Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman
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- 2.5.3.0 MAIN CONTENTS
- INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN
| 2.5.2.0 OBJECTIVE The main objective of this unit is to provide basic understanding of the topic Dummy Variable through the use of imitation variables existing or being introduced into a regression equation to solve some variables that are qualitative or immeasurable in numerical terms. 2.5.3.0 MAIN CONTENTS The inherent assumption for the application of dummy variables is that the regression lines for the different groups differ only in the intercept term but have the same slope coefficients. For example (1). You are investigating the relationship between schooling x and earnings y, and you have both males and females in your sample. You would like to see if the sex of the respondent makes a difference. INTRODUCTION TO ECONOMETRICS II ECO 306 NOUN 92 (2). You are investigating the relationship between income and expenditure in Cameroun, and your sample includes both English-speaking and French-speaking households. You would like to find out whether the ethnic difference is relevant. (3). You have data on the growth rate of GDP per capita and foreign aid per capital fora sample of developing countries, of which some are democracies and some are not. You would like to investigate whether the impact of foreign aid on growth is affected by the type of government. A solution to these examples would be to run separate regressions for the two categories and see if the coefficients are different. Alternatively, you could run a single regression using all the observations together, measuring the effect of the qualitative factor with what is known as a dummy variable. This effect has the two important advantages of providing a simple way of testing whether the effect of the qualitative factor is significant The qualitative variable has four categories, and we need to develop a more elaborate set of dummy variables. The standard procedure is to choose one category as the reference category to which the basic equation applies, and then to define dummy variables for each of the other categories. In general, it is good practice to select the dominant or most normal category, if there is one, as the reference category. Accordingly, we will define dummy variables for the other three types. TECH will be the dummy variable for the technical schools TECH is equal to 1 if the observation relates to a technical school, 0 otherwise. Similarly, we will define dummy variables WORKER and VOC for the skilled workers schools and the vocational schools. The regression model is now …[2.80] Where are coefficients that represent the extra overhead costs of the technical, skilled workers, and vocational schools, relative to the cost of a general school. Note that you do not include a dummy variable for the reference category, and that is the reason that the reference category is usually described as the omitted |