1. Econometric concepts

· Conditional distribution and conditional expectation. Notion of regression.

· Conditional expectation function as a best predictor.

· Random sampling. Analogy principle.

· Parametric, nonparametric and semi-parametric estimation.

2. Asymptotic inference

· Why asymptotics? Limitations of exact inference.

· Asymptotic tools: convergence, LLN and CLT, continuous mapping theorems, delta-method.

· Asymptotic confidence intervals and large sample hypothesis testing under random sampling.

· Asymptotics with time series: stationarity, ergodicity, MDS, LLN and CLT, HAC estimation.

3. Linear parametric mean regression

· OLS estimator. Asymptotic inference in linear mean regression model.

· Variance estimation robust to conditional heteroscedasticity.

· Efficiency and GLS estimation.

· Time series linear regression.

4. Nonlinear parametric mean regression

· NLLS estimator. Asymptotic inference in nonlinear mean regression model.

· Computation of NLLS estimates: concentration method.

· Efficiency and Weighted NLLS estimation.

5. Method of maximum likelihood

· Likelihood function and likelihood principle.

· Consistency and asymptotic normality of ML estimators.

· Asymptotic efficiency of the ML estimator. Asymptotic variance estimation.

· ML asymptotic tests: Wald, Likelihood Ratio, Lagrange Multiplier.

· ML estimation for time series models and data.

6. Method of moments

· Moment restrictions and moment functions. Exact identification and overidentification.

· Classical and generalized methods of moments.

· Asymptotic properties of GMM estimators. Efficient GMM.

· Test for overidentifying restrictions.

· Linear instrumental variables regression.

· GMM and time series data. Rational expectations models and other applications.

7. Bootstrap inference

· Empirical distribution. Approximation by bootstrapping.

· Bootstrap confidence intervals and bootstrap hypothesis testing.

· Recentering and pivotization. Asymptotic refinement.

· Bootstrap resampling in cross-sections and in time series.