IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Quantifying survey expectations: What's wrong with the probability approach?

Author Info

Listed author(s):
  • Breitung, Jörg
  • Schmeling, Maik

Abstract

We study a matched sample of individual stock market forecasts consisting of both qualitative and quantitative forecasts. This allows us to test for the quality of forecast quantification methods by comparing quantified qualitative forecasts with actual quantitative forecasts. Focusing mainly on the widely used quantification framework advocated by Carlson and Parkin (1975), the so-called "probability approach", we find that quantified expectations derived from the probability approach display a surprisingly weak correlation with reported quantitative stock return forecasts. We trace the reason for this low correlation to the importance of asymmetric and time-varying thresholds, whereas individual heterogeneity across forecasters seems to play a minor role. Hence, our results suggest that qualitative survey data may not be a very useful device to obtain quantitative forecasts and we suggest ways to remedy this problem when designing qualitative surveys.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-485.pdf
Download Restriction: no

Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-485.

as
in new window

Length: 38 pages
Date of creation: Dec 2011
Handle: RePEc:han:dpaper:dp-485
Contact details of provider: Postal:
Koenigsworther Platz 1, D-30167 Hannover

Phone: (0511) 762-5350
Fax: (0511) 762-5665
Web page: http://www.wiwi.uni-hannover.de

More information through EDIRC

Keywords: Quantification; Stock Market Expectations; Probability Approach; Heterogeneity;

Other versions of this item:

Find related papers by JEL classification:
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
  • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  1. Dasgupta, Susmita & Lahiri, Kajal, 1992. "A Comparative Study of Alternative Methods of Quantifying Qualitative Survey Responses Using NAPM Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 391-400, October.
  2. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
  3. Pesaran, M. Hashem & Weale, Martin, 2006. "Survey Expectations," Handbook of Economic Forecasting, Elsevier.
  4. Breitung Jörg, 2008. "Assessing the Rationality of Survey Expectations: The Probability Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(5-6), pages 630-643, October.
  5. Jan Marc Berk, 1999. "Measuring inflation expectations: a survey data approach," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1467-1480.
  6. Silvia Lui & James Mitchell & Martin Weale, 2011. "Qualitative business surveys: signal or noise?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 327-348, 04.
  7. N. Gregory Mankiw & Ricardo Reis & Justin Wolfers, 2004. "Disagreement about Inflation Expectations," NBER Chapters,in: NBER Macroeconomics Annual 2003, Volume 18, pages 209-270 National Bureau of Economic Research, Inc.
  8. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1107-1125.
  9. Loffler, Gunter, 1999. "Refining the Carlson-Parkin method," Economics Letters, Elsevier, vol. 64(2), pages 167-171, August.
  10. Doepke Joerg & Dovern Jonas & Fritsche Ulrich & Slacalek Jiri, 2008. "The Dynamics of European Inflation Expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-23, March.
  11. Menkhoff, Lukas & Rebitzky, Rafael R. & Schröder, Michael, 2009. "Heterogeneity in exchange rate expectations: Evidence on the chartist-fundamentalist approach," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 241-252, May.
  12. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
  13. Frankel, Jeffrey A & Froot, Kenneth A, 1990. "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market," American Economic Review, American Economic Association, vol. 80(2), pages 181-185, May.
  14. Steffen Henzel & Timo Wollmershäuser, 2005. "An Alternative to the Carlson-Parkin Method for the Quantification of Qualitative Inflation Expectations: Evidence from the Ifo World Economic Survey," Ifo Working Paper Series Ifo Working Papers No. 9, Ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  15. Mitchell, James & Mouratidis, Kostas & Weale, Martin, 2007. "Uncertainty in UK manufacturing: Evidence from qualitative survey data," Economics Letters, Elsevier, vol. 94(2), pages 245-252, February.
  16. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
  17. Defris, L. V. & Williams, R. A., 1979. "Quantitative versus qualitative measures of price expectations: The evidence from Australian consumer surveys," Economics Letters, Elsevier, vol. 2(2), pages 169-173.
  18. Müller, Christian, 2010. "You CAN Carlson-Parkin," Economics Letters, Elsevier, vol. 108(1), pages 33-35, July.
  19. Pesaran, M Hashem, 1985. "Formation of Inflation Expectations in British Manufacturing Industries," Economic Journal, Royal Economic Society, vol. 95(380), pages 948-975, December.
  20. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  21. Thomas Maag, 2009. "On the accuracy of the probability method for quantifying beliefs about inflation," KOF Working papers 09-230, KOF Swiss Economic Institute, ETH Zurich.
  22. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm-Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, 07.
  23. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data : A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
  24. Batchelor, Roy A & Orr, Adrian B, 1988. "Inflation Expectations Revisited," Economica, London School of Economics and Political Science, vol. 55(219), pages 317-331, August.
  25. Nolte, Ingmar & Pohlmeier, Winfried, 2007. "Using forecasts of forecasters to forecast," International Journal of Forecasting, Elsevier, vol. 23(1), pages 15-28.
  26. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
  27. Carlson, John A & Parkin, J Michael, 1975. "Inflation Expectations," Economica, London School of Economics and Political Science, vol. 42(166), pages 123-138, May.
  28. Batchelor, R. A., 1982. "Expectations, output and inflation : The European experience," European Economic Review, Elsevier, vol. 17(1), pages 1-25.
  29. James Mitchell & Richard J. Smith & Martin R. Weale, 2002. "Quantification of Qualitative Firm-Level Survey Data," Economic Journal, Royal Economic Society, vol. 112(478), pages 117-135, March.
  30. Mitchell, James, 2002. "The use of non-normal distributions in quantifying qualitative survey data on expectations," Economics Letters, Elsevier, vol. 76(1), pages 101-107, June.
  31. Fishe, Raymond P. H. & Lahiri, Kajal, 1981. "On the estimation of inflationary expectations from qualitative responses," Journal of Econometrics, Elsevier, vol. 16(1), pages 89-102, May.
  32. Batchelor, R A, 1986. "Quantitative v. Qualitative Measures of Inflation Expectations," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(2), pages 99-120, May.
Full references (including those not matched with items on IDEAS)

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

as
in new window


Cited by:

  1. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
  2. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming”," AQR Working Papers 201706, University of Barcelona, Regional Quantitative Analysis Group, revised May 2017.
  3. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
  4. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
  5. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
  6. Kajal Lahiri & Yongchen Zhao, 2013. "Quantifying Heterogeneous Survey Expectations: The Carlson-Parkin Method Revisited," Discussion Papers 13-08, University at Albany, SUNY, Department of Economics.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Access and download statistics

When requesting a correction, please mention this item's handle: RePEc:han:dpaper:dp-485. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Heidrich, Christian)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.