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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. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
  2. U. Horst & Jose A. Scheinkman, 2010. "Equilibria in Systems of Social Interactions," Levine's Working Paper Archive 506439000000000119, David K. Levine.
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  4. 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.
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  8. Alfarano, Simone & Lux, Thomas, 2005. "A noise trader model as a generator of apparent financial power laws and long memory," Economics Working Papers 2005,13, Christian-Albrechts-University of Kiel, Department of Economics.
  9. Robert J. Shiller, 1980. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," NBER Working Papers 0456, National Bureau of Economic Research, Inc.
  10. Glaeser, Edward L & Sacerdote, Bruce & Scheinkman, Jose A, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, MIT Press, vol. 111(2), pages 507-48, May.
  11. Lux, Thomas, 1997. "Time variation of second moments from a noise trader/infection model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(1), pages 1-38, November.
  12. Chen, Ping, 2002. "Microfoundations of macroeconomic fluctuations and the laws of probability theory: the principle of large numbers versus rational expectations arbitrage," Journal of Economic Behavior & Organization, Elsevier, vol. 49(3), pages 327-344, November.
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  17. Jones, Andrew M., 1994. "Health, addiction, social interaction and the decision to quit smoking," Journal of Health Economics, Elsevier, vol. 13(1), pages 93-110, March.
  18. 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.
  19. George A. Akerlof, 2009. "How Human Psychology Drives the Economy and Why It Matters," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(5), pages 1175-1175.
  20. 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.
  21. Follmer, Hans, 1974. "Random economies with many interacting agents," Journal of Mathematical Economics, Elsevier, vol. 1(1), pages 51-62, March.
  22. Kirman, Alan, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, MIT Press, vol. 108(1), pages 137-56, February.
  23. Sarah Gelper & Aurelie Lemmens & Christophe Croux, 2007. "Consumer sentiment and consumer spending: decomposing the Granger causal relationship in the time domain," Applied Economics, Taylor & Francis Journals, vol. 39(1), pages 1-11.
  24. 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 863-83, November.
  25. Christopher D Carroll, 2002. "Macroeconomic Expectations of Households and Professional Forecasters," Economics Working Paper Archive 477, The Johns Hopkins University,Department of Economics.
  26. Lee, Lung-fei, 2007. "Identification and estimation of econometric models with group interactions, contextual factors and fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 333-374, October.
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Citations

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Cited by:
  1. Ryuichi Yamamoto & Hideaki Hirata, . "Strategy Switching in the Japanese Stock Market," Working Paper 164466, Harvard University OpenScholar.
  2. Lines Marji & Westerhoff Frank, 2012. "Effects of Inflation Expectations on Macroeconomic Dynamics: Extrapolative Versus Regressive Expectations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(4), pages 1-30, October.
  3. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters," Discussion Papers of Business and Economics 1/2012, Department of Business and Economics, University of Southern Denmark.
  4. Jang, Tae-Seok & Sacht, Stephen, 2012. "Identification of Animal Spirits in a Bounded Rationality Model: An Application to the Euro Area," MPRA Paper 37399, University Library of Munich, Germany.
  5. 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.
  6. 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.
  7. 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.
  8. Lux, Thomas, 2012. "Estimation of an agent-based model of investor sentiment formation in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1284-1302.
  9. Thomas Lux, 2013. "Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach," Annals of Finance, Springer, vol. 9(2), pages 217-248, May.

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