From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: RECT[SERIES] difficulties
Date: Fri, 30 Apr 1999 09:44:22 -0600
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Michael:

The problem is with your CARDS statement:

> 	DO J = 1,NVARS
> 	    IMPULSE(NOPRINT) NVARS MLAGS J SIGMA
> 	    CARDS EQNS IRF(EQNS,J) J EQNS
> 	END DO J

Note that you are using "J" as the newstart parameter. Thus when J 
equals 1, the responses start in entry 1, but for J==2, they start in 
entry 2, and so on. Change this to:

       CARDS EQNS IRF(EQNS,J) 1 EQNS
                             
And you should get the desired result.

> element, and so forth.  The series is only MLAGS elements long, so the
> final two elements are simply lost.

By the way, they aren't actually lost--you just weren't printing them 
out. For example, suppose MLAGS==12. For J=1, the responses were 
being stored in entries 1 through 12. But for J=2, they were being 
stored in entires 2 through 13. 

Because you were limiting the PRINT statement to the range 1 through
MLAGS, you just weren't seeing any of the entries past MLAGS.

Sincerely,
Tom Maycock
Estima
 

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From: Klaus Fischer <Klaus.Fischer@fas.ulaval.ca>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Data envelopment analysis (DEA)
Date: Fri, 30 Apr 1999 11:41:16 -0300
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Hello fellow Rats enthusiasts. Has anyone among you used the programming
procedure LQPROGR to do a Data Envelopment Analysis (DEA) implementation
and would be willing to share it? I know it is not too hard but it would
certainly allow us to save time and errors, reinventing wheels is always
a little bit dumb. Besides, I need the results fast.

Thanks,

Klaus Fischer
CREFA, Laval University

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From: glossers@uwwvax.uww.edu
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Data envelopment analysis (DEA)
Date: Fri, 30 Apr 1999 17:58:16 -0500
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>Hello fellow Rats enthusiasts. Has anyone among you used the programming
>procedure LQPROGR to do a Data Envelopment Analysis (DEA) implementation
>and would be willing to share it? I know it is not too hard but it would
>certainly allow us to save time and errors, reinventing wheels is always
>a little bit dumb. Besides, I need the results fast.
>

Klaus,

Just out of curiousity, why would you want to use LQPROGR? LQPROGR does not
provide information about shadow prices. If you have less than 200
variables to solve for, you would probably be better off using Excel's
Solver tool. Sensitivity analysis aside, it's also easier to set up the
problem using Excel (similar capability also exists for Lotus and Quattro
Pro). To see how to do DEA analysis with Excel, check out Contemporary
Management Science with Spreadsheets by Anderson, Sweeney and Williams
(South Western 1999).

Stuart Glosser
Dept. of Economics
University of Wisconsin at Whitewater




---------- End of message ----------

From: Tommy Sveen <tommy.sveen@nhh.no>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Structural VAR
Date: Fri, 07 May 1999 16:24:44 +0200
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Hi RATS users!

I want to estimate a three-variable VAR (gdp, prices and nominal money
in log differences) using a structural VAR with long-run restrictions on
the C-matrix (using the notation in  "Topics in Structural VAR
Econometrics" by Amisano & Giannini 1997, where thus dx = C(L)u=C(L)Ce).
The idea is that gdp is driven by one shock only, and real money by two
shocks. Now, cointegration tests indicate one cointegration relationship
among these variables as well: the quantity theory of money holds in the
long run, say. How should I proceed to estimate this? The problem is not
covered in Amisano & Giannini.
In the description of Malcom it says that the procedure integrates
cointegration and structural var; but I don't know if this includes
cointegration AND long-run restrictions as well.

Thanks in advance
Tommy


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From: "Iacoviello,M  (pg)" <M.Iacoviello@lse.ac.uk>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: re: structural VAR
Date: Fri, 7 May 1999 16:41:10 +0100 
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To the best of my knowledge, the best way to analyze structural VARs when
there are cointegrating vectors between the variables is use common trends
analysis.
The main references are:
	
1) Fischer, L., P.Fackler and D.Orden (1995), ``Long-Run identifying
restrictions for an error-correction model of New Zeland money, prices and
output'', Journal of International Money and Finance, 14, 127-47.
	
2) Jacobson, T., P.Jansson, A.Vredin, A.Warne (1998), ``A VAR\ Model for
Monetary Policy Analysis in a Small Open Economy'', mimeo IIES, Stockholm.
	
3) Jacobson, T., A.Vredin, A.Warne (1998), ``Are Real Wages and Unemployment
Related'', Economica}, 65, 69-96.
	
4) King, R., C.Plosser, J.Stock and M.Watson (1991), ``Stochastic Trends and
Economic Fluctuations'',  American Economic Review}, 81, 4, 819-40.
	
5) Quah, D., (1994), ``Comment'', in Measuring and Interpreting Business
Cycles}, FIEF\ Studies in Labor Markets and Economic Policy, Oxford
University Press, Oxford.
	
6) Warne, A. (1993), ``A Common Trends Model: Identification, Estimation and
Inference'', seminar paper No.555, IIES, Stockholm.

Probably the best starting points are the papers 6, then 2 and 3. The paper
by Danny Quah is a clear and concise summary.

Finally (and most importantly) you can find a way around thanks to Henrik
Hansen and Anders Warne ( they kindly provided me with the RATS routine,
which is publicly available at Anders Warne's webpage). Since I have
encountered the same problems that Tommy has, my educated guess is that you
can find their src files quite helpful.
 http://www.iies.su.se/data/home/warnea/NoFrames/genreint.htm

Matteo Iacoviello
PhD students
LSE

---------- End of message ----------

From: Rocco Mosconi <mosconi@cdc8g5.cdc.polimi.it>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Structural VAR
Date: Fri, 07 May 1999 18:59:53 +0200
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Tommy,
yes, MALCOLM allows you to make SVAR analysis in the presence of
cointegration. As in the general approach discussed in Amisano&Giannini
1997, this is done in two steps:
1) the reduced for VAR-ECM is estimated, under reduced rank restrictions,
possibly including (over-)identifying restrictions on the betas.
2) the structural VAR parameters, i.e. the parameters relating the reduced
form errors with the structural form innovations, are estimated based on the
variance covariance matrix obtained in step 1.
The restrictions you mention, i.e. the C-model if I understand correctly, is
supported in the second step, as well as more complicated restrictions (the
AB-model, Blanchard-Quah restrictions on the long run structural impulse
responses, restrictions on structural loadings, and mixing of these
restrictions). You may also produce impulse response and FEVD graphs with
asymptotic bounds. Gianni Amisano has actually programmed that section of
MALCOLM, and written the corresponding sections in MALCOLM manual, so the
notation and programming is consistent with his book with Carlo Giannini.
More information may be found at the URL http://www.greta.it/malcolm

Yours

Rocco Mosconi
--
__________________________________________________
Dipartimento di Economia e Produzione
Politecnico di Milano
Piazza L. da Vinci 32, 20133 Milano
Phone: (39)-02-23992747; Fax: (39)-02-23992710
rocco.mosconi@polimi.it or rocco@mosconi.it
"The difference between theory and practice is
greater in practice than in theory." (John Wilkes)
__________________________________________________



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From: ehrmann@datacomm.iue.it
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Andrews test for instability
Date: Mon, 10 May 1999 15:00:42 +0000
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 Hello Everybody,

I am looking for code to perform Andrews' test of parameter 
instability (Andrews, D. (1993), Tests for Parameter Instability and 
Structural Change with Unknown Change Point, Econometrica 61(4), 
821-56). Any information would be very helpful.

Thanks a lot, Michael

______________________________
Michael Ehrmann
Department of Economics
Istituto Universitario Europeo
Via dei Roccettini 9
I-50016 San Domenico di Fiesole (FI)
ITALY
Fax:  +39/055/599887

---------- End of message ----------

From: Enrico De Giorgi <edegiorgi@ticino.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Rolling Linear Regression
Date: Mon, 10 May 1999 23:45:32 +0200
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Dear RATS Users:

* ROLLREG.SRC            by Simon van Norden           10-11-1992
* Copyright 1993, 1992, 1990 by Bank of Canada
* (modified for RATS v4 August 1993 by Jeff Gable)
* (Robust Error Bug Fix  June 1995 by Rob Vigfusson)

I am working with the procedure witten by Simon van Norden for computing
a rolling linear regression on a time series. The general sintax given
by the author is the following

 @ROLLREG(OPTION)  depvar  first last outseries
 # list of series

In my case, the number of time series in the independent list  is
varying and therefore I would like to write the command in the following
manner (as usual by RATS)

@ROLLREG(OPTION)  depvar  first last outseries
 # series(1) TO series(%ROWS(series))

where series denotes a VECT[SERIES], but this last alternative gives an
errors by the procedure. Can someone help me?

Thanks in advance,
Enrico Degiorgi



---------- End of message ----------

From: Josef Baumgartner <Josef.Baumgartner@wifo.ac.at>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Bry and Boschan (1971) Porcedures in RATS
Date: Tue, 11 May 1999 10:04:33 +0200
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Dear RATS users!

I want to do determine business cycle turning points for the Austrian
economy using different methods. Therfore I am looking for Bry and
Boschan's (1971) procedure for programmed determination of turning
points im monthly and quaterly series. If anybody of you has a RATS
program of Bry and Boschan's(1971) procedure or anyother procedure
dating peaks and troughs of cycles I would be grateful if s/he could
send me a copy of the program.

Thanks in advance.

Josef 

Lit.:
Bry G., Boschan C., (1971), Cyclical analysis of time series: Selected
procedures and computer programs,  NBER Technical Paper No. 20.
-- 
___________________________________________________________________
Josef Baumgartner                           Austrian Institute for
Tel.:   +43-1-7982601-230                   Economic Research
Fax:    +43-1-7989386                       P.O. Box 91
Mail: Josef.Baumgartner@wifo.ac.at          A-1103 Vienna, Austria     
http://www.wifo.ac.at/

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From: Irida Achthoven <achthovi@qed.econ.queensu.ca>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Wilcoxon test
Date: Tue, 11 May 1999 04:35:29 -0400 (EDT)
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Dear rats users

I need to perform a wilcoxon test on the differences of 2 vectors of
squared  forecast errors.

Has any of you , written an procedure or a prg to perform this test.

I would really appreciate it if you could share the procedure or prg with
me.

Any suggestions are also very welcome.
best regards,
Irida


---------- End of message ----------

From: Michael Hanson <mhanson@econ.lsa.umich.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Andrews test for instability
Date: Tue, 11 May 1999 10:50:27 -0400 (EDT)
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	I, too, would be interested in any information regarding coding 
Andrews (1993) in RATS.  Thanks.

