This course aims to give students basic understanding of econometric theories and applying econometric techniques of regression analysis. Many applications are examined to achieve this goal. Emphasis is placed on the classical linear regression model, least-squares estimation, hypothesis testing, and model building. Various econometric models are adopted to analyze practical economic problems and make forecasts. Furthermore, in this course students are trained to use computer statistics software (i.e Stata 11).

This course introduces students to the time series methods and practices which are most relevant to the analysis of economic and financial time series with focus on applications in macroeconomics, international finance, and finance. We will cover univariate and multivariate models of stationary and nonstationary time series in the time domain. This course prepares you for empirical analysis towards satisfactory progress in your PhD thesis or graduate studies. The emphasis is on modelling skills and practical applications.

The goals of the course are threefold: (1) develop a comprehensive set of tools and techniques for analyzing various forms of univariate and multivariate time series and understanding the current literature in applied time series econometrics; (2) survey some of the current research topics in time series econometrics; (3) show how to use time series tools in applications using the software such as EViews, Ox, PcGive, and JMulti.

More than other courses, this course tries to deal with the genuine subtlety of honest data analysis and the often misunderstood role that mathematical models play in our understanding of empirical data.

Students will be given fundamental grounding in the use of some widely used tools, but much of the energy of the course is focus on individual investigation of time series. Active participation in the class is very important. This class is more about the opportunity for individual and team discoveries than it is about mastering a fixed set of techniques.

The topics we will cover this course include:

Stationary Univariate Models. Wold decomposition theorem, Difference equations, Smoothing, Seasonal Adjustment, ARMA models and Box-Jenkins methodology, Model Selection, Forecasting methodology.

Nonstationary Univariate Models. Trend/Cycle decomposition, Beveridge-Nelson decomposition, Deterministic and stochastic trend models, Unit root tests, Stationarity tests

Structural Change And Nonlinear Models.  Tests for structural change with unknown change point. Estimation of linear models with structural change. GARCH Models, Regime switching models.

Stationary Multivariate Models. Dynamic simultaneous equations models, Vector autoregression (VAR) models, Granger causality, Impulse response functions, Variance decompositions, Structural VAR models.

Nonstationary Multivariate Models. Spurious regression, Cointegration, Granger representation theorem, Vector error correction models (VECMs), Structural VAR models with cointegration, Testing for cointegration, Estimating the cointegrating rank, Estimating cointegrating vectors.