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Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey

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  • Lux, Thomas

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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Bibliographic Info

Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number 2008,07.

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Date of creation: 2008
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Handle: RePEc:zbw:cauewp:7327

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Keywords: Business climate; Business cycle forecasts; Opinion formation; Social interactions;

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References

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  1. 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.
  2. Brock, William A & Durlauf, Steven N, 2001. "Discrete Choice with Social Interactions," Review of Economic Studies, Wiley Blackwell, vol. 68(2), pages 235-60, April.
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  5. 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.
  6. 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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  23. 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.
  24. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Teaching an old dog new tricks: Improved estimation of the parameters of SDEs by numerical solution of the Fokker-Planck equation," Stan Hurn Discussion Papers 2006-01, School of Economics and Finance, Queensland University of Technology.
  25. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-96, July.
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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. 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.
  3. Hohnisch, Martin & Westerhoff, Frank, 2008. "Business cycle synchronization in a simple Keynesian macro-model with socially transmitted economic sentiment and international sentiment spill-over," Structural Change and Economic Dynamics, Elsevier, vol. 19(3), pages 249-259, September.
  4. 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.
  5. 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.
  6. Bolt, W. & Demertzis, D. & Diks, C.G.H. & Van der Leij, M.J., 2011. "Complex Methods in Economics: An Example of Behavioral Heterogeneity in House Prices," CeNDEF Working Papers 11-12, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  7. Stefan Reitz & Jan C. Rülke & Georg Stadtmann, 2010. "Regressive Oil Price Expectations Toward More Fundamental Values of the Oil Price," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 230(4), pages 454-466, August.
  8. Sacht, Stephen & Jang, Tae-Seok, 2012. "Identification of Animal Spirits in a Bounded Rationality Model: An Application to the Euro Area," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62071, Verein für Socialpolitik / German Economic Association.
  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.
  10. 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.
  11. 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.
  12. Jean-Philippe Bouchaud, 2012. "Crises and collective socio-economic phenomena: simple models and challenges," Papers 1209.0453, arXiv.org, revised Dec 2012.

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