					-- Mike

======================================================================
Michael S. Hanson                                    mhanson@umich.edu
Department of Economics         http://www.econ.lsa.umich.edu/~mhanson
University of Michigan


On Mon, 10 May 1999 ehrmann@datacomm.iue.it wrote:

>  Hello Everybody,
> 
> I am looking for code to perform Andrews' test of parameter 
> instability (Andrews, D. (1993), Tests for Parameter Instability and 
> Structural Change with Unknown Change Point, Econometrica 61(4), 
> 821-56). Any information would be very helpful.
> 
> Thanks a lot, Michael
> 
> ______________________________
> Michael Ehrmann
> Department of Economics
> Istituto Universitario Europeo
> Via dei Roccettini 9
> I-50016 San Domenico di Fiesole (FI)
> ITALY
> Fax:  +39/055/599887
> 


---------- End of message ----------

From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Rolling Linear Regression
Date: Tue, 11 May 1999 11:34:45 -0600
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> I am working with the procedure witten by Simon van Norden for computing
> a rolling linear regression on a time series. The general sintax given
> by the author is the following
> 
>  @ROLLREG(OPTION)  depvar  first last outseries
>  # list of series
> 
> In my case, the number of time series in the independent list  is
> varying and therefore I would like to write the command in the following
> manner (as usual by RATS)
> 
> @ROLLREG(OPTION)  depvar  first last outseries
>  # series(1) TO series(%ROWS(series))
> 
> where series denotes a VECT[SERIES], but this last alternative gives an
> errors by the procedure. Can someone help me?
> 

It would be more than a little helpful to know exactly what error 
message was produced. A full answer to your question would probably 
also require being able to see the procedure code itself.

In general, however, there is an easier way to do what you are trying 
to do here. Basically anywhere RATS expects a list of series, you can 
simply supply the name of an array of series. For example, suppose 
you have the following:

declare vector[series] vs(5)
*
* (instructions to load data into the vector VS)
*

The following would all be equivalent ways of telling RATS to use all 
5 series as regressors:

linreg y
# vs(1) vs(2) vs(3) vs(4) vs(5)

linreg y
# vs(1) to vs(5)

linreg
# vs

The advantage of the latter is, of course, that it doesn't require 
any advance knowledge of the dimensions of VS.

This also works in situations like:

print / vs(1) to vs(5)

print / vs

which are both equivalent statements (assuming VS has 5 elements).

Sincerely,
Tom Maycock
Estima



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From: enrico.degiorgi@vontobel.ch
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE: Rolling Linear Regression
Date: Wed, 12 May 1999 09:11:59 +0100
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> -----Original Message-----
> From:	Estima [SMTP:estima@estima.com]
> Sent:	Dienstag, 11. Mai 1999 18:35
> To:	RATS Discussion List
> Subject:	Re: Rolling Linear Regression
> 
> > I am working with the procedure witten by Simon van Norden for computing
> > a rolling linear regression on a time series. The general sintax given
> > by the author is the following
> > 
> >  @ROLLREG(OPTION)  depvar  first last outseries
> >  # list of series
> > 
> > In my case, the number of time series in the independent list  is
> > varying and therefore I would like to write the command in the following
> > manner (as usual by RATS)
> > 
> > @ROLLREG(OPTION)  depvar  first last outseries
> >  # series(1) TO series(%ROWS(series))
> > 
> > where series denotes a VECT[SERIES], but this last alternative gives an
> > errors by the procedure. Can someone help me?
> > 
	[Degiorgi Enrico]  
	The error involves matrix-dimension at line 4265 of the procedure
(i'm working with the MOVE option): a sum of two matrices with different
dimensions is computed. Next follows the procedure
>  <<Rollregnew.src>> 

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	OLLRE%7E1.SRC

*ENV NOECHO
*
* ROLLREG.SRC            by Simon van Norden           10-11-1992
* Copyright 1993, 1992, 1990 by Bank of Canada
* (modified for RATS v4 August 1993 by Jeff Gable)
* (Robust Error Bug Fix  June 1995 by Rob Vigfusson)
*
* This procedure does rolling OLS regressions in one of three modes.
* Given an initial range of observations, ADD adds observations
* (one at a time) to the end of the sample, DROP drops them from the
* beginning of the sample, and MOVE simultaneously adds and drops
* so that the number of observations in each regression is constant.
*
* The syntax is
* @ROLLREG  depvar  first last outseries
* # list of series
* where
* depvar            is the name of the LHS variable
* list of series    lists the RHS variables for the regression
* outseries	    series with stored coefficients (Beta)
* first, last       (optional) maximum range for the regression
*                   defaults to maximum range for which all variables
*                   are defined
* Options:
* ADD=3D              starting with a regression from  first  to  ADD,
*                   adds one observations at a time until it does a
*                   regression from  first  to  last
* DROP=3D             starting with a regression from  first  to  last,
*                   drops one observations at a time until it does a
*                   regression from  DROP  to  last
* MOVE=3D             starting with a regression from first to =
first+MOVE-1
*                   adds and drops one observations at a time until it
*                   does a regression from  last-move+1  to  last
*                   (i.e. there are MOVE observations in each =
regression)
*
* Note that exactly one of these three options must be used.
* Also note that if you use dates with any of the above options, you =
must
* enclose the dates in parentheses.
* e.g. @rollreg(drop=3D(89,4)) gnp
*      @rollreg(add=3D(71,4)) gnp
*      @rollreg(move=3D20) gnp
*
* ROBUST/[NOROBUST] uses the ROBUSTERRORS option to calculate the
*                   standard errors.
* LAGS =3D [0]        number of lags to use with the ROBUST option.
*                   This does **not** vary with the degrees of freedom.
* DAMP =3D [1.0]      shape of the spectral window to use with the
*                   ROBUST option.  Note that the default here is a
*                   Newey-West or Bartlett window while in RATS the
*                   default is a flat window (DAMP=3D0.0).
*
* [PRINT]/NOPRINT   prints the coefficient estimates
* GRAPH/[NOGRAPH]   graphs the test statistics.  At present, the graphs =
can be
*                   converted to postscript format using the command:
*                   >>gsp2pst chow.gsp post-name<<
*      They can then be printed using the lpr command or viewed
*                   using the postscript viewer.    You must issue the =
"open
*                   plot" and "close plot" commands from the calling =
program.
* COPY/[NOCOPY]     writes the estimated coefficients to a file.
*                   You must have already opened the copy unit to this
*                   file
* FORMAT =3D [FREE]/BINARY/WKS/DIF/PRN/EXCEL/TROLL
*                   for use only with COPY; writes the data in that
*                   format.  Note that ORG=3DVAR.
*
procedure rollreg  y  first last outseries
type series[real] y outseries
type integer  first last
option integer ADD     0
option integer DROP    0
option integer MOVE    0
option switch robust   0
option real damp      1.0
option integer lags    0
option switch print    1
option switch graph    0
option switch copy     0
option choice format   1 free binary wks dif prn excel troll
local vector[integer] x
local integer fobs lobs numreg base cut k   fbeta lbeta stdbase
local vector[real] xtyt
local rectangular[real] xy
local series[real] m
*DISPLAY
*
* Define the system to be estimated and initialize
*
enter(varying,entries=3Dnumreg) x
inquire(regressorlist) fobs lobs
# y x
if (first .and. last)
    {;  compute fobs=3Dfirst;   compute lobs=3Dlast;    }
*DISPLAY 'Rolling Regression from' %datelabel(fobs) 'to' =
%datelabel(lobs) $
*    'of ' %label(y) ' on '
*DISPLAY(hold) '    '
do k=3D1,numreg
   *DSIPLAY(hold)  %label([series]x(k))  '  '
end do k
*DISPLAY
make(transpose) xy fobs lobs
# x y
if (add=3D=3D0)+(drop=3D=3D0)+(move=3D=3D0) < 2
    DISPLAY 'ROLLREG ERROR: Use only one of ADD, DROP and MOVE =
options.'
else if .not. (add .or. drop .or. move)
    DISPLAY 'ROLLREG ERROR: Must specify one of ADD, DROP and MOVE =
options.'

else if add
    {
*
* Adding observations to end
*
    *DISPLAY 'using ADD=3D' %datelabel(add)
    scratch 2*numreg add lobs base
    cmoment fobs add
    # x y
    linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) =
y
    # x
    dofor m =3D base+1 to base+numreg
        compute m(add) =3D %beta(m-base)
    end dofor
 if robust =3D=3D 0
     dofor m=3Dbase+1+numreg to base+2*numreg
    compute m(add) =3D sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)
     end dofor
 else if robust =3D=3D 1
    dofor m=3Dbase+1+numreg to base+2*numreg
      compute m(add) =3D sqrt(%xx(m-base-numreg,m-base-numreg))
      end dofor


    do cut=3Dadd+1,lobs
 cmoment(setup) fobs cut
 # x y
        overlay xy(1,cut-fobs+1) with xtyt(numreg+1)
        compute %cmom =3D %cmom + xtyt*tr(xtyt)
        =
linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) y
        # x
        dofor m=3Dbase+1 to base+numreg
            compute m(cut) =3D %beta(m-base)
        end dofor
 if robust =3D=3D 0
         dofor m=3Dbase+1+numreg to base+2*numreg
             compute m(cut) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)
         end dofor
 else if robust =3D=3D 1
  dofor m=3Dbase+1+numreg to base+2*numreg
              compute m(cut) =3D sqrt(%xx(m-base-numreg,m-base-numreg))
         end dofor

    end do cut
    compute fbeta =3D add; compute lbeta =3D lobs
    }
else if drop
    {
*
* Dropping observations from start
*
    *DISPLAY 'using DROP=3D' %datelabel(drop)
    scratch 2*numreg fobs drop base
    cmoment fobs lobs
    # x y
    linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) =
y
    # x
    dofor m=3Dbase+1 to base+numreg
        compute m(fobs) =3D %beta(m-base)
    end dofor
    if robust =3D=3D 0
     dofor m=3Dbase+1+numreg to base+2*numreg
         compute m(fobs) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)

     end dofor
    else if robust =3D=3D 1
 dofor m=3Dbase+1+numreg to base+2*numreg
         compute m(fobs) =3D sqrt(%xx(m-base-numreg,m-base-numreg))
     end dofor


