters. Journal of Money, Credit and Banking, 43, 993-1017. O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., and Rakow, T. (2006). Uncertain Judgements: Eliciting Experts'Probabilities: John Wiley and Sons, Ltd. Patton, A. J., and Timmermann, A. (2010). Why do forecasters disagree? lessons from the term structure of cross-sectional dispersion. Journal of Monetary Economics, 57, 803-820. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74, 967-1012. Stock, J. H., and Watson, M. W. (2007). Why has U.S. Inflation Become Harder to Forecast?. Journal of Money, Credit and Banking, Supplement to Vol. 39, 3-33. Stock, J. H., and Watson, M. W. (2010). Modelling Inflation after the Crisis. NBER Working Paper Series, 16488. Tay, A. S., and Wallis, K. F. (2000). Density forecasting: A survey. Journal of Forecasting, 19, 235�254. Reprinted in Clements, M. P. and Hendry, D. F. (eds.) A Companion to Economic Forecasting, pp.45 -68, Oxford: Blackwells (2002). Zarnowitz, V., and Braun, P. (1993). Twenty-two years of the NBER-ASA quarterly economic outlook surveys: aspects and comparisons of forecasting performance. In Stock, J., and Watson, M. (eds.), Business Cycles, Indicators, and Forecasting, pp. 11�84: Chicago: University of Chicago Press and NBER.
URI: http://wrap.warwick.ac.uk/id/eprint/44609

Request changes to a record

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...
twitter

Email us: publications@warwick.ac.uk
Contact Details
About Us