Help for carrion et al.(2005) unit root test

Hello,

i do thank you for the User forum. i have difficult when i run the code of the carrion et al.(2005) unit root test. i entered my datatabase throug Gauss 10 and i defined my matrix for one variable for 13 countries over 1980-2012 (33 years) as follows:

» new;
» data ="C:\\Users\\MONTASSAR\\Desktop\\export.xlsx";
» spreadSheetWrite(data, "export.xlsx", "A2", 3);
0.00000000
» cell_range = "A2:N34";
» row = { 2, //starting row
34 };
» col = { 1,
14 };
» print xlsMakeRange(row, col);
A2:N34
» data=spreadSheetReadM("export.xlsx", "A2:N34", 3);
» print data;
» alg=data[.,2];
» bah=data[.,3];
» ira=data[.,4];
» liby=data[.,5];
» oma=data[.,6];
» sau=data[.,7];
» emira=data[.,8];
» yem=data[.,9];
» qat=data[.,10];
» kuw=data[.,11];
» iraq=data[.,12];
» egyp=data[.,13];
» syri=data[.,14];
» print;

» data=alg~bah~ira~liby~oma~sau~emira~yem~qat~kuw~iraq~egyp~syri;
» k=cols(data);
» bigt=rows(data);
» print bigt;
33.000000
» print k;
13.000000

the problem how could i complete the  rest of the code and how could i run the code. i am new  Gauss user. so, i do need your help. the code of carrion unit root test is as follows:

/*
Lectura de la base de dades de PIB per capita
de Maddison.

Període: 1870-1994 per a 17 països (primera columna amb els anys).
*/

new;
cls;

output file=c:\josepll\articles\pankpss\bootstrap\carrion.out reset;
outwidth 256;

load odata[125,18]=c:\josepll\articles\pankpss\unknown\Maddison\pibpcmad.prn;

aus=ln(odata[.,2]);
aust=ln(odata[.,3]);
bel=ln(odata[.,4]);
can=ln(odata[.,5]);
den=ln(odata[.,6]);
fin=ln(odata[.,7]);
fra=ln(odata[.,8]);
ger=ln(odata[.,9]);
ita=ln(odata[.,10]);
jap=odata[1:15,11]|ln(odata[16:rows(odata),11]);
ned=ln(odata[.,12]);
nze=ln(odata[.,13]);
nor=ln(odata[.,14]);
swe=ln(odata[.,15]);
swi=odata[1:29,16]|ln(odata[30:rows(odata),16]);
uk=ln(odata[.,17]);
usa=ln(odata[.,18]);
print "
/*********************************************************************/
/* Anàlisi */
/*********************************************************************/ " ;

/*data=aust~bel~can~den~fra~jap~usa; @ Data set that RHo at the 1% in Ben-David and Papell (1995) @*/
/*data=aust~bel~can~den~fra~jap~swe~uk~usa; @ Data set that RHo at the 1% and 2.5% in Ben-David and Papell (1995) @*/
/*data=aust~bel~can~den~fin~fra~ger~jap~swe~uk~usa; @ Data set that RHo at the 1, 2.5 and 10% in Ben-David and Papell (1995) @*/
/*data=aus~aust~bel~can~den~fin~fra~ger~ita~jap~ned~nze~nor~swe~swi~uk~usa; @ The Whole data set @*/
data=aus~aust~bel~can~den~fin~fra~ger~ita~ned~nze~nor~swe~uk~usa; @ Countries with complete time series 1870-... @
k=cols(data); @ Number of countries (individuals) @
bigt=rows(data); @ Number of time periods @
kernel=0|5;

m=5; @maximum number of structural changes allowed@
model0=2; @ Model quan no hi ha canvis. Segon model: efectes individuals + tendència @
model = 4; @ Model quan hi ha canvis. Canvis en la mitjana i en el pendent @

/*
model0=1; @ Model quan no hi ha canvis. Primer model: efectes individuals @
model = 3; @ Model quan hi ha canvis. Canvis en la mitjana @
*/