    do cut=3Dfobs,drop-1
 cmoment(setup) cut+1 lobs
 # x y
        overlay xy(1,cut-fobs+1) with xtyt(numreg+1)
        compute %cmom =3D %cmom - xtyt*tr(xtyt)
        =
linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) y
        # x
        dofor m=3Dbase+1 to base+numreg
            compute m(cut+1) =3D %beta(m-base)
        end dofor
 if robust =3D=3D 0
        dofor m=3Dbase+1+numreg to base+2*numreg
            comptue m(cut+1) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)
        end dofor
 else if robust =3D=3D 1
 dofor m=3Dbase+1+numreg to base+2*numreg
            compute m(cut+1) =3D sqrt(%xx(m-base-numreg,m-base-numreg))
        end dofor



    end do cut
    compute fbeta =3D fobs; compute lbeta =3D drop
    }
else if MOVE
    {
*
* Moving sample regression
*
    *DISPLAY 'using MOVE=3D' ### move
    scratch 2*numreg fobs+move-1 lobs base
    cmoment fobs fobs+move-1
    # x y
    linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) =
y
    # x
    dofor m=3Dbase+1 to base+numreg
        compute m(fobs+move-1) =3D %beta(m-base)
    end dofor
    if robust =3D=3D 0
     dofor m=3Dbase+1+numreg to base+2*numreg
        compute m(fobs+move-1) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)
     end dofor
    else if robust =3D=3D 1
  dofor m=3Dbase+1+numreg to base+2*numreg
        compute m(fobs+move-1) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg))
 end dofor

    do cut=3Dfobs+move,lobs
 cmoment(setup) cut-move+1 cut
 # x y
        overlay xy(1,cut-fobs+1) with xtyt(numreg+1)
        compute %cmom =3D %cmom + xtyt*tr(xtyt)
        overlay xy(1,cut-move-fobs+1) with xtyt(numreg+1)
        compute %cmom =3D %cmom - xtyt*tr(xtyt)
        =
linreg(cmom,noprint,robusterrors=3Drobust,damp=3Ddamp,lags=3Dlags) y
        # x
        dofor m=3Dbase+1 to base+numreg
            compute m(cut) =3D %beta(m-base)
        end dofor
 if robust =3D=3D 0
         dofor m=3Dbase+1+numreg to base+2*numreg
            compute m(cut) =3D =
sqrt(%xx(m-base-numreg,m-base-numreg)*%seesq)
         end dofor
 else if robust =3D=3D 1
   dofor m=3Dbase+1+numreg to base+2*numreg
            compute m(cut) =3D sqrt(%xx(m-base-numreg,m-base-numreg))
          end dofor
    end do cut
    compute fbeta =3D fobs+move-1; compute lbeta =3D lobs
    }
*
* Now output the results
*
if (add=3D=3D0)+(drop=3D=3D0)+(move=3D=3D0) =3D=3D 2
{
do k =3D 1,numreg
    	labels base+k base+k+numreg
	# %label([series]x(k)) %concat('se',%label([series]x(k)))
end do k
*Outseries
SET outseries =3D [series](base+k)

if print
    print(dates) fbeta lbeta base+1 to base+2*numreg
if copy
    copy(format=3Dformat) fbeta lbeta  base+1 to base+2*numreg
if graph
    {
    scratch 2 fbeta lbeta stdbase
    labels stdbase+1 stdbase+2
    # '95%upper' '95%lower'
    do k =3D base+1, base+numreg
        set stdbase+1 fbeta lbeta =3D k + (k+numreg)*1.96
        set stdbase+2 fbeta lbeta =3D k - (k+numreg)*1.96
        if graph
            {
            graph(dates,header=3D'rollreg estimated betas') 3
            # k fbeta lbeta
            # stdbase+1 fbeta lbeta
            # stdbase+2 fbeta lbeta
            }
    end do k
    }
*DISPLAY 'Coefficients stored in series' ### base+1 'to' ### =
base+numreg 'and'
*DISPLAY 'standard errors in series' ### base+numreg+1 'to' ### =
base+2*numreg

}
*
end proc rollreg
*ENV ECHO

 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * =
*
*
 *  ROLLREG.SRC
 *  Copyright 1993 by the Bank of Canada
*
 *  Programmed for RATS by Robert Amano, Jeff Gable and Simon van =
Norden
*
 *  In exchange for access to these procedures, users are requested to
*
 *  acknowledge the use of the Bank of Canada RATS procedures in
*
 *  all published work.
*
 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * =
*
*










------_=_NextPart_000_01BE9C4F.1BD94AC0--

---------- End of message ----------

From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE: Rolling Linear Regression
Date: Wed, 12 May 1999 12:40:25 -0600
Errors-to: <rats-l-owner@efs.mq.edu.au>
Reply-to: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
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> > > @ROLLREG(OPTION)  depvar  first last outseries
> > >  # series(1) TO series(%ROWS(series))
> > > 
> > > where series denotes a VECT[SERIES], but this last alternative gives an
> > > errors by the procedure. Can someone help me?
> > > 
> 	[Degiorgi Enrico]  
> 	The error involves matrix-dimension at line 4265 of the procedure
> (i'm working with the MOVE option): a sum of two matrices with different
> dimensions is computed. Next follows the procedure


As I suspected, the problem is with the way the procedure implements 
the use of ENTER and the regressor lists it creates. For example, 
the procedure takes the number of entries on the supplementary 
card as the number of regressors:

(01.0232) enter(varying,entries=numreg) x
(01.0258) inquire(regressorlist) fobs lobs
(01.0283) # y x


The ENTRIES option records the number of "elements" supplied on the
regressor list, which will only be the same as the number of
regressors if you just list a bunch of series names or numbers, 
and don't use lag notation or the "TO" keyword.  


Please see the description of this in the RATS Manual (p. 5-36) and
on the RATS FAQ page on our web site
(www.estima.com/faqs/ratsfaq.htm) for full details.

Note that the same basic problem affects the section that assigns 
labels to the output series:

display 'skipping labelling'
   labels base+k base+k+numreg
   # %label([series]x(k)) %concat('se',%label([series]x(k))) end
do k

This assumes that entry K of X is a series number. It might be, but 
it might also be a lag number of the numeric code used for the {, }, 
or TO symbols. As noted on the FAQ, the best way to handle this is to 
use the %EQN.... functions.

So, the procedure should either be modified to handle the 
regressor list properly, or you'll just need to stick to listing each 
series explicitly (and avoid using lag notation):

@rollreg ...
# series(1) series(2) series(3) etc.


Sincerely,
Tom Maycock
Estima


------------------------------------------------------------
|   Estima                    |  Sales:   (800) 822-8038   |
|   P.O. Box 1818             |  Support: (847) 864-1910   |
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|   USA                       |  estima@estima.com         |
|                             |  http://www.estima.com     |
------------------------------------------------------------

---------- End of message ----------

From: Imam.Alam@uni.edu
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Away from my mailbox
Date: Wed, 12 May 1999 12:35:47 -0500 (CDT)
Errors-to: <rats-l-owner@efs.mq.edu.au>
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I am currently away from my mailbox and unavailable. I expect to 
return on July 6, 1999. If you would like to contact me while I am 
away, please send e-mails to zsi97@yahoo.com.


You should receive this notice only once even if you send me multiple
messages during my time away from the office.  ALL messages will be
delivered, and I will read them when I get back.

Thanks,


-Imam (Babul)

_____________________________________________________________________________

M. Imam Alam
Department of Economics
University of Northern Iowa
Cedar Falls, IA 50614-0129


---------- End of message ----------

From: Ying-Man Chung <y_mchung@yahoo.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE:standard deviation
Date: Wed, 12 May 1999 18:44:50 -0700 (PDT)
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Dear Rats users,

Might I request for commands to simulate random variables which
standard deviation is 0.4 or others (except unity), please?


Thank you for your help!


With best wishes,

Sarah

_________________________________________________________
Do You Yahoo!?
Free instant messaging and more at http://messenger.yahoo.com

---------- End of message ----------

From: mark.astley@bankofengland.co.uk (Mark Astley)
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Random Variables
Date: Thu, 13 May 1999 16:39:58 +0100
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Dear fellow RATs users.

I'm having difficulty figuring out how to do something that I thought
should be pretty easy.  In particular, I'm trying to generate a couple
of series of random numbers (no problem!) - but I want each of the
series to be orthogonal to each other (problem!).

Any ideas/hints gratefully received.

Many thanks

Mark Astley




---------- End of message ----------

From: Christopher F Baum <baum@bc.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Random Variables
Date: Thu, 13 May 1999 11:55:37 -0400
Errors-to: <rats-l-owner@efs.mq.edu.au>
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How about

UNIX RATS 4.31. Run on May 13 1999
(c) 1992-7 Thomas A. Doan. All rights reserved
all 2000
set x1 = %ran(1.0)
set z = %ran(1.0)
linreg(noprint) z / x2
# constant x1
cmom(corr,print)
# x1 x2

Correlation Matrix
          X1             X2
X1       1.000000  -4.839699e-18
X2  -4.839699e-18       1.000000


tab

Series             Obs       Mean       Std Error      Minimum       Maximum
X1                 2000 -0.0259255638  0.9803306476 -3.2729719446
2.8294232659
Z                  2000 -0.0261322052  0.9825802262 -3.3782203854
3.1316805382
X2                 2000 -0.0000000000  0.9823822119 -3.3620263960
3.1261604880

end


--On Thu, May 13, 1999 16:39 +0100 Mark Astley
<mark.astley@bankofengland.co.uk> wrote:

> Dear fellow RATs users.
> 
> I'm having difficulty figuring out how to do something that I thought
> should be pretty easy.  In particular, I'm trying to generate a couple
> of series of random numbers (no problem!) - but I want each of the
> series to be orthogonal to each other (problem!).
> 
> Any ideas/hints gratefully received.
> 
> Many thanks
> 
> Mark Astley
> 
> 
> 





---------- End of message ----------

From: Ying-Man Chung <y_mchung@yahoo.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE:simulation of two independent / orthogonal series
Date: Thu, 13 May 1999 10:29:08 -0700 (PDT)
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Dear Rats users,

Might I request for command to simulate two series of random vaiables (
they are I(1)) which are independent of each other (or orthogonal to
each other.)


Thank you for your help!




