Econometrics For Dummies by Roberto Pedace

Author:Roberto Pedace
Language: eng
Format: epub, pdf
Publisher: Wiley
Published: 2013-05-31T04:00:00+00:00

Chapter 9

Regression with Dummy Explanatory Variables

In This Chapter

Converting qualitative information into quantitative data

Estimating differences in means between two groups with regression analysis

Performing regression analysis using qualitative and quantitative data simultaneously

Testing for joint significance

Quantitative variables such as years of experience, costs, and prices aren’t the only variables that can have a major influence on the dependent variable in a regression model. Qualitative variables — think gender, race, season of the year, and geographical location — can too. In this chapter, I explain how qualitative variables can be used as independent (or explanatory) variables just as readily as quantitative variables in traditional ordinary least squares (OLS) regression. I also show you all the common ways in which qualitative variables are used in econometric analysis and help you figure out how to interpret the coefficient estimates.

Numbers Please! Quantifying Qualitative Information

Estimating an econometric model requires that all the information be quantified. In other words, numbers must be used to characterize both your quantitative and qualitative variables. Quantitative variables are typically coded with numeric values in the raw data, but qualitative variables are likely to require you to perform some quantification manipulation. In this section you find out how to quantify variables when working with two groups or with multiple groups.

Defining a dummy variable when you have only two possible characteristics

In many cases, the qualitative characteristics you want to include in your econometric analysis have two groups (or categories). In general, you have two groups when sample observations have a “this” or “that” option. For example, in most surveys, gender is classified as either male or female.

If a qualitative characteristic has two groups, you need to create one dummy variable in order to quantitatively capture that attribute. The dummy variable takes the value of 1 if one of the two characteristics is present and 0 if the other characteristic is observed. The group that’s identified (or assigned) 0 values for the created dummy variable is called your reference or base group.

Table 9-1 illustrates how you can create a dummy variable from your original data. Column 1 contains the movie title, and Column 2 contains the lead actor’s name. Column 3 isn’t part of the original data, but I create the variable Female using the information in Column 2. The variable Female is a dummy variable equal to 1 if the lead actor is female and equal to 0 if the lead actor is male. Notice that only one dummy variable is needed to capture two possibilities (in this case, male and female).

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