/**********************************************************************************************/
/**********************************************************************************************/

eps1=.15; @Value of the trimming (in percentage) for the construction
and critical values of the supF ype tests (used in the
supF test, the Dmax, the supF(l+1|l) and the sequential
procedure). if these test are used, h below should be set
at int(eps1*bigt). But if the tests are not required, estimation
can be done with an arbitrary h.
There are five options: eps1 = .05, .10, .15, .20 or .25.
for each option, the maximal value of m above is: 10 for eps1 = .05;
8 for eps1 = .10, 5 for eps1 = .15, 3 for eps1 = .20 and 2 for eps1 = .25.@
h=int(eps1*bigt); @minimal length of a segment (h >= q). Note: if
robust=1, h should be set at a larger value.@
/* the following are options if p > 0.
----------------------------------- */
fixb=0; @set to 1 if use fixed initial values for beta@
betaini=0; @if fixb=1, load the initial value of beta.@
maxi=20; @maximum number of iterations for the nonlinear
procedure to obtain global minimizers.@
printd=1; @set to 1 if want the output from the iterations
to be printed.@
eps=0.0001; @criterion for the convergence.@
/*--------------------------------- */

robust=0; @set to 1 if want to allow for heterogeneity
and autocorrelation the in residuals, 0 otherwise.
The method used is Andrews(1991) automatic
bandwidth with AR(1) approximation and the
quadratic quernel. Note: Do not set to 1 if
lagged dependent variables are included as
regressors.@
prewhit=0; @set to 1 if want to apply AR(1) prewhitening
prior to estimating the long run covariance
matrix@
hetdat=1; @Option for the construction of the F-tests.
Set to 1 if want to allow different moment matrices of the
regressors accross segments. if hetdat = 0, the same
moment matrices are assumed for each segment and estimated
from the full sample. It is recommended to set hetdat=1. if p > 0
set hetdat = 1.@
hetvar=1; @Option for the construction of the F-tests.
Set to 1 if want to allow for the variance of the residuals
to be different across segments. if hetvar=0, the variance
of the residuals is assumed constant across segments
and constructed from the full sample. This option is not available
when robust = 1.@
hetomega=1; @Used in the construction of the confidence
intervals for the break dates. if hetomega=0,
the long run covariance matrix of zu is assumed
identical accross segments (the variance of the
errors u if robust = 0).@
hetq=1; @Used in the construction of the confidence
intervals for the break dates. if hetq=0,
the moment matrix of the data is assumed
identical accross segments.@
doglobal=1; @set to 1 if want to call the procedure
to obtain global minimizers.@
dotest=1; @set to 1 if want to construct the sup F,
UDmax and WDmax tests. doglobal must be set
to 1 to run this procedure.@
dospflp1=1; @set to 1 if want to construct the sup(l+1|l)
tests where under the null the l breaks are
obtained using global minimizers. doglobal
must be set to 1 to run this procedure.@
doorder=1; @set to 1 if want to call the procedure that
selects the number of breaks using information
criteria. doglobal must be set to 1 to run
this procedure.@
dosequa=1; @set to 1 if want to estimate the breaks
sequentially and estimate the number of
breaks using the supF(l+1|l) test.@
dorepart=1; @set to 1 if want to modify the
break dates obtained from the sequential
method using the repartition method of
Bai (1995), Estimating breaks one at a time.
This is needed for the confidence intervals
obtained with estim below to be valid.@
estimbic=1; @set to 1 if want to estimate the model with
the number of breaks selected by BIC.@
estimlwz=0; @set to 1 if want to estimate the model with
the number of breaks selected by LWZ.@
estimseq=0; @set to 1 if want to estimate the model with
the number of breaks selected using the
sequential procedure.@
estimrep=0; @set to 1 if want to esimate the model with
the breaks selected using the repartition
method.@
estimfix=0; @set to 1 if want to estimate the model with
a prespecified number of breaks equal to fixn
set below.@
fixn=0;

/*****************************************************************************/
/*****************************************************************************/
if model == 3;
z=ones(bigt,1); @ Matrix conformeb by the elements that are allowed to change @
q=cols(z); @ Number of regressors z @
x=0;
p=0;
elseif model == 4;
z=ones(bigt,1)~seqa(1,1,bigt); @ Matrix conformeb by the elements that are allowed to change @
q=cols(z); @ Number of regressors z @
x=0;
p=0;
endif;
@ Matrices of results @
numkpss=zeros(k,1);
denkpss=zeros(k,1);
m_lee_est=zeros(k,4);
m_tb=zeros(1,m);
m_tb2=zeros(1,m);

m_deter=zeros(bigt,k);

j=1;
do until j>k;