WITH BESt wishes,

Sarah

_________________________________________________________
Do You Yahoo!?
Free instant messaging and more at http://messenger.yahoo.com

---------- End of message ----------

From: "jerry" <g8652024@ccunix.ccu.edu.tw>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: a problem about garchmv.prg
Date: Fri, 14 May 1999 14:54:46 +0800
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Hi  everybody
I am confused by the garchmv.prg. Is it necessary to set the upper or =
lower triangular of the constant=20
matrix to be 0.0 on BEKK modle ?  I still can not understant. For =
example, on the bivariate=20
garch(1,1) on BEKK model, the VC21is setted to be zero. Why? I wish =
receive you message .                =20

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD W3 HTML//EN">
<HTML>
<HEAD>

<META content=3Dtext/html;charset=3Dbig5 http-equiv=3DContent-Type>
<META content=3D'"MSHTML 4.72.3110.7"' name=3DGENERATOR>
</HEAD>
<BODY bgColor=3D#ffffff>
<DIV><FONT size=3D2>Hi&nbsp; everybody</FONT></DIV>
<DIV><FONT size=3D2>I am confused by the garchmv.prg. Is it necessary to =
set the=20
upper or lower triangular of the constant </FONT></DIV>
<DIV><FONT size=3D2>matrix to be 0.0 on BEKK modle ?&nbsp; I still can =
not=20
understant. For example, on the bivariate </FONT></DIV>
<DIV><FONT size=3D2>garch(1,1) on BEKK model, the VC21is setted to be =
zero. Why?=20
</FONT><FONT size=3D2>I wish receive you message .&nbsp;</FONT><FONT=20
size=3D2>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbs=
p;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</FONT></DIV></BODY></HTML>

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From: "Okay" <okay@boun.edu.tr>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: a problem about garchmv.prg
Date: Fri, 14 May 1999 14:52:13 +0300
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the message is empty
-----Original Message-----
From: jerry <g8652024@ccunix.ccu.edu.tw>
To: RATS Discussion List <RATS-L@efs.mq.edu.au>
Date: 14 May=FDs 1999 Cuma 09:59
Subject: a problem about garchmv.prg




---------- End of message ----------

From: "VIJAYA SARATHI N." <SARATHI@FPMLAN.iimahd.ernet.in>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: garch-in-m
Date: Sat, 15 May 1999 18:57:42 GMT+0530
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Organization:  IIM, Ahmedabad
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hello colleagues,

i'm trying to estimate a multivariate GARCH-in-M model for a dataset 
consisting of 7 time series. i would greatly appreciate it, if you 
can provide me a RATS code for implementing this model.

regards,

Sarathi
IIM, India.

---------- End of message ----------

From: Attila Ratfai <attila@econ.lsa.umich.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: non-linear solver
Date: Sat, 15 May 1999 14:58:17 -0400 (EDT)
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Dear Rats Users,

I am searching for a code that solves a set of non-linear equations that
look like

	e11*x2+e12*x4   = 0
       	x1^2+x2^2-v11   = 0
	x3^2+x4^2-v22   = 0
      	x1*x3+x2*x4-v21 = 0

where x1,x2,x3,x4 are variables and e11,e12,v11,v22,v21 are constants.

Thanks for any suggestion.

Attila Ratfai


---------- End of message ----------

From: David Gelernter <gelernter.1@osu.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Phillips-Ouliaris tests for cointegration 
Date: Mon, 17 May 1999 23:34:50 -0400
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Dear RATS users:

Does anyone out there have a procedure for computing Phillips-Ouliaris
(1990) Z tests for cointegration.  Actually, any of the other usual tests
will do for my purposes.

If you have a procedure which you recommend, please send it to me.


Thanks.



-----------


       _----- -_
       /         \                
      |           |
      |           |              
      |    __  __)			S. David Gelernter
      |    /  \/   \         		Department of Economics
      /\/\ (o   )o  )		The Ohio State University      
     /c    \__/ --.  		410 Arps Hall   
     (                )		1945 North High Street       
      \_   _-------'  		Columbus OH 43210      
       |  /         \         		E-mail: gelernter.1@osu.edu
       | | '\______)   
       |  \_____)               
       |_____ |                
       |_____/\/\
      /         \            
     /             \




---------- End of message ----------

From: Jen Riley <JenR@ORGO.CAD.GU.EDU.AU>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE: Phillips-Ouliaris tests for cointegration 
Date: Tue, 18 May 1999 14:47:35 +1000
Errors-to: <rats-l-owner@efs.mq.edu.au>
Reply-to: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
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David,

The only thing I know of is the COINTPO.SRC file one can download from 

	http://www.estima.com/procindx.htm

Hope this helps. I'm very interested too if other procedures are
available.


Jen Riley
School of Economics
Griffith University
Nathan QLD 4111
Australia
Phone: 61 7 3875 7796
E-mail: JenR@orgo.cad.gu.edu.au


	-----Original Message-----
	From:	David Gelernter [SMTP:gelernter.1@osu.edu]
	Sent:	Tuesday, May 18, 1999 1:35 PM
	To:	RATS Discussion List
	Subject:	Phillips-Ouliaris tests for cointegration 

	Dear RATS users:

	Does anyone out there have a procedure for computing
Phillips-Ouliaris (1990) Z tests for cointegration.  Actually, any of
the other usual tests will do for my purposes.
	If you have a procedure which you recommend, please send it to
me.
	Thanks.
	-----------


	       _----- -_
	       /         \                
			      |           |
			      |           |              
		      |    __  __)			S. David
Gelernter
		      |    /  \/   \         		Department of
Economics
	      /\/\ (o   )o  )		The Ohio State University      
	     /c    \__/ --.  		410 Arps Hall   
	     (                )		1945 North High Street       
	      \_   _-------'  		Columbus OH 43210      
		       |  /         \         		E-mail:
gelernter.1@osu.edu
			       | | '\______)   
		       |  \_____)               
		       |_____ |                
		       |_____/\/\
	      /         \            
	     /             \



---------- End of message ----------

From: Attila Ratfai <attila@econ.lsa.umich.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Panel VAR
Date: Tue, 18 May 1999 13:17:40 -0400 (EDT)
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Dear Rats Users,

I am about to estimate a large dimensional (54 equations) panel VAR with
some inessential complications. In brief, the main problem is that the
ESTIMATE command does not allow for cross-equation restrictions. One may
also use equation-by-equation LINREG. However, I have not been able to
figure out how to restrict the off-diagonal dynamic slope parameters to
zero, the diagonal parameters equal to each other and the off-diagonal
covariance parameters to zero at the same time.

Any suggestion would be appreciated. 


Attila Ratfai
University of Michigan


---------- End of message ----------

From: Phil Dawson <P.J.Dawson@ncl.ac.uk>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: HEGY procedure 
Date: Thu, 20 May 1999 12:04:27 +0100
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Dear All,

The procedure HEGY (Hylleberg et al, 1990) computes seasonal unit root
tests for quarterly
time series. Has anyone modified this for use with monthly data, please?

Phil Dawson
Dr.P.J. Dawson
Dept. of Agricultural Economics and Food Marketing
The University of Newcastle upon Tyne
Newcastle upon Tyne
NE1 7RU

Tel: +44 (0)191 222 6883
Fax: +44 (0)191 222 6720


---------- End of message ----------

From: "Mats Marcusson (DEP)" <MM@EM.DK>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: SV: HEGY procedure 
Date: Thu, 20 May 1999 14:28:25 +0200
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Hi Phil,
I don't know whether anyone has made a code in Rats for these  tests, but if
You take a look in Beaulieu, J.J. & Miron, J. A.(1993). "Seasonal Unit Roots
and Aggregate US Data". Journal of Econometrics, Vol. 55, pp. 305-28, You'll
find it's fairly easy to make the tests.
/Marcus

-----Oprindelig meddelelse-----
Fra: Phil Dawson [mailto:P.J.Dawson@ncl.ac.uk]
Sendt: Thursday, May 20, 1999 1:04 PM
Til: RATS Discussion List
Emne: HEGY procedure 


Dear All,

The procedure HEGY (Hylleberg et al, 1990) computes seasonal unit root
tests for quarterly
time series. Has anyone modified this for use with monthly data, please?

Phil Dawson
Dr.P.J. Dawson
Dept. of Agricultural Economics and Food Marketing
The University of Newcastle upon Tyne
Newcastle upon Tyne
NE1 7RU

Tel: +44 (0)191 222 6883
Fax: +44 (0)191 222 6720

---------- End of message ----------

From: Jean-Philip BELLOTTEAU <Jean-Philip.Bellotteau@ratp.fr>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: HEGY procedure
Date: Fri, 21 May 1999 11:53:50 -0700
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Content-Type: multipart/mixed; boundary="------------21056504234"

Ce message est en plusieurs parties sous format MIME.

--------------21056504234
Content-Type: text/plain; charset=us-ascii
Content-Transfer-Encoding: 7bit

Hi Phil,

This is a code in Rats for the monthly HEGY tests based on "Beaulieu &
Miron" article

I'm not sure with critical values and the conclusion of the test ! but
any comments or correction are welcome !

Jean-Philip Bellotteau
RATP/CML/MK

--------------21056504234
Content-Type: text/plain; charset=iso-8859-1; name="HEGY.SRC"
Content-Transfer-Encoding: quoted-printable
Content-Disposition: inline; filename="HEGY.SRC"

/*
@HEGY12(lags=3D-1/[0]/..,det=3Dnone/constant/sd/trend/[strend],alpha=3D0.=
01/0.025/[0.05]/0.1,[notrace]/trace) SERIES START END

                                            Juin 1998 -  07/08/98
                                    Jean-Philip BELLOTTEAU - RATP/CML/RRC=



Computes Beaulieu & Miron (1993) seasonal unit root tests for monthly
time series, with and without intercept, seasonal dummies, and/or trend
from the regression:

  a(B)Y13(t) =3D DET + PI1 Y1(t-1) + PI2 Y2(t-1) + PI3 Y3(t-1) +  PI4 Y4(=
t-1)  +  PI5 Y5(t-1)  +  PI6 Y6(t-1) =

                   + PI7 Y7(t-1) + PI8 Y8(t-1) + PI9 Y9(t-1) + PI10 Y10(t=
-1) + PI11 Y11(t-1) + PI12 Y12(t-1) + e(t)

Please note: This procedure is written for use with monthly time series.
             see HEGY4 for quaterly data.

