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