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Qualitative and Dummy Variables in Regression Modeling

CFA® Exam Level 2

This lesson is part 12 of 17 in the course Quantitative Methods
  • Handle qualitative independent variables with a quantitative proxy or use a dummy variable.
  • When using a dummy independent variables (such as assigning a number to the degree of consumer confidence), define a collectively exhaustive set of “j” categories, then j-1 (“j minus one”) will give you the number of dummy variables for inclusion in your model.
  • Models with dummy independents can easily be misspecified.

Model types with qualitative dependent variables

  • Probit models – based on a normal distribution and attempt to estimate the probability that the dependent variable will equal 1.
  • Logit models – based on the logistic distribution and like Probit models, they attempt to estimate the probability that the dependent variable will equal 1.
  • Discriminant Analysis – creates a score and if the score crosses a threshold then the dependent variable is assigned a 1.

Looking at the big picture, you want your multiple regression model to:

  1. Have a good theoretical basis and;
  2. Pass the most stringent statistical tests (refer back to the sub-section “Assumption Violations”).
Previous Lesson

‹ Regression Analysis and Assumption Violations

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Time Series Analysis: Simple and Log-linear Trend Models ›

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In this Course

  • CFA L2: Quantitative Methods – Introduction
  • Quants: Correlation Analysis
  • Quants: Single Variable Linear Regression Analysis
  • Standard Error of the Estimate or SEE
  • Confidence Intervals (CI) for Dependent Variable Prediction
  • Coefficient of Determination (R-Squared)
  • Analysis of Variance or ANOVA
  • Multiple Regression Analysis
  • Multiple Regression and Coefficient of Determination (R-Squared)
  • Fcalc – the Global Test for Regression Significance
  • Regression Analysis and Assumption Violations
  • Qualitative and Dummy Variables in Regression Modeling
  • Time Series Analysis: Simple and Log-linear Trend Models
  • Auto-Regressive (AR) Time Series Models
  • Auto-Regressive Models – Random Walks and Unit Roots
  • ARMA Models and ARCH Testing
  • How to Select the Most Appropriate Time Series Model?

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