Transcription of Lecture Handout Autocorrelation
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Lecture Handout Autocorrelation
Lecture 16. Autocorrelation In which you learn to recognise whether the residuals from your model are correlated over time, the consequences of this for OLS estimation, how to test for Autocorrelation and possible solutions to the problem 1. Given the model Yt = b0 + b1Xt + ut Think of Autocorrelation as signifying a systematic relationship between the residuals measured at different points in time This could be caused by inertia in economic variables (multiplier working through), incorrect functional form or data interpolation/revision The effect is that Cov(ut ut-1 ) 0. A simple model of this systematic relationship would be ut = ut-1 + et -1<= <=1 (1).
Lecture 16. Autocorrelation In which you learn to recognise whether the residuals from your model are correlated over time, the consequences of this for OLS estimation, how to test for autocorrelation and possible solutions to the problem
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