Abstract
In this chapter, we examine the General Linear Model (GLM), an important topic for econometrics and statistics, as well as other disciplines. The term general refers to the fact that there are no restrictions in the number of explanatory variables we may consider, the term linear refers to the manner in which the parameters enter the model. It does not refer to the form of the variables. This is often termed in the literature the regression model, and analysis of empirical results obtained from such models as regression analysis.
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Notes
- 1.
The term estimator recurs very frequently in econometrics; just to fix its meaning in this chapter and others we define it as: an estimator is a function of the data only (x′s and y′s), say h(y,X) that does not include unknown parameters.
- 2.
The term consistent generally means that as the sample, T, tends to infinity the estimator converges to the parameter it seeks to estimate. Since the early development of econometrics it meant almost exclusively convergence in probability. This is the meaning we shall use in this and other chapters, i.e. an estimator is consistent for the parameter it seeks to estimate if it converges to it in any fashion that implies convergence in probability.
- 3.
In the context of Eq. (10.79) the notation ∼ is to be read “behaves like”.
- 4.
Occasionally this test is referred to as a test of significance of R 2.
- 5.
The sample size is assumed to be the same for all firms.
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Dhrymes, P.J. (2013). The General Linear Model. In: Mathematics for Econometrics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8145-4_10
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