On Wed, 16 Feb 2011, stif...@gmx.de wrote:
Hey everyone,
For an investment strategy I built some portfolios of historical stock returns
(every 6 month for 10 years->20observations). To get more observations I´m
using overlapping observations(40obs. which means lag=1).The goal is to test
whether the reruns are positiv or market efficient(=0).
To correct for autocorrelation I would like to use NeweyWest(sandwich)in
R, to get the correct standard deviation for the t-test, but NeweyWest
requires a regression model (lm or glm) which I dont have. Is there a
possibility to do this without a linear model??
Use the trivial linear model lm(y ~ 1) whose only coefficient then
corresponds to the estimated mean of y.
Furthermore, if I understand you correctly, you induce a strong
autocorrelation by using the overlapping observations. So you may be
better off modeling this explicitly in an arima() model, or maybe a gls()
approach.
hth,
Z
Thanks!!! solari
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