Izlence

Estimation of binary dependent variable models. Use of instrumental variables technique. Introduction to forecasting models. Analysis of time series data. Detection of autocorrelation and heteroscedasticity in time series data. Identification of stationarity and cointegration of time series. Causality in time series models.

This course aims to provide the necessary background techniques needed to analyse the behaviour of time series processes; to develop the analytical skills required to characterize the theoretical properties of different time series processes; to provide a sound understanding of the applicability and limitations of the univariate time series methodology as a means for empirical modelling and producing forecasts of economic time series.

After a successful finishing of the course, the student should be able to
1) Demonstrate understanding of verbal, graphical, mathematical and econometric representation of economic ideas and analysis, including the relationship between them.
2) Demonstrate more extensive knowledge and skills of quantitative economics and econometrics.
3) Apply complex ideas to solve problems; reason logically and work analytically; perform with high levels of accuracy.
4) Apply mathematical, statistical and graphical techniques in an appropriate manner and analyze and solve complex problems accurately.
5) Relate economic questions to empirical observations and try to deal with those using econometric models based on sound hypotheses.
6) Have a working knowledge of statistical software and use it to address empirical questions.
7) Demonstrate the ability to apply econometric principles by writing a quality paper and giving oral presentations of the findings of the paper as an individual and as a group member.

Students will use MS Office applications (Word, Excel, and PowerPoint) to work on their assignments/case study/research review, presentations and a Statistical Software (STATA) for the lab applications and their written projects.