There are certain circumstances in which the assumption of constant error variance, homoscedasticity, in the linear model is not tenable. For example, in cross-sectional analysis in economics, the units under investigation are usually firms, households, or individuals, and the degree to which the linear equation explains their behavior may depend upon their specific characteristics. We illustrate this point by the use of three examples.
KeywordsError Variance Maximum Likelihood Estimator Likelihood Ratio Statistic Advertising Expenditure Chow Test
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.
Aitchison, J. and Silvey, S. D. (1960). Maximum-likelihood estimation procedures and associated tests of significance. Journal of The Royal Statistical Society
, B, 22
, 154–171.Google Scholar
Amemiya, T. (1977). A note on a heteroscedastic model. Journal of Econometrics
, 365–370.CrossRefGoogle Scholar
Breusch, T. S. and Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica
, 1287–1294.CrossRefGoogle Scholar
Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica
, 591–605.CrossRefGoogle Scholar
Crain, W. M. and Zardkoohi, A. (1978). A test of the property-rights theory of the firm: water utilities in the United States. Journal of Law and Economics
, 395–408.CrossRefGoogle Scholar
Duesenberry, J. S. (1949). Income, Saving and the Theory of Consumer Behavior.
Cambridge, MA: Harvard University Press.Google Scholar
Gleisjer, H. (1969). A new test for heteroscedasticity. Journal of The American Statistical Association
, 316–323.CrossRefGoogle Scholar
Goldfeld, S. M. and Quandt, R. E. (1965). Some tests for homoscedasticity. Journal of the American Statistical Association
, 539–547.CrossRefGoogle Scholar
Goldfeld, S. M. and Quandt, R. E. (1972). Nonlinear Methods in Econometrics.
Amsterdam: North Holland.Google Scholar
Greenberg, E. (1980). Finite sample moments of a preliminary test estimator in the case of possible heteroscedasticity. Econometrica
, 1805–1813.CrossRefGoogle Scholar
Harvey, A. C. (1974). Estimation of parameters in a heteroscedastic regression model. Paper presented at the European Meeting of The Econometric Society, Grenoble, September.Google Scholar
Harvey, A. C. (1976). Estimating regression models with multiplicative heteroscedasticity. Econometrica
, 461–465.CrossRefGoogle Scholar
Imhof, J. P. (1961). Computing the distribution of quadratic forms in normal variables. Biometrika
, 419–426.Google Scholar
Jayatissa, W. A. (1977). Tests of equality between sets of coefficients in two linear regressions when disturbance variances are unequal. Econometrica
, 1291–1292.CrossRefGoogle Scholar
Mincer, J., (1974). Schooling, Experience, and Earnings.
New York: National Bureau of Economic Research.Google Scholar
Prais, S. J. and Houthakker, H. S. (1955). The Analysis of Family Budgets.
New York: Cambridge University Press.Google Scholar
Schmidt, P. and Sickles, R. (1977). Some further evidence on the use of the Chow test under heteroscedasticity. Econometrica
, 1293–1298.CrossRefGoogle Scholar
Taylor, W. E. (1978). The heteroscedastic linear model: exact finite sample results. Econometrica
, 663–675.CrossRefGoogle Scholar
Toyoda, T. (1974). Use of the Chow test under heteroscedasticity. Econometrica
, 601–608.CrossRefGoogle Scholar
White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica
, 817–838.CrossRefGoogle Scholar
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