# Re: Re: Re: st: fully modified OLS and dynamic OLS

 From "Grant Peter Kabango" To statalist@hsphsun2.harvard.edu Subject Re: Re: Re: st: fully modified OLS and dynamic OLS Date Thu, 19 Mar 2009 13:03:05 +0000

Thank you Robert,

This has helped a lot.

Grant.

-----Original Message-----
From: Robert A Yaffee <bob.yaffee@nyu.edu>
To: statalist@hsphsun2.harvard.edu
Date: Thu, 19 Mar 2009 00:15:52 -0400
Subject: Re: Re: st: fully modified OLS and dynamic OLS

Grant,
PPS:  K1 and K2 could be leads and lags in my last note.
Also, if you had enough strong instruments, you  could try either ivregress or ivreg2.
Cheers,
Bob

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

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2008.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

----- Original Message -----
From: Robert A Yaffee <bob.yaffee@nyu.edu>
Date: Wednesday, March 18, 2009 11:37 pm
Subject: Re: Re: st: fully modified OLS and dynamic OLS
To: statalist@hsphsun2.harvard.edu

> Grant,
>     As for the Dynamic OLS, if this is the method put forth by Saikkonen
> in 1991,  this could be done with Stata easily.
>     As mentioned in  GS Maddala and I.M. Kim's  Unit Roots, Cointegration,
> and Structural Change 2003,  Cambridge University Press, p. 163:
>     the model regresses a y(1,t) on a y(2,t) and a d.y(2,t-j)
>     would be formulated as a regression
>     of y(1,t) = B'y(2,t) + Sum (from j=-k1 to K2) b(j)d.y(2,t-j) +
> v(t) ,
> where k1 and k2 are selected to increase at an appropriate rate up to
> T.
> The method adds leads and lags to y(2,t) but not to y(1,t).
>     You could estimate this with 3sls.
>            Regards,
>                  Bob Yaffee
>
>
> Robert A. Yaffee, Ph.D.
> Research Professor
> Silver School of Social Work
> New York University
>
>
> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2008.pdf
>
> CV:  http://homepages.nyu.edu/~ray1/vita.pdf
>
> ----- Original Message -----
> From: Grant Peter Kabango <g.kabango.1@research.gla.ac.uk>
> Date: Wednesday, March 18, 2009 10:09 pm
> Subject: Re: Re: st: fully modified OLS and dynamic OLS
> To: statalist@hsphsun2.harvard.edu
>
>
> > Thank you Michael.
> >
> > Grant
> >
> > -----Original Message-----
> > From: Michael Hanson <mshanson@mac.com>
> > To: statalist@hsphsun2.harvard.edu
> > Date: Wed, 18 Mar 2009 21:57:12 -0400
> > Subject: Re: st: fully modified OLS and dynamic OLS
> >
> > On Mar 18, 2009, at 6:37 PM, Grant Peter Kabango wrote:
> >
> > > I would like to estimate using the fully modified OLS (FMOLS) and
>
> > > dynamic OLS (DOLS), and I wish to find out the Stata command for
>
> > > these processes.
> >
> >
> > On Mar 18, 2009, at 7:01 PM, Martin Weiss wrote:
> >
> > > -findit- does not return any results. Are these procedures known
>
> > > under any other name?
> >
> >
> > The short answers are no (to Grant: "Are FM-OLS or DOLS currently
> > implemented in Stata?") and no (to Martin: "Are they known by other
>
> > names?").  Note that this request and the corresponding response
> > about alternative names reappear on Statalist roughly every year or
>
> > two (and apparently has been more popular recently):
> >
> > <
> > <
> > <
> > <
> > <
> >
> >
> > FM-OLS and DOLS are single equation estimators for cointegrated
> > relationships.  Given that this is a fairly specialized topic in
> time
> >
> > series analysis, I don't believe they can be found in other
> > Stata .ado files under different names.  (Although I would be happy
>
> > to be proven wrong on that point.)
> >
> > Dynamic OLS is quite easy to implement in Stata, since it just
> > involves augmenting a (super-consistent) OLS estimate of the
> > cointegrating relationship with leads and lags of the RHS variable.
>
> >
> > For appropriate inference, HAC standard errors must be used.  Below
>
> > is a simple (and very stylized) example.  Note that this example
> uses
> >
> > both -ivreg2- and -freduse-, which are available from SSC.
> >
> >
> > // Begin example
> >
> > clear
> > freduse GDPC96 PCECC96
> > gen t = qofd(daten)
> > format t %tq
> > drop date*
> > tsset t
> >
> > gen y = ln(GDPC96)
> > gen c = ln(PCECC96)
> >
> > ivreg2 c y L(-4/4)D.y, bw(auto) robust
> > ivregress 2sls c y L(-4/4)D.y, vce(hac nw opt)
> >
> > // End example
> >
> > The coefficient on y in each regression will be the estimate of the
>
> > parameter B in the cointegrating vector [1 -B], such that [c y] [1 -
>
> > B]' ~ I(0).  Note that -ivreg2- will work with Stata 9 or better
> > (once installed); -ivregress- was introduced in Stata 10.  They
> > should return identical results in the above example.
> >
> >
> > The FM-OLS estimator is a little more sophisticated, and -- as far
> as
> >
> > I can tell -- no one has contributed a user-written version.  Thus,
>
> > your options are (1) write it yourself (see the references below --
>
> > would be a good exercise to implement in Mata); (2) use a system
> > estimator (such as -vec-); or (3) use a different statistical
> > package.  (For example, FM-OLS has been implemented in RATS: see
> > <http://www.estima.com/
> > procs_perl/panelfm.src>.  Procedures may also have been written for
>
> > R, but a quick search did not turn up anything.)
> >
> > Hope this helps.  Perhaps the next time someone looks for the DOLS
> or
> >
> > FM-OLS estimators, they will search the mailing list archive first.
> >
> > -- Mike
> >
> >
> > References:
> >
> > Banerjee, Anindya, et al, "Co-integration, Error-Correction, and the
>
> >
> > Econometric Analysis of Non-Stationary Data," Oxford University
> > Press, 1993.
> >
> > Maddala, G.S. and In-Moo Kim, "Unit Roots, Cointegration, and
> > Structural Change," Cambridge University Press, 1998.
> >
> > Phillips, Peter and Bruce Hansen, "Statistical Inference in
> > Instrumental Variables Regression with I(1) Processes," Review of
> > Economic Studies, v.57, 1990, pp. 99-125.
> >
> > Phillips, Peter, "Fully Modified Least Squares and Vector
> > Autoregression," Econometrica, v.63 n.5, 1995, pp. 1023-1078.
> >
> > Stock, James and Mark Watson, "A Simple Estimator of Cointegrating
>
> > Vectors in Higher Order Integrated Systems," Econometrica, v.61 n.4,
>
> >
> > 1993, pp. 783-820.
> >
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