Abstract
The regression model is a random model in the sense that an error term is included in the equation linking the dependent variable to the explanatory variables. The ordinary least squares method—the most frequently used estimation method—supposes (i) the absence of autocorrelation of errors and (ii) the homoskedasticity of errors, i.e., the fact that the variance of the errors is constant. When this second assumption is violated, we speak of heteroskedasticity: the variance of the errors is no longer constant. This chapter concentrates on the problems of autocorrelation and heteroskedasticity of errors. It presents the appropriate estimation methods, as well as the sources, tests, and solutions to autocorrelation and heteroskedasticity. It also provides several empirical applications to illustrate the various theoretical concepts.
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Notes
- 1.
It is assumed here that there is no autocorrelation of errors.
- 2.
Such a statistic is called a Lagrange multiplier statistic.
- 3.
It is assumed here that the errors are homoskedastic.
- 4.
Let us mention, however, that Farebrother (1980) tabulated the critical values of the statistic DW in the absence of a constant term.
- 5.
It is assumed here that the lagged dependent variable is not among the explanatory variables. We will come back to the Box-Pierce test in Chap. 7.
- 6.
It is unnecessary to introduce a constant term in the regression of \(e_{t}\) on \(e_{t-1}\) since the mean of the residuals is zero.
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Mignon, V. (2024). Heteroskedasticity and Autocorrelation of Errors. In: Principles of Econometrics. Classroom Companion: Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-52535-3_4
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