The following OPTIONS are available:

DET   =3D NONE,CONS,SD,TREND,STREND

         Select the determinstic componant. NONE sets the determinstic
         componant to zero; CONS includes intercept only; SD includes CON=
S
         and (11) seasonal dummies; TREND includes CONS and trend; STREND=

         includes CONS, SD, and Trend

LAGS  =3D [0]      Controls default lag length
                 With the option LAGS=3D-1, the procedure determines the =
number
                 of lags by S-BIC criteria with maxlag =3D Schwert inform=
ation



The procedure reports a Lagrange Multiplier of order 1-12  & a Ljung-Box =
test for autocorrelation in
the error term =


REFERENCES:
J.J. Beaulieu, J.A. Miron, "Seasonal unit roots in aggregate U.S. data" -=
 Journal of Econometrics [1993]

monthly version by                      Jean-Philip Bellotteau
Send comments and suggestions to:       Jean-Philip.bellotteau@ratp.fr
*/

PROCEDURE HEGY12 SERIES START END
   TYPE SERIES SERIES
   TYPE INTEGER START END
   *
   OPTION INTEGER LAGS     0
   OPTION CHOICE  DET      5  NONE CONStant SD TREND STREND
   OPTION CHOICE  alpha    3  0.01 0.025 0.05 0.1
   OPTION switch  trace      0
   OPTION switch  graph      0
   *
   LOCAL INTEGER             STARTL ENDL
   LOCAL VEC[SERIES]        y
   LOCAL VEC[REAL]           pi f
   LOCAL REC[REAL]           cv
   LOCAL SERIES             TREND SDUM et
   LOCAL REAL                r3 r32 qlb plb qlm plm sbic minsbic
   LOCAL INT                i j k l retard l12 nobs
   LOCAL VEC[INT]          reg aux
   LOCAL VEC[VEC[INT]]       regexc
   LOCAL   equation            hg lm hg2
   LOCAL   VEC[LABEL]         testcv

   *
   if %defined(series)
   {
      INQUIRE(SERIES=3DSERIES) STARTL>>START ENDL>>END
      *

      dim y(13) pi(12) f(16) cv(6,20) regexc(16) testcv(16)
      do i=3D1,13
         clear y(i)
         label y(i)
         # 'Y'+%string(i)
      endo i
      do i=3D1,16
         com testcv(i) =3D ' '
      endo i

      COM CV =3D || 0.01,  0.025,  0.05,   0.10,    0.01,  0.025,  0.05, =
  0.10,    0.01,  0.025,  0.05,   0.10,    0.01,  0.025,  0.05,   0.10,  =
  0.9,  0.95,  0.975,   0.99 |  $
                 -2.51, -2.18,  -1.89,  -1.58,   -2.53, -2.16,  -1.87,  -=
1.57,   -2.50, -2.16,  -1.88,  -1.55,   -2.31, -1.95,  -1.63,  -1.27,   2=
=2E34,  3.03,  3.71,    4.6  |  $
                 -3.35, -3.06,  -2.80,  -2.51,   -2.48, -2.15,  -1.89,  -=
1.57,   -2.51, -2.16,  -1.87,  -1.54,   -2.30, -1.93,  -1.62,  -1.27,   2=
=2E32,  3.01,  3.68,    4.6  |  $
                 -3.32, -3.02,  -2.76,  -2.47,   -3.28, -3.01,  -2.76,  -=
2.48,   -3.83, -3.51,  -3.25,  -2.95,   -2.61, -2.21,  -1.85,  -1.45,   5=
=2E27,  6.26,  7.19,    8.35 |  $
                 -3.87, -3.58,  -3.32,  -3.06,   -2.52, -2.18,  -1.88,  -=
1.55,   -2.49, -2.16,  -1.88,  -1.54,   -2.28, -1.93,  -1.61,  -1.25,   2=
=2E30,  2.90,  3.64,    4.53 |  $
                 -3.83, -3.54,  -3.28,  -2.99,   -3.31, -3.02,  -2.75,  -=
2.47,   -3.79, -3.50,  -3.24,  -2.95,   -2.57, -2.18,  -1.85,  -1.45,   5=
=2E25,  6.23,  7.14,    8.33 ||

      com r32 =3D sqrt(3)/2.0, r3 =3D sqrt(3)
      if lags>0
         com retard =3D lags
      else
         com retard =3D 0

      SET Y(1)   =3D          SERIES  + SERIES{1} + SERIES{2}  + SERIES{3=
}  + SERIES{4} + SERIES{5}  + SERIES{6}  + SERIES{7} + SERIES{8}  + SERIE=
S{9}  + SERIES{10} + SERIES{11}
      SET Y(2)   =3D      -   SERIES  + SERIES{1} - SERIES{2}  + SERIES{3=
}  - SERIES{4} + SERIES{5}  - SERIES{6}  + SERIES{7} - SERIES{8}  + SERIE=
S{9}  - SERIES{10} + SERIES{11}
      SET Y(3)   =3D                  - SERIES{1}              + SERIES{3=
}              - SERIES{5}               + SERIES{7}              - SERIE=
S{9}               + SERIES{11}
      SET Y(4)   =3D      -   SERIES              + SERIES{2}            =
   - SERIES{4}              + SERIES{6}              - SERIES{8}         =
      + SERIES{10}
      SET Y(5)   =3D 0.5*(-   SERIES  - SERIES{1}+2*SERIES{2}  - SERIES{3=
}  - SERIES{4}+2*SERIES{5}  - SERIES{6}  - SERIES{7}+2*SERIES{8}  - SERIE=
S{9}  - SERIES{10}+2*SERIES{11}            )
      SET Y(6)   =3D r32*(    SERIES  - SERIES{1}              + SERIES{3=
}  - SERIES{4}              + SERIES{6}  - SERIES{7}              + SERIE=
S{9}  - SERIES{10}                         )
      SET Y(7)   =3D 0.5*(    SERIES  - SERIES{1}-2*SERIES{2}  - SERIES{3=
}  + SERIES{4}+2*SERIES{5}  + SERIES{6}  - SERIES{7}-2*SERIES{8}  - SERIE=
S{9}  + SERIES{10}+2*SERIES{11}            )
      SET Y(8)   =3D r32*(-   SERIES  - SERIES{1}              + SERIES{3=
}  + SERIES{4}              - SERIES{6}  - SERIES{7}              + SERIE=
S{9}  + SERIES{10}                         )
      SET Y(9)   =3D 0.5*(-r3*SERIES  + SERIES{1}              - SERIES{3=
}+r3*SERIES{4}-2*SERIES{5}+r3*SERIES{6}  - SERIES{7}              + SERIE=
S{9}-r3*SERIES{10}+2*SERIES{11}            )
      SET Y(10)  =3D 0.5*(    SERIES-r3*SERIES{1}+2*SERIES{2}-r3*SERIES{3=
}  + SERIES{4}              - SERIES{6}+r3*SERIES{7}-2*SERIES{8}+r3*SERIE=
S{9}  - SERIES{10}                         )
      SET Y(11)  =3D 0.5*( r3*SERIES  + SERIES{1}              - SERIES{3=
}-r3*SERIES{4}-2*SERIES{5}-r3*SERIES{6}  - SERIES{7}              + SERIE=
S{9}+r3*SERIES{10}+2*SERIES{11}            )
      SET Y(12)  =3D 0.5*(-   SERIES-r3*SERIES{1}-2*SERIES{2}-r3*SERIES{3=
}  - SERIES{4}              + SERIES{6}+r3*SERIES{7}+2*SERIES{8}+r3*SERIE=
S{9}  - SERIES{10}                         )
      SET Y(13)  =3D          SERIES                                     =
                                                                         =
                               - SERIES{12}

      if graph
      {
         spgraph(hfields=3D3,vfields=3D5)
            graph 1
            # series
            do i=3D1,13
               graph 1
               # y(i)
            enddo i
         spgraph(done)
      }

      clear et trend sdum
      SET TREND STARTL ENDL =3D T
      SEA SDUM
      *
      com nobs =3D endl-startl+1
      DIS
      DIS @20 'Monthly seasonal unit root by HEGY procedure (Beaulieu - M=
iron [1993])'
      dis
      dis 'Series'  %LABEL(SERIES) @-1 '.' %DATELABEL(startl) @-1 '-' @-1=
 %DATELABEL(endl) ',' nobs 'Obs.'
      dis

      enter(varying) reg
      # Y(1){1} Y(2){1} Y(3){1} Y(4){1} Y(5){1} Y(6){1} Y(7){1} Y(8){1} Y=
(9){1} Y(10){1} Y(11){1} Y(12){1}

      com k =3D %rows(reg)
      do i=3D1,11
         dim regexc(i)(k)
         do j=3D1,k
            com regexc(i)(j) =3D reg(j+%rows(reg)-k)
         enddo j
         com k =3D k - 4
      enddo i

      com i =3D 12
      do j=3D9,41,8
         dim regexc(i)(8)
         do l=3D1,8
            com regexc(i)(l) =3D reg(j+l-1)
         enddo l
         com i =3D i + 1
      enddo j

      if det<>1
      {
         if det=3D=3D2
            enter(varying) aux
            # constant
         else if det=3D=3D3
            enter(varying) aux
            # constant sdum{-10 to 0}
         else if det=3D=3D4
            enter(varying) aux
            # constant trend
         else if det=3D=3D5
            enter(varying) aux
            # constant sdum{-10 to 0} trend

         if retard=3D=3D0
         {
            equation hg Y(13)
            # reg aux
            enter(varying) aux
            # reg aux
            equation lm et
            # et{1 to 12} reg aux
         }
         else
         {
            equation hg Y(13)
            # reg Y(13){1 to retard} aux
            enter(varying) aux
            # reg Y(13){1 to retard} aux
            equation lm et
            # et{1 to 12} reg Y(13){1 to retard} aux
         }
      }
      else
      {
         if retard=3D=3D0
         {
            equation hg Y(13)
            # reg
            enter(varying) aux
            # reg
            equation lm et
            # et{1 to 12} reg
         }
         else
         {
            equation hg Y(13)
            # reg Y(13){1 to retard}
            enter(varying) aux
            # reg Y(13){1 to retard}
            equation lm et
            # et{1 to 12} reg Y(13){1 to retard}
         }
      }

      if lags=3D=3D-1
      {
         dis
         dis 'Set of the number of lags by S-BIC criteria (and no serial =
correlation). Sample' %DATELABEL(startl+12+l12) @-1 '-' @-1 %DATELABEL(en=
dl) @-1 ',' endl-startl-12-l12+1 'Obs.'
         dis
         dis 'Lag         S-BIC  p(Q=B0=3D0)'
         dis '**************************'

         if 12*((0.01*nobs)**0.25) - fix(12*((0.01*nobs)**0.25))<0.5
            com l12 =3D fix(12*((0.01*nobs)**0.25))
         else
            com l12 =3D fix(12*((0.01*nobs)**0.25))+1 ; * Schwert informa=
tion [1987]
         com minsbic =3D 1e8
         do i=3D0,l12
            if i>0
               LINREG(noprint) Y(13) STARTL+12+l12 ENDL et
               # reg aux Y(13){1 to i}
            else
               LINREG(noprint) Y(13) STARTL+12+l12 ENDL et
               # reg aux
            com sbic =3D %nobs*log(%seesq*%nobs)+%nreg*log(%nobs)
            if (sbic < minsbic).and.(%qsignif>0.1)
               com minsbic =3D sbic, retard =3D i
            dis ## i @4 sbic #####.## %qsignif
         enddo i
         dis
         dis 'Optimal lag :' retard
         if retard>0
            linreg(noprint,create,define=3Dhg) Y(13)
            # reg aux Y(13){1 to retard}
      }

