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32_TimeSeriesA_handout

# 32_TimeSeriesA_handout - Time Series I W-261 Y Kryukov CMU...

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Unformatted text preview: Time Series - I Wooldridge 10 73-261 Y. Kryukov CMU, Tepper School of Business November 15, 2010 Y. Kryukov (CMU) 73-261: Time Series Nov-15 1 / 13 Overview Topic 3.2 Time series TS data as explanatory variables: Distributed lags Time Trend Seasonality Residuals with TS data: underlying theory Stationarity AR and MA One simple &x: di/erencing Y. Kryukov (CMU) 73-261: Time Series Nov-15 2 ¡ 13 Time series Cross section : observation over di/erent subjects: people, markets, countries. Time series : Observations come from same object, observed at di/erent points in time ( t = 1 , ..., T ) t can be year, quarter, month, day, minute, etc. Changes in x do not a/ect y until next year = ) y t is a/ected by lagged x : x t & 1 Past a/ects future = autocorrelation: cov ( y t , y t & 1 ) 6 = Need special techniques to deal with it. Y. Kryukov (CMU) 73-261: Time Series Nov-15 3 ¡ 13 Distributed lag model Static model &ignores timing of e/ects. E.g. Phillips curve: Inf t = β + β 1 Unempl t + u t Distributed lag model &delayed e/ects E.g. child tax exemption ( e ) vs. birth rate ( br ): br t = α + δ e t + δ 1 e t & 1 + δ 2 e t & 2 + u t e t = exemption during current year . . ....
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32_TimeSeriesA_handout - Time Series I W-261 Y Kryukov CMU...

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