• For example, in the model
wage 0 1sw 2 mm 3 sm 4 educ u
• Sm, mm, and sw were binary variables for the categories of single men, married men
and single women. Married women was the baseline category.
• 1 – expected difference in wages between single women and married women • 2 – expected difference in wages between married men and married women • 3 – expected difference in wages between single men and married women
capture the additional effect when both of the dummy variables are equal to one.
• For example, sometimes when analyzing the wages of married and single men and
women, instead of a dummy variable for each non-baseline category, researchers include dummy variables for female and married, and an interaction term that is both of these multiplied together.
• Also, you may have with you one A4 sheet of notes, handwritten on one side. • The definition of “handwritten” is: the note sheet must be produced with no other
Multiple Regression Analysis with Qualitative Information
• Therefore, when years of education (educ) is held constant, the coefficients have these
interpretations:
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2010 22 February 2010
Administrative Matters
• You are probably wondering about the scope of the midterm examination, which will
Multiple Regression Analysis with Qualitative Information
• Another common way of analyzing multiple categories is through interaction terms. • An interaction term is two dummy variables multiplied together. It allows you to
analyze data where the variables are qualitatively measured.
• The method is to select one category as a baseline category and then use binary
variables to designate the other categories.
Multiple Regression Analysis with Qualitative Information
• Why would you do this? Suppose that we are looking at wages and the baseline
category is single men.
be held in class on Friday, March 12th.
• It will cover capters 2, 3, 4, and 7 (i.e., it will cover up to multiple regression analysis
with qualitative information – the next topic, heteroskedasticity, will not be on the midterm).
Multiple Regression Analysis with Qualitative Information
• The coefficients on the binary variables then represent expected differences in the
independent variable between the category of the binary variable and the baseline category.
technology than a pen or pencil.
Multiple Regression Analysis with Qualitative Information
• Last time, we discussed the method of using binary variables, or dummy variables, to
• The interaction term is a way to measure whether or not the difference in wages