        LINREG(print=3Dtrace,equation=3Dhg) Y(13) STARTL+12+retard ENDL e=
t
      do i=3D1,12
         COM pi(i) =3D %BETA(i)/SQRT(%SEESQ*%XX(i,i))

         if i=3D=3D1
         {
            if pi(i)>cv(det+1,alpha)
               com testcv(i) =3D '*'
         }
         else if i=3D=3D2
         {
            if pi(i)>cv(det+1,alpha+4)
               com testcv(i) =3D '*'
         }
         else if (float(i)/2.0)<>fix(float(i)/2.0)
         {
            if pi(i)>cv(det+1,alpha+8)
               com testcv(i) =3D '*'
         }
         else
         {
            if pi(i)>cv(det+1,alpha+12)
               com testcv(i) =3D '*'
         }
      enddo i
      com qlb =3D %qstat , plb =3D %qsignif
      do i=3D1,16
         exclude(noprint)
         # regexc(i)
         com f(i) =3D %CDSTAT

         if i>=3D12
         {
            if f(i)>cv(det+1,21-alpha)
               com testcv(i) =3D '*'
         }
      enddo i

        LINREG(noprint,equation=3Dlm) et STARTL+retard+24 ENDL
      cdf(noprint) chisqr %trsq 12
      com qlm =3D %cdstat, plm =3D %signif


      dis 'Freq.                 0     pi    -- pi/2 --    -- 2pi/3 -    =
-- pi/3 --    -- 5pi/6 -   -- pi/6 ---    pi/2   2pi/3    pi/3   5pi/6   =
  pi/6'
      dis 'Roots       Lag     PI1    PI2    PI3    PI4    PI5    PI6    =
PI7    PI8    PI9   PI10   PI11   PI12  F[3-4]  F[5-6]  F[7-8] F[9-10] F[=
11-12]'
      dis '**************************************************************=
*************************************************************************=
******'

      dis %label(series) @13 ## retard @18 ##.## pi(1) @-1 testcv(1) @25 =
##.## pi(2) @-1 testcv(2) @32 ##.## pi(3) @-1 testcv(3) @39 ##.## pi(4) @=
-1 testcv(4) @46 ##.## pi(5) @-1 testcv(5) @53 ##.## pi(6) @-1 testcv(6) =
$
          @60 ##.## pi(7) @-1 testcv(7) @67 ##.## pi(8) @-1 testcv(8) @74=
 ##.## pi(9) @-1 testcv(9) @81 ##.## pi(10) @-1 testcv(10) @88 ##.## pi(1=
1) @-1 testcv(11) @95 ##.## pi(12) @-1 testcv(12) $
          @102 ###.## f(12) @-1 testcv(12) @110 ###.## f(13) @-1 testcv(1=
3) @118 ###.## f(14) @-1 testcv(14) @126 ###.## f(15) @-1 testcv(15) @134=
 ###.## f(16) @-1 testcv(16)
      dis
      dis  'Ljung-Box test for serial correlation' @40 ##.## qlb ', signi=
ficance level of' #.## plb
      dis  'LM test for serial correlation' @40 ##.## qlm ', significance=
 level of' #.## plm

      release y pi f cv reg aux regexc
   }
   else
      dis '@HEGY12(lags=3D-1/[0]/..,det=3Dnone/constant/sd/trend/[strend]=
,alpha=3D0.01/0.025/[0.05]/01,[notrace]/trace) SERIES START END'
end

/*
@HEGY4 (lags=3D-1/[0]/..,det=3Dnone/constant/sd/[strend]/all,alpha=3D[0.0=
5]/0.1,[notrace]/trace) SERIES START END


                                            Juin 1998 -  16/04/99
                                    Jean-Philip BELLOTTEAU - RATP/CML/RRC=



Computes HEGY (1990) seasonal unit root tests for quaterly
time series, with/and without intercept, trend and seasonal dummies
from the regression:

  a(B)Y5(t) =3D DET + PI1*Y1(t-1) + PI2*Y2(t-1) + PI3*Y3(t-1) +  PI4*Y4(t=
-1) + e(t)

Please note: This procedure is written for use with quaterly time series.=

             see HEGY12 for quaterly data.

The following OPTIONS are available:

DET   =3D NONE,CONSTANT,STREND,ALL

LAGS  =3D [0]      Controls default lag length. Can be overridden for
                 With the option LAGS=3D-1, the procedure determines the =
number
                 of lags by S-BIC criteria



The procedure reports a Lagrange Multiplier of order 1-4  & a Ljung-Box t=
est for autocorrelation in
the error term =


quaterly version based on Suliman Al-Turki
Send comments and suggestions to:       Jean-Philip.bellotteau@ratp.fr
*/



PROCEDURE HEGY4 SERIES START END
   TYPE SERIES SERIES
   TYPE INTEGER START END
   *
   OPTION INTEGER LAGS     0
   OPTION CHOICE  DET      4  none constant sd strend all
   OPTION CHOICE  alpha    1  0.05 0.1
   OPTION switch  trace    0
   OPTION switch  graph    0
   *
   LOCAL INTEGER             STARTL ENDL
   LOCAL VEC[SERIES]        y
   LOCAL VEC[REAL]           pi f
   LOCAL REC[REAL]           cv
   LOCAL SERIES             TREND sd et
   LOCAL REAL                qlb plb qlm plm sbic minsbic
   LOCAL INT                i j k l m retard l4 nobs det2 det3
	local string				ch1
   LOCAL VEC[INT]          aux
   LOCAL   equation            hg lm hg2
   LOCAL   VEC[LABEL]         testcv
   LOCAL   VEC[string]         labser

   *
   if %defined(series)
   {
      INQUIRE(SERIES=3DSERIES) STARTL>>START ENDL>>END
      *

      dim y(5) pi(4) f(1) cv(5,6) testcv(3) labser(5)
      do i=3D1,5
         clear y(i)
         label y(i)
         # 'Y'+%string(i)
      endo i
      do i=3D1,3
         com testcv(i) =3D ' '
      endo i

      COM CV =3D || 0.05,   0.1,     0.05,   0.1,    0.05,   0.1    |  $
                 -0.00,  -0.00,   -0.00,  -0.00,   0.00,   0.00   |  $
                 -0.00,  -0.00,   -0.00,  -0.00,   0.00,   0.00   |  $
                 -2.77,  -2.44,   -2.77,  -2.44,   6.63,   5.44   |  $
                 -3.34,  -3.02,   -2.77,  -2.44,   6.56,   5.38   ||
		*              t(PI1)           t(PI2)         F(PI3,PI4)
		* source : Franses and Hobijn (1997)

      if lags>0
         com retard =3D lags
      else
         com retard =3D 0

      SET Y(1) startl+4 endl  =3D  SERIES{1} + SERIES{2} + SERIES{3} + se=
ries{4}
      SET Y(2) startl+4 endl  =3D -SERIES{1} + SERIES{2} - SERIES{3} + se=
ries{4}
      SET Y(3) startl+4 endl  =3D -SERIES{1} + SERIES{3}
      SET Y(4) startl+4 endl  =3D -SERIES{2} + SERIES{4}
      SET Y(5) startl+4 endl  =3D  SERIES - series{4}

		com labser =3D ||'(B + B**2 + B**3 + B**4)'|'-(B - B**2 + B**3 - B**4)'=
|'-(B - B**3)'|'-(B**2 - B**4)'|'(1-B**4)'||

	   if graph
	   {
		   spgraph(hfields=3D2,vfields=3D3)
   		   graph(vlabel=3D%label(series)) 1
		      # series
			   do i=3D1,5
		   	   graph(vlabel=3Dlabser(i)) 1
			      # y(i)
			   enddo i
		   spgraph(done)
	   }

      clear et trend sd
      SET TREND STARTL ENDL =3D T
      SEA sd
      *
      com nobs =3D endl-startl+1
      DIS
      DIS @20 'Quaterly seasonal unit root by HEGY procedure ([1990])'
      dis
      dis 'Series'  %LABEL(SERIES) @-1 '.' %DATELABEL(startl) @-1 '-' @-1=
 %DATELABEL(endl) ',' nobs 'Obs. (' fix(nobs/4) 'years)'

      if det<>5
         com det2 =3D det3 =3D det
      else
         com det2 =3D 3, det3 =3D 4
      do m=3Ddet2,det3
         if m=3D=3D2
         {
            enter(varying) aux
            # constant
				if (det<>5).or.(det=3D=3D5.and.trace)
	            dis 'Input : constant'
         }
         else if m=3D=3D3
         {
            enter(varying) aux
            # constant sd{-2 to 0}
				if (det<>5).or.(det=3D=3D5.and.trace)
	             dis 'Input : constant + seasonal dummies'
         }
         else if m=3D=3D4
         {
            enter(varying) aux
            # constant sd{-2 to 0} trend
				if (det<>5).or.(det=3D=3D5.and.trace)
	            dis 'Input : constant + seasonal dummies + trend'
         }
         else
         {
            dim aux(0)
				if (det<>5).or.(det=3D=3D5.and.trace)
	            dis 'No input'
         }

         if lags=3D=3D-1
         {
            com l4 =3D %imin(nobs/4,12)
            com minsbic =3D 1e8
            if trace
            {
               dis
               dis 'Set of the number of lags by S-BIC criteria (and no s=
erial correlation).'
               dis 'Sample' %DATELABEL(startl+4+l4) @-1 '-' @-1 %DATELABE=
L(endl) @-1 ',' endl-startl-4-l4+1 'Obs.'
               dis
               dis 'Lag         S-BIC  p(Q=B0=3D0)'
               dis '**************************'
            }
            do i=3D0,l4
               if i>0
                  LINREG(noprint) Y(5) STARTL+4+l4 ENDL et
                  # Y(1) Y(2) Y(3) Y(4) aux Y(5){1 to i}
               else
                  LINREG(noprint) Y(5) STARTL+4+l4 ENDL et
                  # Y(1) Y(2) Y(3) Y(4) aux
               com sbic =3D %nobs*log(%seesq*%nobs)+%nreg*log(%nobs)
               if (sbic < minsbic).and.(%qsignif>0.1)
                  com minsbic =3D sbic, retard =3D i
               if trace
                  dis ## i @4 sbic #####.## %qsignif
            enddo i
            if trace
            {
               dis
               dis 'Optimal lag :' retard
            }
         }

         if retard=3D=3D0
         {
            equation hg Y(5)
            # Y(1) Y(2) Y(3) Y(4) aux
            equation lm et
            # et{1 to 12} Y(1) Y(2) Y(3) Y(4) aux
         }
         else
         {
            equation hg Y(5)
            # Y(1) Y(2) Y(3) Y(4) aux Y(5){1 to retard}
            equation lm et
            # et{1 to 12} Y(1) Y(2) Y(3) Y(4) aux Y(5){1 to retard}
         }

