Fit Linear Regression: Analyze Economic Impact of Coefficients

Q: Fit Linear Regression: Analyze Economic Impact of Coefficients

This question evaluates understanding of linear regression modeling, coefficient estimation and economic interpretation, hypothesis testing using t-statistics and p-values, nested F-tests for model comparison, and the ability to connect estimated betas to economic meaning in a financial tabular dataset.

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Question

Scenario

You are given a tabular financial dataset df where the column target is the dependent variable (e.g., next-period return or excess return), and all other columns are candidate predictors/features.

Task

Fit an Ordinary Least Squares (OLS) regression with target as the dependent variable and all other columns as predictors.
Report each estimated coefficient (beta) and interpret its economic meaning.
Describe how you would sequentially test the marginal contribution of each feature (i.e., whether adding/removing a feature improves the model), referencing t-tests and nested F-tests.

Notes

Use Python statsmodels OLS.
Refer to summary(), t-statistics, p-values, adjusted R², and nested F-tests. Stepwise procedures are acceptable if justified.

Fit Linear Regression: Analyze Economic Impact of Coefficients

Scenario

Task

Notes

Solution

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