TIME SERIES ECONOMETRICS FALL 2010

TIME SERIES ECONOMETRICS FALL 2010 DANIEL PREVE Time Series Econometrics is a course in time series analysis, with emphasis on economic applications, ...

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TIME SERIES ECONOMETRICS FALL 2010 DANIEL PREVE

Time Series Econometrics is a course in time series analysis, with emphasis on economic applications, designed for Master and PhD students in statistics and economics. The course is self-contained in the sense that no prior knowledge of time series analysis is required. However, basic familiarity with linear algebra and calculus is recommended (appendices covering these topics are available in the textbook). Objective The objective of the course is to provide the students with a thorough understanding of some basic topics in modern time series analysis. The intention is that the course material will provide a solid foundation for studies/research in, for example, applied economics.

Content Listed below are the lecture topics and, in some cases, examples of material covered by the lectures. (1) Introduction & Overview. (2) Difference Equations. (3) Lag Operators. (4) Stationary ARMA Processes: stationarity, ergodicity. (5) Forecasting: Wold’s decomposition, tests for forecast accuracy. (6) Maximum Likelihood Estimation. (7) Asymptotic Distribution Theory: limit theorems for serially dependent observations. (8) Linear Regression Models (general time series setting). (9) Vector Processes. (10) The Kalman Filter: state-space representation. (11) Generalized Method of Moments. (12) Models of Nonstationary Time Series: unit root theory. (13) Cointegration (introduction). (14) Time Series Models of Heteroskedasticity: ARCH, GARCH. (15) Selected Topics: long memory processes. Examination There will be one exam and one mandatory hand-in assignment. PhD students in statistics will be required to solve some additional problems on the exam. 1

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DANIEL PREVE

Textbook Hamilton J. D. (1994). Time Series Analysis. Princeton: Princeton University Press.