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Qualitative and Dummy Variables in Regression Modeling | Finance Train

Quantitative Methods

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17 lessons0%

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?

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.