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Posted 03-27-2021 05:00 PM
(849 views)

Hello, I have to perform this following task in a replication step aimed at a research.

"To compute FFC-adjusted returns for day *t*, I first estimate individual stock factor loadings by regressing **delisting-adjusted excess returns** on the FFC four factors (**including an intercept**) on a **120-trading day rolling window** from t –150 to t –31 **trading days** for each stock".

I think I have properly sorted out my dataset so as to have the data in the desired form. Still, I have troubles in performing this time series regression. Moreover, my sample is really big, so I would require something which does not takes ages to run.

Any suggestions on how to carry on?

Thanks,

Riccardo

4 REPLIES 4

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Hello,

If you have your input data set right (I guess you shifted the target column such that the x-vector for day [t-31] is in the same observation as the target y for day [t]?), then use THE BRUTE FORCE APPROACH as explained in this paper:

Rolling Regressions with PROC FCMP and PROC REG

Mark Keintz, Wharton Research Data Services, University of Pennsylvania

https://www.lexjansen.com/nesug/nesug12/fi/fi08.pdf

After creating the data set RWIN (TYPE=VIEW), like done on page 1, call your procedure (for example proc autoreg) with the by-variable "w".

This BRUTE FORCE APPROACH is of course greedy. Much more efficient techniques can be programmed.

Let us know if the real-time (time-to-completion) of your PROC by w takes too long.

By the way, for time series regression it's better to post your question in the SAS Forecasting and Econometrics board.

Good luck,

Koen

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Thank you Koen for the help.

I have tried and it is not actually working properly. Indeed, it takes quite a long time as my dataset is big, not to say enormous.

I will try to look for something a little bit more efficient. Still, thank you for this BRUTE FORCE APPROACH.

I have tried and it is not actually working properly. Indeed, it takes quite a long time as my dataset is big, not to say enormous.

I will try to look for something a little bit more efficient. Still, thank you for this BRUTE FORCE APPROACH.

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To compute FFC-adjusted returns for day *t*, I first estimate individual stock factor loadings by regressing **delisting-adjusted excess returns** on the FFC four factors (**including an intercept**) on a **120-trading day rolling window** from t –150 to t –31 **trading days** for each stock

this is exactly my issue.

I feel I have sorted out data properly, setting the date of interest t, and the estimation window for each date t. I now have predict the value in t with the estimation period data.

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You want to make rolling window datasets (or even rolling window SSCP's - sum-of-squares-and-cross-products) to perform your regressions, correct?

You can take a look at these papers/presentations/comments I made on this subject:

- Philadelphia SAS User's Group (2019) - slide deck.

Rapid Rolling Window Regressions via Home Made Sum of Squares and Cross Products (philasug.org)

- Northeast SAS Users Group (2012) - paper presentation

Rolling Regressions with PROC FCMP and PROC REG (lexjansen.com)

- SAS Communities forum

How to create rolling windows with different numbe... - SAS Support Communities

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