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Re: st: transfer function model and regression: EQUIVALENT???


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: transfer function model and regression: EQUIVALENT???
Date   Thu, 04 May 2006 09:26:25 -0400

Frank,
  The algorithms are different.  The default algorithm for
SAS ARIMA is a conditional least squares with backcasting the
starting values.  Stata employs ML estimation for ARIMA.
  Also, SAS can employ automatic prewhitening whereas Stata 
does not, if it is set up differently.
   Regards,
      Bob Yaffee

  

Robert A. Yaffee, Ph.D.
Research Professor
Shirley M. Ehrenkranz
School of Social Work
New York University

home address:
Apt 19-W
2100 Linwood Ave.
Fort Lee, NJ
07024-3171
Phone: 201-242-3824
Fax: 201-242-3825
yaffee@nyu.edu

----- Original Message -----
From: Frank Zhang <zhangmailer@yahoo.com>
Date: Thursday, April 27, 2006 2:50 pm
Subject: st: transfer function model and regression: EQUIVALENT???

> Dear Statalisters, 
> 
> I have a question about transfer function model with
> ARIMA and REGRESSION precedures. 
> If the model specification is the same, in my view,
> both of the following procedures should have the same
> results.
> 
> proc reg data=one;
>   model y= y1 x ; 
> run;
> proc arima data=one;
>     identify var=y crosscorr=x;
>     estimate p=1 input=(x );
> run;
> 
> But it turned out that the above two procedures give
> quite different results. CONFUSING!
> Can anybody tell me why? THANK YOU!
> 
> The data and code in SAS are as follows: 
> 
> ---------------------------
> data one; 
> input x y;
> y1=lag(y);
> cards;
> 1	-0.109	53.8
> 2	0	53.6
> 3	0.178	53.5
> 4	0.339	53.5
> 5	0.373	53.4
> 6	0.441	53.1
> 7	0.461	52.7
> 8	0.348	52.4
> 9	0.127	52.2
> 10	-0.18	52
> 11	-0.588	52
> 12	-1.055	52.4
> 13	-1.421	53
> 14	-1.52	54
> 15	-1.302	54.9
> 16	-0.814	56
> 17	-0.475	56.8
> 18	-0.193	56.8
> 19	0.088	56.4
> 20	0.435	55.7
> 21	0.771	55
> 22	0.866	54.3
> 23	0.875	53.2
> 24	0.891	52.3
> 25	0.987	51.6
> 26	1.263	51.2
> 27	1.775	50.8
> 28	1.976	50.5
> 29	1.934	50
> 30	1.866	49.2
> 31	1.832	48.4
> 32	1.767	47.9
> 33	1.608	47.6
> 34	1.265	47.5
> 35	0.79	47.5
> 36	0.36	47.6
> 37	0.115	48.1
> 38	0.088	49
> 39	0.331	50
> 40	0.645	51.1
> 41	0.96	51.8
> 42	1.409	51.9
> 43	2.67	51.7
> 44	2.834	51.2
> 45	2.812	50
> 46	2.483	48.3
> 47	1.929	47
> 48	1.485	45.8
> 49	1.214	45.6
> 50	1.239	46
> ;
> run;
> proc arima data=one;
>     identify var=y crosscorr=x;
>     estimate p=1 input=(x );
> run;
> proc reg;
>   model y= y1 x ; 
> run;
> 
> 
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