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Qualitative and Dummy Variables in Regression Modeling

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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:

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

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Previous Lesson

Regression Analysis and Assumption Violations

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Quantitative Methods

17 lessons

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?

Topics

Risk Management

Statistical Methods