Course Content (Syllabus)
• Multiple regression models: least squares estimation, correlated omitted
variable problem, dummy variables, heteroscedasticity,
multicollinearity.
• Endogeneity problems: Hausman test, two stage least squares
estimation, instrumental variables estimation.
• Simultaneous equation models: two- and three-stage least squares
estimation, generalized method of moments (GMM) estimation,
identification problem
• Methods for time series data: autocorrelation, ARCH, unit roots,
cointegration, vector autoregressive (VAR) models
• Qualitative and limited dependent variable models: logit/ probit models,
self-selection models, Tobit model.
• Panel data models: fixed vs random effects models.