This course covers the statistical methods needed to understand empirical economic research and to plan and execute independent research projects. Topics include regressions, statistical inference, asymptotic theory, generalized least squares, instrumental variables, maximum likelihood, big data, simultaneous equations models. (Main reference: Davidson and Mackinnon, "Econometric Theory and Methods", 2009) link
Part I of the course covers commonly used estimation techniques, such as Ordinary Least Squares, Maximum Likelihood, Generalized Least Squares. The Generalized Method of Moments framework is introduced and several popular estimators (IV, 2SLS, 3SLS, FE, RE) are derived from it. Part II provides a survey of the theory of time series methods in econometrics. Topics include univariate stationary and non-stationary models, vector autoregressions, cointegration, high-dimensional predictive models and volatility models. (Main reference: Hayashi, "Econometrics", 2000. Hamilton, "Time Series Analysis", 1994 ) link
The course provides a survey of the theory and application of univariate time series methods in econometrics. Topics covered will include stationary and non-stationary models, frequency domain methods, models for volatility, structural breaks, time-varying parameters and predictions with many covariates. The empirical applications in the course will be drawn primarily from macroeconomics and finance.
All materials are provided via Blackboard. Please register for the course with your Blackboard account. link
This seminar is intended to give participants exposure to state of the art research in time series econometrics and its applications in empirical finance and macroeconomics. Doctoral students are encouraged to present first research projects in these areas during the seminar. pdf