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Unformatted text preview: MIT OpenCourseWare http://ocw.mit.edu 14.384 Time Series Analysis Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms . Introduction 1 14.384 Time Series Analysis, Fall 2007 Professor Anna Mikusheva Paul Schrimpf, scribe September 6, 2007 Lecture 1 Stationarity, Lag Operator, ARMA, and Covariance Structure Introduction History – popular in early 90s, making comeback now. Current comeback is largely due to macroapplications. Can roughly divide time series into macro and finance related stuff. Macro stuff mostly focuses on means. Finance on higher moments. Macro limited by short horizon of data available. Outline Can divide course into 1. Classics stationary nonstationary Univariate ARMA unit root Multivariate VARMA cointegration 2. DSGE • simulated GMM • ML • Bayesian Goals Most of you probably interested in empirical research, so we’ll give you the tools needed to do this. However, we’ll also cover theory and highlight open questions. Problem Sets Will have an empirical part – requires programming. Use whatever language you prefer. We recommend Matlab and discourage Stata. You need not write your programs from scratch. You can freely download pro grams from the web, but make sure you use them correctly and cite them. Working in groups is encouraged, but you should write your own solutions....
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This note was uploaded on 03/20/2012 for the course 14 14.02 at MIT.
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