Document Type

Syllabus

Publication Date

Spring 2024

Course Description

  • Regression mechanics (minimizing SSR) and interpreting coefficients
  • Multivariate regression and interpreting coefficients with multiple X variables
  • Functional form (semi-log) and dummy variables
  • Regression diagnostics (R2, RMSE)
  • Monte Carlo simulation with special focus on the sampling distribution, bias, and SE
  • Gauss-Markov Theorem
  • Confidence Intervals and Hypothesis Testing (including joint hypothesis F tests)
  • Heteroskedasticity and its effects
  • Writing your own empirical paper with microdata from CPS (or other IPUMS data)

Student Outcomes

Students will be able to: (1) use the vocabulary and terminology of statistics, such as, standard error and heteroskedasticity (2) work with Excel at a sophisticated level, using add-ins and advanced functions (3) understand key concepts such as the Gauss-Markov Theorem and multicollinearity (4) run Monte Carlo simulations (5) access and analyze data from public microdata sources such as CPS and ACS (6) write an empirical paper

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