Advanced Econometrics

obligatory courses

The lecture and exercises on econometrics are to familiarize students with advanced econometric techniques, their properties and the most important applications.

The lecture concerns: models estimated on the time series and panels as well as the applications of the MLE and GMM estimators.

The lecture is intended for students of the Data Science programme.

The lecture uses concepts in the field of linear algebra, mathematical analysis, probability calculus, descriptive and mathematical statistics and basic econometrics.

The lecture concerns important areas of modern econometrics: models estimated on the time series and panels, and the applications of the Maximum Likelihood and Generalised Method of Moments estimators.

During the lecture, the most important statistical models used in modern econometrics will be discussed. The lecture will be illustrated with empirical examples.

Exercises for the lecture are used to familiarize students with the applications of econometric models discussed at the lecture and to check students' knowledge on an ongoing basis.

Topics:

1. Stochastic process, Spurious regression

2. Stationarity and Nonstationarity, Stationarity testing – Augmented Dickey-Fuller and KPSS test

3. DL and ARDL models

4. ARMA and ARIMA models

5. Seasonality

6. Cointegration and Error correction model

7. Maximum Likelihood Estimators, Likelihood function, properties and testing process

8. Binary dependent variables models (LPM, Logit, Probit and others)

9. Ordered Logit & Probit

10. Multinomial Logit, Conditional Logit

11. Models for count data (Poisson, Negative Binomial and others)

12. Censored data, sample selection, Censored data model (Tobit), Sample selection (Heckmann model)

13. Panel data, Panel data specificity and properties, OLS, Random Effects and Fixed Effects models, Hausman and Individual Effects tests

14. Endogeneity, Instrumental Variables Methods, Instruments choice, Hausman and Sargan tests

15. Generalised Method of Moments

Obligatory literature:

1. Wooldridge, Introductory Econometrics.

2. Greene, Econometric Analysis, Prentice Hall.

3. Enders, W. (2015). Applied econometric time series. Hoboken: Wiley.

4. Charemza, W. W., & Deadman, D. F. (1999). New directions in econometric practice: general to specific modelling, cointegration and vector autoregression. Cheltenham: E. Elgar.

5. Davidson, McKinnon, Estimation and Inference in Econometrics, OUP, 1993.

6. Maddala, Limited Dependent and Qualitative Variables in Econometrics, OUP 1983.

Students will be able to identify features of time series, panel & cross sectional data and select best modeling method.

They will know how to:

1. choose the most appropriate model,

2. implement it in a statistical tool,

3. assess a quality of the model, and

4. interpret obtained results.

K_W01, K_U03, K_U04

1. Class presence according to common University of Warsaw rules,

2. Preparation and presentation of own research project on real data (50%),

3. Written, open book final exam (50%).