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Updated A.Y. 2016-2017

The course is organized in two modules of equal length:

The first module (Static Regression) will familiarize students with the workhorses of empirical work in economics, namely the linear regression model and the ordinary least squares (OLS) estimator, and with the problems that arise when the model assumptions are violated, in particular when the regressors cannot be regarded as exogenous, that is, uncorrelated with the regression errors: 

  • Conditional expectations and best linear predictors
  • The classical linear model and the OLS estimator
  • Sampling properties of OLS
  • Generalized least squares (GLS) and feasible GLS
  • Diagnostic procedures
  • Hypothesis testing and model selection.

The second module (IV & GMM) will pay special attention to the method of instrumental variables as a way of solving the endogeneity problem and the specific issues that arise with the use of this method:

  • The instrumental variables (IV) method
  • Estimation of causal effects
  • Properties of conventional IV estimators under weak instruments
  • Robust inference under weak instruments
  • The generalized method of moments (GMM)
  • Weak identification and robust inference in GMM.