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Rational Forecasts or Social Opinion Dynamics? Identification of Interaction Effects in a Business Climate Survey

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  • Thomas Lux

Abstract

This paper develops a methodology for estimating the parameters of dynamic opinion or expectation formation processes with social interactions. We study a simple stochastic framework of a collective process of opinion formation by a group of agents who face a binary decision problem. The aggregate dynamics of the individuals' decisions can be analyzed via the stochastic process governing the ensemble average of choices. Numerical approximations to the transient density for this ensemble average allow the evaluation of the likelihood function on the base of discrete observations of the social dynamics. This approach can be used to estimate the parameters of the opinion formation process from aggregate data on its average realization. Our application to a well-known business climate index provides strong indication of social interaction as an important element in respondents' assessment of the business climate

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File URL: http://www.ifw-members.ifw-kiel.de/publications/rational-forecasts-or-social-opinion-dynamics-identification-of-interaction-effects-in-a-business-climate-survey/KWP_1424_Rational%20Forecasts.pdf
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Bibliographic Info

Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1424.

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Length: 40 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:kie:kieliw:1424

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Keywords: business climate; business cycle forecasts; opinion formation; social interactions;

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References

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  1. Edward L. Glaeser & Bruce Sacerdote & Jose A. Scheinkman, 1995. "Crime and Social Interactions," NBER Working Papers 5026, National Bureau of Economic Research, Inc.
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  10. Christopher D Carroll, 2002. "Macroeconomic Expectations of Households and Professional Forecasters," Economics Working Paper Archive 477, The Johns Hopkins University,Department of Economics.
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  12. Horst, Ulrich & Rothe, Christian, 2008. "Queuing, Social Interactions, And The Microstructure Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 12(02), pages 211-233, April.
  13. Gelper, Sarah & Lemmens, Aurélie & Croux, Christophe, 2007. "Consumer sentiment and consumer spending: decomposing the granger causal relationship in the time domain," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/85457, Katholieke Universiteit Leuven.
  14. 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.
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  22. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-96, July.
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  25. Jaba Ghonghadze & Thomas Lux, 2009. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Kiel Working Papers 1487, Kiel Institute for the World Economy.
  26. Thomas Lux, 2009. "Mass Psychology in Action: Identification of Social Interaction Effects in the German Stock Market," Kiel Working Papers 1514, Kiel Institute for the World Economy.
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Citations

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Cited by:
  1. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets: Evidence from the ECB survey of professional forecasters," Discussion Papers 311, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
  2. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of animal spirits in a bounded rationality model: An application to the euro area," Economics Working Papers 2012-12, Christian-Albrechts-University of Kiel, Department of Economics.
  3. Jan-Christoph Ruelke, 2012. "Do Private Sector Forecasters Desire to Deviate From the German Council of Economic Experts?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 232(4), pages 414-428, July.
  4. Leonardo Morales-Arias & Alexander Dross, 2010. "Adaptive Forecasting of Exchange Rates with Panel Data," Research Paper Series 285, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Wilko Bolt & Maria Demertzis & Cees Diks & Marco van der Leij, 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," DNB Working Papers 329, Netherlands Central Bank, Research Department.
  6. Jaba Ghonghadze & Thomas Lux, 2009. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Kiel Working Papers 1487, Kiel Institute for the World Economy.
  7. Lines, Marji & Westerhoff, Frank, 2009. "Effects of inflation expectations on macroeconomic dynamics: Extrapolative versus regressive expectations," BERG Working Paper Series 68, Bamberg University, Bamberg Economic Research Group.

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