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The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys

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  • Lui, Silvia
  • Mitchell, James
  • Weale, Martin

Abstract

Qualitative expectational data from business surveys are widely used to construct forecasts. However, based typically on evaluation at the macroeconomic level, doubts persist about the utility of these data. This paper evaluates the ability of the underlying firm-level expectations to anticipate subsequent outcomes. Importantly, this evaluation is not hampered by only having access to qualitative outcome data obtained from subsequent business surveys. Quantitative outcome data are also exploited. This required access to a unique panel dataset which matches firms' responses from the qualitative business survey with the same firms' quantitative replies to a different survey carried out by the national statistical office. Nonparametric tests then reveal an apparent paradox. Despite evidence that the qualitative and quantitative outcome data are related, we find that the expectational data offer rational forecasts of the qualitative but not the quantitative outcomes. We discuss the role of "discretisation" errors and the loss function in explaining this paradox.

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  • 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.
  • Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1128-1146
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    1. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    2. Ivaldi, Marc, 1992. "Survey Evidence on the Rationality of Expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 225-241, July-Sept.
    3. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    4. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    5. Pesaran, M. Hashem & Weale, Martin, 2006. "Survey Expectations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 14, pages 715-776, Elsevier.
    6. Carroll, Christopher D & Fuhrer, Jeffrey C & Wilcox, David W, 1994. "Does Consumer Sentiment Forecast Household Spending? If So, Why?," American Economic Review, American Economic Association, vol. 84(5), pages 1397-1408, December.
    7. Ciaran Driver & Giovanni Urga, 2004. "Transforming Qualitative Survey Data: Performance Comparisons for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 71-89, February.
    8. Wallis, Kenneth F, 1980. "Econometric Implications of the Rational Expectations Hypothesis," Econometrica, Econometric Society, vol. 48(1), pages 49-73, January.
    9. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    10. William A. Branch, 2004. "The Theory of Rationally Heterogeneous Expectations: Evidence from Survey Data on Inflation Expectations," Economic Journal, Royal Economic Society, vol. 114(497), pages 592-621, July.
    11. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    12. Keane, Michael P & Runkle, David E, 1990. "Testing the Rationality of Price Forecasts: New Evidence from Panel Data," American Economic Review, American Economic Association, vol. 80(4), pages 714-735, September.
    13. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423, September.
    14. Kauppi, Eija & Lassila, Jukka & Teräsvirta, Timo, 1996. "Short-Term Forecasting of Industrial Production with Business Survey Data: Experience from Finland's Great Depression," Discussion Papers 546, The Research Institute of the Finnish Economy.
    15. Carl S Bonham & Richard H Cohen, 2000. "Testing the Rational Expectations Hypothesis using Survey Data," Working Papers 200007, University of Hawaii at Manoa, Department of Economics.
    16. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
    17. 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.
    18. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    19. Brown, Bryan W & Maital, Shlomo, 1981. "What Do Economists Know? An Empirical Study of Experts' Expectations," Econometrica, Econometric Society, vol. 49(2), pages 491-504, March.
    20. Bonham, Carl S & Cohen, Richard H, 2001. "To Aggregate, Pool, or Neither: Testing the Rational-Expectations Hypothesis Using Survey Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 278-291, July.
    21. Troy D. Matheson & James Mitchell & Brian Silverstone, 2010. "Nowcasting and predicting data revisions using panel survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 313-330.
    22. Easaw, Joshy Z. & Heravi, Saeed M., 2004. "Evaluating consumer sentiments as predictors of UK household consumption behavior: Are they accurate and useful?," International Journal of Forecasting, Elsevier, vol. 20(4), pages 671-681.
    23. Gourieroux, Christian & Monfort, Alain & Renault, Eric & Trognon, Alain, 1987. "Generalised residuals," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 5-32.
    24. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
    25. Zellner, Arnold, 1986. "Biased predictors, rationality and the evaluation of forecasts," Economics Letters, Elsevier, vol. 21(1), pages 45-48.
    26. Souleles, Nicholas S, 2004. "Expectations, Heterogeneous Forecast Errors, and Consumption: Micro Evidence from the Michigan Consumer Sentiment Surveys," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(1), pages 39-72, February.
    27. 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, July.
    28. Abberger, Klaus, 2007. "Qualitative business surveys and the assessment of employment -- A case study for Germany," International Journal of Forecasting, Elsevier, vol. 23(2), pages 249-258.
    29. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    30. Gourieroux, Christian & Pradel, Jacqueline, 1986. "Direct test of the rational expectation hypothesis," European Economic Review, Elsevier, vol. 30(2), pages 265-284, April.
    31. Stephen G. Hall & James Mitchell, 2009. "Recent Developments in Density Forecasting," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 5, pages 199-239, Palgrave Macmillan.
    32. Nerlove, Marc, 1983. "Expectations, Plans, and Realizations in Theory and Practice," Econometrica, Econometric Society, vol. 51(5), pages 1251-1279, September.
    33. 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, July.
    34. Das, J.W.M. & Dominitz, J. & van Soest, A.H.O., 1997. "Comparing Predictions and Outcomes : Theory and Application to Income Changes," Other publications TiSEM 6eef11dd-0ae4-4673-b8c0-2, Tilburg University, School of Economics and Management.
    35. McIntosh, James & Schiantarelli, Fabio & Low, William, 1989. "A Qualitative Response Analysis of UK Firms' Employment and Output Decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(3), pages 251-264, July-Sept.
    36. Sendhil Mullainathan & Marianne Bertrand, 2001. "Do People Mean What They Say? Implications for Subjective Survey Data," American Economic Review, American Economic Association, vol. 91(2), pages 67-72, May.
    37. repec:bla:econom:v:42:y:1975:i:166:p:123-38 is not listed on IDEAS
    38. Lee, Kevin C, 1994. "Formation of Price and Cost Inflation Expectations in British Manufacturing Industries: A Multi-Sectoral Analysis," Economic Journal, Royal Economic Society, vol. 104(423), pages 372-385, March.
    39. Wheeler, Tracy, 2010. "What can we learn from surveys of business expectations?," Bank of England Quarterly Bulletin, Bank of England, vol. 50(3), pages 190-198.
    40. Dr Martin Weale & Dr. James Mitchell, 2005. "Forecasting manufacturing output growth using firm-level survey data," National Institute of Economic and Social Research (NIESR) Discussion Papers 251, National Institute of Economic and Social Research.
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    8. 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.
    9. Boneva, Lena & Cloyne, James & Weale, Martin & Wieladek, Tomasz, 2018. "Firms' Expectations of New Orders, Employment, Costs and Prices: Evidence from Micro Data," CEPR Discussion Papers 12722, C.E.P.R. Discussion Papers.
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