1. Econometrics -- basic methods and goals. Examples of econometric models. Classification. Prediction. (1 lecture)
2. The least square method (LSM). The setting of the problem. The determining of the optimal values of the parameters. The error of the approximation. The algebraic properties of the model. (1--2 lectures)
3. The classical single-equation linear econometric model. Model assumptions. The least square estimation of the structural parameters of the model. The statistical verification of the model. Example: The Cobb-Douglas production function. (4--5 lectures)
4. The least square method in nonlinear models. Example: Consumption demand modeling - the Törnqvist function. (1 lecture)
5. Large-sample theory. Review of limit theorems of random variables. Model assumptions. Asymptotic properties of least square estimators. The statistical erification of the model. Example: Rational expectations theory. (4 lectures)
6. Econometric models based on time series. Stationarity. Classical linear models ARMA and ARIMA. Heteroskedastic models ARCH and GARCH. Prediction.(2--3 lectures)
Type of course
W.H.Greene, Econometric Analysis, Prentice Hall, 2000.
F.Hayashi, Econometrics. Princeton University Press, 2000.
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Mathematics
- Master's degree, second cycle programme, Mathematics
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: