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

Journal of Economic Behavior and Organization

Volumn 72, Issue 2, 2009, Pages 638-655

  Lux, Thomas ^a,b  

^a UNIVERSITY OF KIEL   (Germany)

^b KIEL INSTITUTE FOR THE WORLD ECONOMY   (Germany)

Author keywords

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

Indexed keywords

EID: 70350369760     PISSN: 01672681     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jebo.2009.07.003     Document Type: Article

References (43)

1. Akerlof G., and Shiller R. Animal Spirits: How Human Psychology Drives the Economy and Why it Matters for Global Capitalism (2009), Princeton University Press, Princeton
2. Alfarano S., and Lux T. A noise trader model as a generator of apparent financial power laws and long memory. Macroeconomic Dynamics 11 Suppl. 1 (2007) 80-101
3. Aoki M. New Approaches to Macroeconomic Modeling: Evolutionary Stochastic Dynamics, Multiple Equilibria and Externalities as Field Effects (1996), Cambridge University Press, Cambridge
4. Batholomew D. Stochastic Models for the Social Science (1973), Wiley, New York
5. Benabou R. Workings of a city: location, education and production. Quarterly Journal of Economics 108 (1993) 619-652
6. Blume L., and Durlauf S. Equilibrium concepts for social interaction models. International Game Theory Review 5 (2003) 193-209
7. Box-Steffensmeier J., and Smith R. Investigating political dynamics using fractional integration methods. American Journal of Political Science 42 (1998) 661-689
8. Brock W., and Durlauf S. Interaction-based models. In: Heckman J., and Leamer E. (Eds). Handbook of Econometrics vol. V (2001), Elsevier, Amsterdam 3297-3380
9. Brock W., and Durlauf S. Discrete choice with social interactions. Review of Economic Studies 68 (2001) 235-260
10. Carroll C. Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics 118 (2003) 209-248
11. Chen P. Microfoundations of macroeconomic fluctuations and the laws of probability theory: the principle of large numbers versus rational expectations arbitrage. Journal of Economic Behavior and Organization 49 (2002) 307-326
12. Cutler D., Poterba J., and Summers L. What moves stock prices?. Journal of Portfolio Management 4 (1989) 4-12
13. Diebold F., Gunther T., and Tay A. Evaluating density forecasts with applications to financial risk management. International Economic Review 39 (1998) 863-883
14. European Commission, 2007. The Joint Harmonised EU Programme of Business and Consumer Surveys User Guide. http://ec.europa.eu/economy_finance/indicators/business_consumer_surveys/userguide_en.pdf.
15. Ethier S., and Kurtz T. Markov Processes (1986), Wiley, New York
16. Gelper S., Lemmens A., and Croux C. Consumer sentiment and consumer spending: decomposing the Granger causal relationship in the time domain. Applied Economics 39 (2007) 1-11
17. Geweke J., and Porter-Hudak S. The estimation and application of long-memory time series models. Journal of Time Series Analysis 4 (1983) 221-238
18. Ghonghadze, J., Lux, T., 2009. Modeling the dynamics of EU sentiment indicators: an interaction-based approach. Working Paper no. 1487, Kiel, Institute for the World Economy.
19. Glaeser E., Sacerdote B., and Scheinkman J. Crime and social interactions. Quarterly Journal of Economics 111 (1996) 507-548
20. Horst U., and Scheinkman J. Equilibria in systems with social interactions. Journal of Economic Theory 130 (2006) 44-77
21. Horst U., and Rothe C. Queuing, social interactions, and the microstructure of financial markets. Macroeconomic Dynamics 12 (2008) 211-233
22. Hüfner F., Schröder, M., 2002. Prognosegehalt von ifo-Geschäftserwartungen und ZEW-Konjunkturerwartungen: Ein ökonometrischer Vergleich. Jahrbücher für Nationalökonime und Statistik 222, pp. 316-336.
23. Hurn, A., Jeisman, J., Lindsay, K., 2006. Teaching an old dog new tricks: improved estimation of the parameters of stochastic differential equations by numerical solution of the Fokker-Planck equation. Manuscript, Queensland University of Technology, Brisbane.
24. Hurn A., Jeisman J., and Lindsay K. Seeing the wood for the trees: a critical evaluation of methods to estimate the parameters of stochastic differential equations. Journal of Financial Econometrics 5 (2007) 390-455
25. Iacus S. Simulation and Inference for Stochastic Differential Equations (2008), Springer, Berlin
26. Jensen B., and Poulsen R. Transition densities of diffusion processes: numerical comparison of approximation techniques. Journal of Derivatives 9 (2002) 18-34
27. Kirman A. Ants, rationality, and recruitment. Quarterly Journal of Economics 118 (1993) 137-156
28. Kraft M., Landes T., and Weise P. Dynamic aspects of a stochastic business cycle model. Methods of Operations Research 53 (1986) 445-453
29. Lee L.-F. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics 140 (2007) 333-374
30. Ludvigson S. Consumer confidence and consumer spending. Journal of Economic Perspectives 18 (2004) 29-50
31. Lux T. Herd behaviour, bubbles and crashes. Economic Journal 105 (1995) 881-896
32. Lux T. Time variation of second moments from a noise trader/infection model. Journal of Economic Dynamics and Control 22 (1997) 1-38
33. Lux, T., 2009. Mass psychology in action: identification of social interaction effects in the German stock market. Working Paper no. 1514, Kiel, Institute for the World Economy.
34. Pedersen A. Uniform Residuals for Discretely Observed Diffusion Processes (1994), Department of Theoretical Statistics, University of Aarhus
35. Poulsen, R., 1999. Approximate maximum likelihood estimation of discretely observed diffusion processes. Manuscript, University of Aarhus.
36. Risken H. The Fokker-Planck Equation: Methods of Solutions and Applications (1989), Springer, Berlin
37. Seydel R. Tools for Computational Finance (2002), Springer, Berlin
38. Souleles N. Expectations, heterogeneous forecast errors, and consumption: micro evidence from the Michigan consumer sentiment surveys. Journal of Money, Credit and Banking 36 (2004) 39-72
39. Taylor K., and McNabb R. Business cycles and the role of confidence: evidence for Europe. Oxford Bulletin of Economics and Statistics 69 (2007) 185-208
40. Thomas J. Numerical Partial Differential Equations (1995), Springer, Berlin
41. Van Kampen N. Stochastic Processes in Physics and Chemistry. 3rd ed. (2007), Elsevier, Amsterdam
42. Weidlich W. Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences (2000), Harwood Academic Publishers, Amsterdam
43. Weidlich W., and Haag G. Concepts and Methods of a Quantitative Sociology (1983), Springer, Berlin