Qualitative and Dummy Variables in Regression Modeling

  • 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”).

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.