Charles Explorer logo
🇬🇧

Econometrics

Class at Faculty of Social Sciences |
JCM002

Syllabus

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.