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Businessmen's Expectations Are Neither Rational nor Adaptive

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  • Nerlove, Marc
  • Schuermann, Til

Abstract

A framework which allows for the joint testing of the adaptive and rational expectations hypotheses is presented. We assume joint normality of expectations, realizations and variables in the information set, allowing for parsimonious interpretation of the data; conditional first moments are linear in the conditioning variables, and we can easily recover regression coefficients from them and test simple hypotheses by imposing zero restrictions on these coefficients. The nature of the data, which are responses to business surveys and are all categorical, requires simulation techniques to obtain full information maximum likelihood estimates. We use a latent variable model which allows for the construction of a simple likelihood function. However, this likelihood contains multi-(four)dimensional integrals, requiring simulators to evaluate. Simulated maximum-likelihood estimation is carried out using the Geweke-Hajivassilou-Keane (GHK) method, which is consistent and has low variance. The latter is crucial when maximizing the log-likelihood directly. Identification of the parameters is achieved by placing restrictions on the response thresholds and/or the variances. We find that we can reject both hypotheses. --

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Bibliographic Info

Paper provided by ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research in its series ZEW Discussion Papers with number 97-01.

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Date of creation: 1997
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Handle: RePEc:zbw:zewdip:9701

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  1. Vassilis A. Hajivassiliou & Daniel L. McFadden, 1993. "The Method of Simulated Scores for the Estimation of LDV Models," Working Papers _023, Yale University.
  2. Pesaran, M. Hashem & Samiei, Hossein, 1995. "Limited-dependent rational expectations models with future expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 19(8), pages 1325-1353, November.
  3. Pakes, Ariel S, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," Econometrica, Econometric Society, vol. 54(4), pages 755-84, July.
  4. Nerlove, Marc & Ross, David & Willson, Douglas, 1993. "The importance of seasonality in inventory models : Evidence from business survey data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 105-128.
  5. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-57, September.
  6. Marc Nerlove, 1981. "Expectations, Plans and Realizations: In Theory and Practice," Discussion Papers 511, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  7. Marc Nerlove, 1967. "Distributed Lags and Unobserved Components in Economic Time Series," Cowles Foundation Discussion Papers 221, Cowles Foundation for Research in Economics, Yale University.
  8. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
  9. Lovell, Michael C, 1986. "Tests of the Rational Expectations Hypothesis," American Economic Review, American Economic Association, vol. 76(1), pages 110-24, March.
  10. Til Schuermann & Melvyn Weeks, . "Why You May Need Not Worry About Finite Sample Bias In Simulated Maximum Likelihood Estimation," Discussion Papers 94/18, Department of Economics, University of York.
  11. Ulf Olsson, 1979. "Maximum likelihood estimation of the polychoric correlation coefficient," Psychometrika, Springer, vol. 44(4), pages 443-460, December.
  12. Wai-Yin Poon & Sik-Yum Lee, 1987. "Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients," Psychometrika, Springer, vol. 52(3), pages 409-430, September.
  13. Vassilis A. Hajivassiliou & Axel Borsch-Supan, 1990. "Smooth Unbiased Multivariate Probability Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models," Cowles Foundation Discussion Papers 960, Cowles Foundation for Research in Economics, Yale University.
  14. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
  15. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-63, May.
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Cited by:
  1. Yu, Ge, 2003. "Comparing Expectations and Outcomes: Application to UK Data," MPRA Paper 502, University Library of Munich, Germany, revised 2005.

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