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Adequate Number of Data in a Window in Rolling Regression
Posted: Fri Nov 10, 2017 3:25 pm
by bok1234
Dear Mr.Doan,
I am trying to test the model as below through rolling regression.
Total number of sample is 44 semi-annual data (22 years).
Yt = b*Xt + a
I want to know the adequate or minimum number of data in a window in this rolling regression.
Even in this case, should I input at least 30 data in unit window?
I guess that adequate number of data in a window in rolling regression should depend on the number of parameters just like general or usual regression test.
Re: Adequate Number of Data in a Window in Rolling Regressio
Posted: Sat Nov 11, 2017 12:22 pm
by TomDoan
What are you "testing"?
Obviously the window size can't be too small relative to the number of regressors. Regardless of the size of the overall sample, the wider is the window, the less the estimates can change from window to window.
Re: Adequate Number of Data in a Window in Rolling Regressio
Posted: Sun Nov 12, 2017 8:43 pm
by bok1234
I am testing the model as below through rolling regression.
Total number of sample is 44 semi-annual data (22 years)
Yt = b*Xt + a
Y : inflation expectations
X : actual inflation
What about 10 data (5 years) in each window?
Re: Adequate Number of Data in a Window in Rolling Regressio
Posted: Sun Nov 12, 2017 8:52 pm
by TomDoan
Sorry. How are you "testing" anything? 10 observation estimates won't tell you much of anything.
Re: Adequate Number of Data in a Window in Rolling Regressio
Posted: Sun Nov 12, 2017 8:56 pm
by bok1234
I know that 10 data-regression is stupid. But I have only 44 data and if I should select 30 in each window, then I will get only 14 windows as a rolling regression result. Is this good?
Re: Adequate Number of Data in a Window in Rolling Regressio
Posted: Mon Nov 13, 2017 11:42 am
by TomDoan
Since I have no idea what you think the rolling regressions are going to show, I really can't answer that. You have a regression with noisy proxies on both the left and the right, and probably highly serially correlated residuals. If you do rolling estimates, you'll probably discover that the sampling error is considerable, but you can probably tell that without running rolling estimates. You might want to read the excerpt on
rolling causality tests.