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Quantification of Expectations. Are They Useful for Forecasting Inflation?

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  • O Claveria
  • E Pons
  • J Surinach

Abstract

Business tendency surveys are commonly used to provide estimations of a wide range of macroeconomic variables before the publication of official data. The qualitative nature of data on the direction of change has often led to quantifying survey results making use of official data, introducing a measurement error due to incorrect assumptions. Through Monte Carlo simulations it is possible to isolate the measurement error introduced by incorrect assumptions when quantifying survey results. By means of a simulation experiment we check the effect on the measurement error of respondents diverging from "rationality". We also analyse the predictive performance of different quantification methods for fourteen EU countries and the euro area. We find that allowing for asymmetric and stochastic response thresholds (indifference interval) produces a lower measurement error and more accurate forecasts.

Suggested Citation

  • O Claveria & E Pons & J Surinach, 2006. "Quantification of Expectations. Are They Useful for Forecasting Inflation?," Economic Issues Journal Articles, Economic Issues, vol. 11(2), pages 19-38, September.
  • Handle: RePEc:eis:articl:206claveria
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    References listed on IDEAS

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    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. Jan Marc Berk, 1999. "Measuring inflation expectations: a survey data approach," Applied Economics, Taylor & Francis Journals, vol. 31(11), pages 1467-1480.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Common, Michael S, 1985. "Testing for Rational Expectations with Qualitative Survey Data," The Manchester School of Economic & Social Studies, University of Manchester, vol. 53(2), pages 138-148, June.
    5. Balcombe, Kelvin, 1996. "The Carlson-Parkin method applied to NZ price expectations using QSBO survey data," Economics Letters, Elsevier, vol. 51(1), pages 51-57, April.
    6. 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.
    7. 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.
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    Citations

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    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    2. Pinto, Santiago & Sarte, Pierre-Daniel G. & Sharp, Robert, 2015. "Learning About Consumer Uncertainty from Qualitative Surveys: As Uncertain As Ever," Working Paper 15-9, Federal Reserve Bank of Richmond.
    3. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    4. 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.
    5. repec:spr:qualqt:v:51:y:2017:i:6:d:10.1007_s11135-016-0416-0 is not listed on IDEAS
    6. Werner Hölzl & Gerhard Schwarz, 2014. "The "WIFO-Konjunkturtest": Methodology and Forecast Characteristics of the WIFO Business Cycle Survey," WIFO Monatsberichte (monthly reports), WIFO, vol. 87(12), pages 835-850, December.

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