Introductory Econometrics pp 115-228 | Cite as

# The General Linear Model III

- 2.3k Downloads

## Abstract

In the two preceding chapters we have set forth, in some detail, the estimation of parameters and the properties of the resulting estimators in the context of the standard GLM. We recall that rather stringent assumptions were made relative to the error process and the explanatory variables. Now that the exposition has been completed it behooves us to inquire as to what happens when some, or all, of these assumptions are violated. The motivation is at least twofold. First, situations may, in fact, arise in which some nonstandard assumption may be appropriate. In such a case we would want to know how to handle the problem. Second, we would like to know what is the cost in terms of the properties of the resulting estimators if we operate under the standard assumptions that, as it turns out, are not valid. Thus, even though *we may not know* that the standard assumptions are, in part, violated we would like to know what is the cost in case *they are violated*.

## References

- 1.Anderson, T. W. (1948). On the theory of testing serial correlation.
*Skandinavisk Aktuarietidskrift, 31*, 88–116.Google Scholar - 2.Anderson, T. W. (1971).
*The statistical analysis of time series*. New York: Wiley.Google Scholar - 9.Arnott, R. (1985). The use and misuse of consensus earnings.
*Journal of Portfolio Management, 12*, 18–27.CrossRefGoogle Scholar - 13.Ashley, R., & Patterson, D. M. (2010). Apparent long memory in time series as an artifact of a time-varying mean: Considering alternatives to the fractionally integrated model.
*Macroeconomic Dynamics, 14*, 59–87.CrossRefGoogle Scholar - 45.Bunn, D. (1989). Editorial: Forecasting with more than one model.
*Journal of Forecasting, 8*, 161–166.CrossRefGoogle Scholar - 65.Cochrane, D., & Orcutt, G. H. (1949). Applications of least squares to relations containing autocorrelated error terms.
*Journal of the American Statistical Association, 44*, 32–61.Google Scholar - 80.Dhrymes, P. J. (1971).
*Distributed lags: Problems of estimation and formulation*. San Francisco: Holden–Day.Google Scholar - 98.Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression, I.
*Biometrika, 37*, 408–428.Google Scholar - 280.Sargan, J. D. (1964). Wages and prices in the United Kingdom: A study in econometric methodology. In P. E. Hart et al. (Eds.),
*Econometric analysis for National Economic Planning*. London: Butterworths.Google Scholar