Abstract
So far we have considered only one regressor X besides the constant in the regression equation. Economic relationships usually include more than one regressor. For example, a demand equation for a product will usually include real price of that product in addition to real income as well as real price of a competitive product and the advertising expenditures on this product. In this case
where Y i denotes the i-th observation on the dependent variable Y, in this case the sales of this product. X ki denotes the i-th observation on the independent variable X k for k = 2, … , K in this case, own price, the competitor’s price and advertising expenditures. α is the intercept and β 2, β 3, … , β K are the (K − 1) slope coefficients. The ui’s satisfy the classical assumptions 1–4 given in Chapter 3. Assumption 4 is modified to include all the X’s appearing in the regression, i.e., every X k for k = 2, ’ , K, is uncorrelated with the ui’s with the property that \(\sum\nolimits^{n}_{i=1} (X_{ki} - \bar{X}_k)^2/n \ \hbox{where} \ \bar{X}_k = \sum\nolimits^{n}_{i=1} X_{ki}/n\) has a finite probability limit which is different from zero.
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Notes
- 1.
This chapter draws upon the material in Kelejian and Oates (1989) and Wallace and Silver (1988). Several econometrics books have an excellent discussion on dummy variables, see Gujarati (1978), Judge et al. (1985), Kennedy (1992), Johnston (1984) and Maddala (2001), to mention a few. Other readings referenced in this chapter include:
References
Footnote
This chapter draws upon the material in Kelejian and Oates (1989) and Wallace and Silver (1988). Several econometrics books have an excellent discussion on dummy variables, see Gujarati (1978), Judge et al. (1985), Kennedy (1992), Johnston (1984) and Maddala (2001), to mention a few. Other readings referenced in this chapter include:
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Baltagi, B.H. (2011). Multiple Regression Analysis. In: Econometrics. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20059-5_4
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