           LINREG(print=3Dtrace,equation=3Dhg) Y(5) STARTL+4+retard ENDL =
et
         do i=3D1,4
            COM pi(i) =3D %BETA(i)/SQRT(%SEESQ*%XX(i,i))
         enddo i
         if pi(1)<cv(m+1,alpha)
            com testcv(1) =3D '*'
         if pi(2)<cv(m+1,2+alpha)
            com testcv(2) =3D '*'

         com qlb =3D %qstat , plb =3D %qsignif

         exclude(noprint)
         # y(3) y(4)

         com f(1) =3D %CDSTAT
         if f(1)>cv(m+1,4+alpha)
            com testcv(3) =3D '*'

         LINREG(noprint,equation=3Dlm) et STARTL+4+retard+12 ENDL
         cdf(noprint) chisqr %trsq 12
         com qlm =3D %cdstat, plm =3D %signif

			com ch1 =3D ''
			if testcv(1)<>'*'
				dis(store=3Dch1) 'SI(1,'
			else
				dis(store=3Dch1) 'SI(0,'

			if (testcv(2)=3D=3D'*').and.(testcv(3)=3D=3D'*')
				dis(store=3Dch1) ch1 @-2 '0)'
			else
				dis(store=3Dch1) ch1 @-2 '1)'

			if (det<>5).or.(det=3D=3D5.and.m=3D=3D3.and..not.trace)
			{
	         dis
   	      dis 'Ang. Freq.(w)         0     pi    -- pi/2 --    pi/2'
      	   dis 'Freq.     (v)         0    1/2    --- 1/4 --     1/4'
         	dis 'P=E9riod    (T)        oo      2    ---- 4 ---       4'
	         dis 'Roots       Lag     PI1    PI2    PI3    PI4  F[3-4]  |  L=
B    LM | ccl'
   	      dis '----------------------------------------------------------=
----------------'
			}
         dis %label(series) '(' @-1 m-1 @-1 ')' @13 ## retard @18 ##.## p=
i(1) @-1 testcv(1) @25 ##.## pi(2) @-1 testcv(2) @32 ##.## pi(3) @39 ##.#=
# pi(4) @46 ###.## f(1) @-1 testcv(3) @54 '|' @-1 #.## plb ##.## plm '|' =
ch1

			if (det<>5).or.(det=3D=3D5.and.m=3D=3D4.and..not.trace)
			{
				dis '----------------------------------------------------------------=
----------'
				if alpha=3D=3D1
					dis ' * Significant at 5% level'
				else
					dis ' * Significant at 10% level'
         	dis
			}
      enddo m
      release y pi f cv aux
   }
   else
      dis '@HEGY4(lags=3D-1/[0]/..,det=3Dconstant/sd/strend/[all],alpha=3D=
[0.05]/0.1,[notrace]/trace) SERIES START END'
end

--------------21056504234--


---------- End of message ----------

From: Thamana Lekprichakul <thamana@hawaii.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Index jump when used in combination with procedure
Date: Tue, 25 May 1999 13:06:47 -1000
Errors-to: <rats-l-owner@efs.mq.edu.au>
Reply-to: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
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Dear All,

I am relatively new to RATS, please excuse me if my question appears to be
elementary. I am trying to write a program for Data Envelopment Analysis
(DEA) using Linear programming procedure in RATS version 4.2. This
involves a series of optimization for each observation. A problem I
encountered is that the index jumps from 1 to 4 and then remains at 4
until the end of the loop. Here is my sample commands:

SOURCE(NOECHO) LPQPPROC.SRC
Do K=1,%rows(Z),1
(01.0031)   COMPUTE B(1) = -1*Z(k,1)
(01.0059)   COMPUTE B(2) = -1*Z(k,2)
(01.0087)   COMPUTE A(3,1) = -1*Z(k,3)
(01.0117)   COMPUTE A(4,1) = -1*Z(k,4)
(01.0147)   @LPROG(PRINT) x c A b NEQ ERROR
(01.0200) End do K

The command "compute Bi and A(i,1)" is to replace the old values with new
values from each row of matrix Z. This is where the skipping occurs, e.g. 
from 1 to 4 and remains at 4 until end. However, the optimization
continues from 1 to the end as expected. What went wrong?

Your help will be greatly appreciated.

/Thamana

  ========================================================================
  *    Thamana LEKPRICHAKUL                 (Ph)1+808+944-7425           *
  *    East-West Center-Population Program  (Fax)1+808+944-7490          *
  *    1601 East-West Rd.                   E-Mail: thamana@hawaii.edu   *
  *    Honolulu, Hawaii 96848-1601                                       * 
  *    USA.                                                              *
  ========================================================================


---------- End of message ----------

From: Gustavo Adolfo Hernandez Diaz <ghernandez@dnp.gov.co>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Scatter graph
Date: Wed, 26 May 1999 09:30:44 -0500
Errors-to: <rats-l-owner@efs.mq.edu.au>
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RATS users:

Somebody know, how to make a regresion line in a scatter graph?

Regards

> Gustavo Hern=E1ndez
> ghernandez@dnp.gov.co
> ghernadez30@hotmail.com
> Calle 26 No 13-19, Bogot=E1, Colombia
> Fax: (571) 2818530
> =20
>=20

---------- End of message ----------

From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Index jump when used in combination with procedure
Date: Wed, 26 May 1999 10:13:01 -0600
Errors-to: <rats-l-owner@efs.mq.edu.au>
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> SOURCE(NOECHO) LPQPPROC.SRC
> Do K=1,%rows(Z),1
> (01.0031)   COMPUTE B(1) = -1*Z(k,1)
> (01.0059)   COMPUTE B(2) = -1*Z(k,2)
> (01.0087)   COMPUTE A(3,1) = -1*Z(k,3)
> (01.0117)   COMPUTE A(4,1) = -1*Z(k,4)
> (01.0147)   @LPROG(PRINT) x c A b NEQ ERROR
> (01.0200) End do K
> 
> The command "compute Bi and A(i,1)" is to replace the old values with new
> values from each row of matrix Z. This is where the skipping occurs, e.g. 
> from 1 to 4 and remains at 4 until end. However, the optimization
> continues from 1 to the end as expected. What went wrong?

It turns out that the variable "K" is also used inside the procedure 
and is not declared as a LOCAL variable, which it should be. Thus the 
procedure is changing the value of K when it is executed.

To fix this, you can add K to the list of LOCAL INTEGER variables in 
the PROCEDURE LUDECOMP--one of the procedures stored in teh 
LPQPPROC.SRC file (it's the first procedure on the file, and begins 
right after the block of comments at the top). For example, you can 
just change the line:

 local integer         n 

to

 local integer         n k

Alternatively, you can just use a variable other than "K" as your 
loop index (e.g., "KK" should work just fine).

Sincerely,
Tom Maycock
Estima


------------------------------------------------------------
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|   P.O. Box 1818             |  Support: (847) 864-1910   |
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|   USA                       |  estima@estima.com         |
|                             |  http://www.estima.com     |
------------------------------------------------------------

---------- End of message ----------

From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Scatter graph
Date: Thu, 27 May 1999 09:59:51 -0600
Errors-to: <rats-l-owner@efs.mq.edu.au>
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> 
> Somebody know, how to make a regresion line in a scatter graph?
> 

You can do this by using the SPGRAPH function to place two scatter 
graphs right on top of each other. You'll probably need to use the 
HMAX, HMIN, VMAX, and VMIN options on both SCATTER instructions (set 
to the exact same values) to ensure that the two graphs overlay each 
other seamlessly. Similarly, if you use any of the label options, be 
sure to repeat the label options identically on both SCATTER 
instructions. An example follows below.

By the way, the upcoming Version 5 will include an OVERLAY option, 
just like on the GRAPH command in 4.2/4.3. This will allow you to do 
this with a single SCATTER command.


Here's an example:

* Create data, run regression, get fitted values:
all 100
set y = .3* t + %ran(1.0)
set x = t
linreg y
# x
prj fitted
set trend = t

* Generate the graph. Note lack of HFIELD or VFIELD
*  options on SPGRAPH, because we want the two
*  graphs to appear right on top of each other. 
* Options on SCATTER's identical except for STYLE=LINE 
*  on separate graph. 

spgraph
 scatter(vmax=35,vmin=-5,hmax=100,hmin=0)
 #  trend y
 scatter(style=line,vmax=35,vmin=-5,hmax=100,hmin=0)
 #  trend fitted
spgraph(done)


Sincerely,
Tom Maycock
Estima

------------------------------------------------------------
|   Estima                    |  Sales:   (800) 822-8038   |
|   P.O. Box 1818             |  Support: (847) 864-1910   |
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|   USA                       |  estima@estima.com         |
|                             |  http://www.estima.com     |
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From: szuniga@entelchile.net (SERGIO ZUNIGA  JARA)
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE: Scatter graph
Date: Thu, 27 May 1999 11:00:12 -0600
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I'll write you in spanish:

Hola, lo que yo hago (no se si sera lo mas eficiente), es grabar el archivo
grafico desde RATS con la extension .BMP, luego este lo puedo modificar en
cualquier editor de graficos insertando lineas y lo que quieras. Entonces,
una vez modificado, lo importas a WORD, si ese es tu procesador de textos.
Incluso WORD tiene un editor de graficos, de modo que los cambios puedes
hacerlos alli. Ojala te sirva.
Saludos,

***************************************************
Sergio Zuniga       szuniga@entelchile.net
www.geocities.com/WallStreet/Market/4208
Universidad Catolica del Norte
Larrondo 1281
Coquimbo - Chile
Tel.: 56-51-327248
***************************************************
-----Mensaje original-----
De: Gustavo Adolfo Hernandez Diaz <ghernandez@dnp.gov.co>
Para: RATS Discussion List <RATS-L@efs.mq.edu.au>
Fecha: miércoles, 26 de mayo de 1999 8:42
Asunto: Scatter graph


RATS users:

Somebody know, how to make a regresion line in a scatter graph?

