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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.
Review of Linear Regressions, including classic linear panel data models. Time Series Analysis. Times Series Regression and Autocorrelation. Trends and Seasonality. The AR(1) And MA(1) Models. Topics in Linear Panel Data Models. Nonlinear Estimation Techniques. M-Estimation. Maximum Likelihood. GMM. Nonlinear Models and Related Topics. Discrete Response Models. Treatment Effect and Regression Discontinuity.Estimation of Dynamic Structural Models.
Upon succesful completion of this course, a student will 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 analyse 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 a 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.