09 Lecture 16 Std

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

  Lecture, Lecture 16

Differences in Differences (using Stata)

1 1 -2.52e+09 1.45e+09 -1.73 0.088 -5.42e+09 3.81e+08 time#treated 1.treated 1.78e+09 1.05e+09 1.70 0.094 -3.11e+08 3.86e+09 1.time 2.29e+09 9.00e+08 2.54 0.013 4.92e+08 4.09e+09 y Coef. Std. Err. t P>|t| [95% Conf. Interval] Robust Root MSE = 3.0e+09

1 Omitted Variable Bias: Part I - University of California ...

Now, remember that ^ 1 is a random variable, so that it has an expected value: E h P^ 1 i = E 1 + P i (x i x)u i i (x i x)x i = 1 + E P i (x i x )u i P i (x i x )x i = 1 Aha! So under assumptions SLR.1-4, on average our estimates of ^ 1 will be equal to the true population parameter 1 that we were after the whole time. 2

  Variable, Bias, Omitted, Omitted variable bias