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A Time Series Model with Qualitative Variables

  • D. M. Grether
  • G. S. Maddala

Abstract

The literature on time-series analysis has been largely confined to the analysis of time-series data where:
  1. (i)

    The number of observations is large, and

     
  2. (ii)

    the variables are all observed as continuous variables.

     

Keywords

Time Series Model Consistent Estimate Party Identification Economic Expectation Model Variable Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Amemiya, T.: Regression Analysis When the Dependent Variable Is Truncated Normal. Econometrica 41, 1973, 997–1016.CrossRefGoogle Scholar
  2. Brody, R.: Stability and Change in Party Identification: Presidential to Off-Years. Paper presented at 1977 annual meeting of the American Political Science Association. Momeographed, 1977.Google Scholar
  3. Campbell, A. et al.: The American Voter. New York 1960.Google Scholar
  4. Chamberlain, G.: Analysis of Covariance with Qualitative Data. Review of Economic Studies 47, 1980, 225–238.CrossRefGoogle Scholar
  5. Converse, P.E.: The Nature of Belief Systems in Mass Publics. Ideology and Discontent. Ed. by D.E. Apter. Glenco, Illinois, 1964.Google Scholar
  6. Converse, P.E., and G.B. Markus: Plus ca change...: The New CPS Election Study Panel. American Political Science Review 73, 1979, 32–49.CrossRefGoogle Scholar
  7. Dreyer, E.: Change and Stability in Party Identification. Journal of Politics 25, 1973, 712–722.Google Scholar
  8. Fiorina, M.P.: Retrospective Voting in American National Elections. Mimeographed. Pasadena 1979.Google Scholar
  9. Grether, D.M.: Correlations with Ordinal Data. Journal of Econometrics 2, 1974, 241–246.CrossRefGoogle Scholar
  10. —: On the Use of Ordinal Data in Correlation Analysis. American Sociological Review 41, 1976, 908–912.CrossRefGoogle Scholar
  11. —: Bayes Rule as a Descriptive Model: The Representativeness Heuristic. Quarterly Journal of Economics 95, 1980, 537–557.CrossRefGoogle Scholar
  12. Heckman, J.: Statistical Models for Discrete Panel Data. Center for Mathematical Studies in Businiess and Economics Report No. 7902, University of Chicago, 1979.Google Scholar
  13. Howell, W.C.: An Evaluation of Subjective Probability in a Visual Discrimination Task. Journal of Experimental Psychology 75, 1967, 979–986.CrossRefGoogle Scholar
  14. —: Uncertainty from Internal and External Sources: A Clear Case of Overconfidence. Journal of Experimental Psychology 89, 1971, 240–243.CrossRefGoogle Scholar
  15. Kahneman, D., and A. Tversky: Subjective Probability: A Judgment of Representativeness. Cognitive Psychology 3, 1972, 430–454.CrossRefGoogle Scholar
  16. Klein, L.R.: The Estimation of Distrubted Lags. Econometrica 26, 1958, 553–565.CrossRefGoogle Scholar
  17. Lee, L.-F.: Strong Consistency of the Tobit Estimator in the Presence of Serially Correlated Disturbance. Minneapolis 1980.Google Scholar
  18. Robinson, P.M.: On the Asymptotic Properties of Estimators of Models Containing Limited Dependent Variables. Mimeographed. Vancouver 1980.Google Scholar
  19. Tversky, A., and D. Kahneman: Judgment under Uncertainty: Heuristics and Biases. Science 185, 1974, 1124–1131.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1982

Authors and Affiliations

  • D. M. Grether
    • 1
  • G. S. Maddala
    • 1
  1. 1.California Institute of TechnologyPasadenaUSA

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