Reference no: EM133727945

Assignment: Income Model Regression Econometric Analysis

This covers model specification, OLS estimation, inference, diagnostics, remedies, forecasting, and causal analysis. Let me know if you need any clarification or have additional requirements.

You are provided with a data set of 500 observations that includes information on an individual's income, age, highest education level, gender, and marital status.

1. Specify an appropriate multiple linear regression model using income as the dependent variable and the other variables as the independent variables. Discuss your choice of independent variables and expected signs on the coefficients.

2. Estimate the parameters of your proposed model using OLS. Interpret each estimated coefficient and indicate whether it is statistically significant.

3. Conduct appropriate specification tests to determine whether your model suffers from omission of relevant variables, inclusion of irrelevant variables, multi-collinearity, heteroscedasticity, or autocorrelation. Clearly state your null and alternative hypotheses and use appropriate test statistics.

4. If any specification issues are detected in (3), propose solutions to address the problems. Re-estimate the model incorporating your proposed solutions.

5. Based on your revised model, forecast the income for a 35 year old, married female with a master's degree. Clearly show your workings.

6. Determine whether your model is appropriate for causal analysis. If so, estimate the causal impact of education level on income using your model, explaining your methodology. If not, discuss why causal inference is problematic.

7. Outline potential violations of the classical linear model assumptions in the context of this problem.

8. Explain how each violation could impact coefficient estimates and inference.