Regards

> Gustavo Hernández
> ghernandez@dnp.gov.co
> ghernadez30@hotmail.com
> Calle 26 No 13-19, Bogotá, Colombia
> Fax: (571) 2818530
>
>


---------- End of message ----------

From: enrico.degiorgi@vontobel.ch
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: SELECT command
Date: Thu, 27 May 1999 17:26:14 +0100
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	Dear RATS User's:

	I use the SELECT command with the STRINGS option. The 
	VECT[STRING] which I would like to display contains 25 components,
but only the first 12 are of interest.
	The option LIMIT=12 doesn't really limit the number of components to
12. Why? 

	With best regard
	Edg 

---------- End of message ----------

From: "Stephen A. DeLurgio" <sad@forecast.umkc.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Scatter graph
Date: Thu, 27 May 1999 12:28:28 -0500
Errors-to: <rats-l-owner@efs.mq.edu.au>
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Dear Mr. Diaz:

I regularly use a very simple command in rats to get a scatter graph in
RATS, here is a simple example - may be too simple for you problem.

linreg y / res
#constant x1 x2
set pred =3D y - res
scat 2
#x1 y
#x1 pred

This will provide a scatter diagram with two variables on the vertical
(i.e., y axis).  In addition, there is a very powerful prj (projection)
command that can be used to create the predicted values.

Regards,

Steve DeLurgio
Professor of Operations Management
http://forecast.umkc.edu


I hope this is helpful - may be you had a much greater problem then
this.  Also, the

Gustavo Adolfo Hernandez Diaz wrote:

> RATS users:
>
> Somebody know, how to make a regresion line in a scatter graph?
>
> Regards
>
> > Gustavo Hern=E1ndez
> > ghernandez@dnp.gov.co
> > ghernadez30@hotmail.com
> > Calle 26 No 13-19, Bogot=E1, Colombia
> > Fax: (571) 2818530
> >
> >




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From: khl2288@UTARLG.UTA.EDU
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: robusterrors option
Date: Thu, 27 May 1999 14:09:26 -0500 (CDT)
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Dear RATS users,
I have a question on ROBUSTERRORS option in GARCH estimation. My
understanding is that use of ROBUSTERRORS option with BFGS algorithm
provides robust standard errors, which is robust to violation of
conditional normality assumption. While estimating a GARCH model with
conditional normality assumption, we specify the log likelihood function
of normal distribution and estimate the parameters by quasi-maximum
likelihood estimator with the ROBUSTERRORS option.
What if we use other distribution such as student-t as a parametric
density function for the log likelihood function with the ROBUSTERRORS
option? If we can use the ROBUSTERRORS with other density funtion, does
this mean that the QMLE proposed by Bollerslev and Wooldridge (1992) is
robust to departure from the assumed(and jointly estimated) other density
functions(i.e. student-t distribution or GED) than normal distribution? If
the QMLE is robust to any assumed parametric density function, why all
articles employing a multivariate GARCH model use it under the normality
assumption? Why not with other distributions, which are known to have
better performance in explaining the fat tails or asymmetry of the
financial data.
My second question is about Baillie and DeGennaro(1990) article. They show
that the ARCH-M parameter is sensitive to the assumed conditional
distribution in terms of size and sign. They estimate and compare a
GARCH-M model assuming a student-t distribution and normal distibution.
Did they use the QMLE or full efficiency MLE?

I think my question is due to misunderstading on some fundamentals of
econometrics. I desparately need your help because I can't get any help
around me.

Thank you so much for your advice in advance.

Kyong H. Lee
Dept. of Economics
University of Texas at Arlington



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From: "Prieul, David" <dprieul@lehman.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: RE: Robusterrors option
Date: Thu, 27 May 1999 20:34:59 +0100
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Kyong,

If you use an extremum estimator P = argmax f(x|p) where the true
density of the data is actually p(x|q), not much changes. Quasi-maximum
likelihood should provide you with the density f(x|p) that minimises a
quantity called the Kullback discrepancy. This may however not be a
sensible thing to do, depending on the choice of f(x|p). Initially,
Garch were estimated using parametric densities such as Gaussian,
Student, or GED, while recent papers follow a nonparametric or
semiparametric approach to make the approximation of p(x|q) by f(x|p)
more accurate. Aymptotic normality implies that the covariance matric of
the estimator is of the form [A^(-1) I A^(-1)].

    



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From: "Estima" <estima@estima.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: SELECT command
Date: Thu, 27 May 1999 16:20:13 -0600
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> 
> 	I use the SELECT command with the STRINGS option. The 
> 	VECT[STRING] which I would like to display contains 25 components,
> but only the first 12 are of interest.
> 	The option LIMIT=12 doesn't really limit the number of components to
> 12. Why? 

The LIMIT option currently only applies when using SELECT with the 
SERIES option. If you want to limit the choices to the first N 
elements of a vector, just create a new vector with the desired 
number of entries and copy the data. For example, suppose you have a 
vector of strings called VS with 25 entries, and you only want the 
user to see the first 12:

dec vec[string] vsshort(12)
ewise vsshort(i) = vs(i)
select(strings=vsshort) n

Sincerely,
Tom Maycock
Estima
 

------------------------------------------------------------
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|   P.O. Box 1818             |  Support: (847) 864-1910   |
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|   USA                       |  estima@estima.com         |
|                             |  http://www.estima.com     |
------------------------------------------------------------

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From: Peter Went <pwent@unlgrad1.unl.edu>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Unsubscribe
Date: Thu, 27 May 1999 16:23:59 -0500
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Please unsubscribe

Estima wrote:
> 
> >
> >       I use the SELECT command with the STRINGS option. The
> >       VECT[STRING] which I would like to display contains 25 components,
> > but only the first 12 are of interest.
> >       The option LIMIT=12 doesn't really limit the number of components to
> > 12. Why?
> 
> The LIMIT option currently only applies when using SELECT with the
> SERIES option. If you want to limit the choices to the first N
> elements of a vector, just create a new vector with the desired
> number of entries and copy the data. For example, suppose you have a
> vector of strings called VS with 25 entries, and you only want the
> user to see the first 12:
> 
> dec vec[string] vsshort(12)
> ewise vsshort(i) = vs(i)
> select(strings=vsshort) n
> 
> Sincerely,
> Tom Maycock
> Estima
> 
> 
> ------------------------------------------------------------
> |   Estima                    |  Sales:   (800) 822-8038   |
> |   P.O. Box 1818             |  Support: (847) 864-1910   |
> |   Evanston, IL 60204-1818   |  Fax:     (847) 864-6221   |
> |   USA                       |  estima@estima.com         |
> |                             |  http://www.estima.com     |
> ------------------------------------------------------------

-- 
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
This message has been sent by Peter Went, who can be reached by
email: pwent@unlgrad1.unl.edu
snail: 210 CBA, Department of Finance, University of Nebraska, Lincoln,
NE 68588-0490, USA
phone: 402.472.0886 (office) or 402.472.2330 (department secretary)
internet: http:/www-class.unl.edu/fina361  and
http://www-class.unl.edu/fina365
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
E N D  O F  M E S S A G E

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From: enrico.degiorgi@vontobel.ch
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Christoph Baum Adress
Date: Fri, 28 May 1999 14:41:35 +0100
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Dear All:

Know someone the E-Mail adress of Christoph Baum, University of Boston?

Tanks so much in advance

Enrico Degiorgi

Vontobel Asset Management AG
Zurich, Switzerland

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From: Marc Sommer <marc.sommer@ruebliland.ch>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Re: Christoph Baum Adress
Date: Sat, 29 May 1999 04:17:19 +0100
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--------------1C6FE97412C5CADE2F8E544E
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Hi Enrico

Kit Baum has posted several messages to this list in the past. The email
address he used was baum@bc.edu. However, the address published on his home
page (http://fmwww.bc.edu/EC-V/Baum.fac.html) is kit.baum@bc.edu.

Hope this helps.

Cheers,
Marc Sommer
--

                         __o       Marc Sommer
'''''''''''''''''''''''_-\<;       Wallbach 15
______________________(_)/-(_)     5213 Villnachern
                                   SWITZERLAND
                   __o
'''''''''''''''''_-\<;             marc.sommer@ruebliland.ch
________________(_)/-(_)           marc.sommer@leu.com


--------------1C6FE97412C5CADE2F8E544E
Content-Type: text/html; charset=us-ascii
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<!doctype html public "-//w3c//dtd html 4.0 transitional//en">
<html>
<tt>Hi Enrico</tt><tt></tt>
<p><tt>Kit Baum has posted several messages to this list in the past. The
email address he used was baum@bc.edu. However, the address published on
his home page (<A HREF="http://fmwww.bc.edu/EC-V/Baum.fac.html">http://fmwww.bc.edu/EC-V/Baum.fac.html</A>) is kit.baum@bc.edu.</tt><tt></tt>
<p><tt>Hope this helps.</tt><tt></tt>
<p><tt>Cheers,</tt>
<br><tt>Marc Sommer</tt>
<br><tt>--</tt><tt></tt>
<p><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
__o&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Marc Sommer</tt>
<br><tt>'''''''''''''''''''''''_-\&lt;;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Wallbach 15</tt>
<br><tt>______________________(_)/-(_)&nbsp;&nbsp;&nbsp;&nbsp; 5213 Villnachern</tt>
<br><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
SWITZERLAND</tt>
<br><tt>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
__o</tt>
<br><tt>'''''''''''''''''_-\&lt;;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
marc.sommer@ruebliland.ch</tt>
<br><tt>________________(_)/-(_)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
marc.sommer@leu.com</tt>
<br><tt></tt>&nbsp;</html>

--------------1C6FE97412C5CADE2F8E544E--


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From: "Guido Travaglini" <g_2002@hotmail.com>
To: "RATS Discussion List" <RATS-L@efs.mq.edu.au>
Subject: Nelson-Plosser dataset
Date: Sun, 30 May 1999 23:02:16 PDT
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Anybody in this list knows wherefrom can I download the complete US
dataset of the Nelson and Plosser article 'Trends and Random Walks in
Macro. Time Series', JME 1982  ? Thanx, Guido, UNIROME, Italy.


______________________________________________________
Get Your Private, Free Email at http://www.hotmail.com

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