{datevec,nbreak,mbic,mlwz,supfl,dateseq,ftest,wftest,reparv}=pbreak(bigt,data[.,j],z,q,m,h,eps1,robust,prewhit,hetomega,
hetq,doglobal,dotest,dospflp1,doorder,dosequa,dorepart,estimbic,estimlwz,
estimseq,estimrep,estimfix,fixb,x,q,eps,maxi,fixb,betaini,printd,hetdat,hetvar,fixn);

nbr=nbreak[2]; @ Numero de canvis detectats pel procediment sequencial al 5% de significacio @

/*nbr=mbic;*/

nbr=mlwz;

if nbr > 0;
tb=selif(datevec[.,nbr],datevec[.,nbr] .gt 0);
{kpsstest,num,den} = pankpss(data[.,j],tb,model,kernel);
deter=dekpss(model,bigt,tb);
beta=data[.,j]/deter;
m_deter[.,j] =deter*beta;

elseif nbr == 0;
{kpsstest,num,den} = pankpss(data[.,j],0,model0,kernel);

z=dekpss(model0,bigt,0);
beta=data[.,j]/z;
m_deter[.,j] =z*beta;

endif;

numkpss[j]=num;
denkpss[j]=den;
m_lee_est[j,.]=kpsstest~nbr~mbic~mlwz;

m_tb=m_tb|datevec;
m_tb2=m_tb2|reparv;

j=j+1;
endo;

test_hom=meanc(numkpss)./meanc(denkpss); @ Assuming homogeneous long-run variance @
test_het=meanc(m_lee_est[.,1]); @ Assuming heterogeneous long-run variance @

m_tb=m_tb[2:rows(m_tb),.];
test_mean=zeros(k,1);
test_var=zeros(k,1);

j=1;
do until j>k;
if m_lee_est[j,2] > 0;
temp1=m_tb[(j*m)-m+1:(j*m),.];
temp2=calcdem(model,temp1[.,m_lee_est[j,2]],bigt); @ Computes the mean and variance @
test_mean[j]=temp2[1];
test_var[j]=temp2[2];
elseif m_lee_est[j,2] == 0;
temp2=calcdem(model0,0,bigt); @ Computes the mean and variance @
test_mean[j]=temp2[1];
test_var[j]=temp2[2];
endif;
j=j+1;
endo;

test_mean=meanc(test_mean);
test_var=meanc(test_var);

testd_hom=sqrt(k)*(test_hom-test_mean)./sqrt(test_var);
testd_het=sqrt(k)*(test_het-test_mean)./sqrt(test_var);
print "************************************";
print "Results for the PANKPSS test ";
print "************************************";

print "Stationarity test with structural breaks (homogeneous): " testd_hom "with p-val: " cdfnc(testd_hom);
print "Stationarity test with structural breaks (heterogeneous): " testd_het "with p-val: " cdfnc(testd_het);
print "Nombre de breaks permesos com a maxim" m;
print "Matriu de tests individuals" m_lee_est;
print "Nombre d'observacions" rows(data);
print "Punts de trencament estimats" m_tb;

@++++++++++++@
@ Bootstrap @
@++++++++++++@

n=cols(data);
t=rows(data);
m_res=zeros(t,n); @ Matriu per deixar els residus @

i=1;
do until i>n; @ Residuals @
m_res[.,i]=data[.,i]-m_deter[.,i];
i=i+1;
endo;
re=2000; @ Nombre de rèpliques del Bootstrap @
m_kpss_hom=zeros(re,1);
m_kpss_het=zeros(re,1);
m_had_test=zeros(n,3);

j=1;
do while j<=re;
tt=rows(m_res);
m_resb=zeros(t+30,n);

i=1;
do while i<=rows(m_resb);
ord=trunc(((tt-1)-1+1)*rndu(1,1)+1);
m_resb[i,.]=m_res[ord,.];
i=i+1;
endo;

m_resb=m_resb[31:rows(m_resb),.];

yb=m_resb;

i=1;
do until i>n;
yb[.,i]=m_deter[.,i]+m_resb[.,i];
i=i+1;
endo;
i=1;
do until i>n;

temp1=m_tb[(i*m)-m+1:(i*m),.]; @ Matrix of Breaks @
dd_tb=temp1[.,m_lee_est[i,2]];

if nbr > 0;
dd_tb=selif(dd_tb,dd_tb .gt 0);
{kpsstest,num,den} = pankpss(yb[.,i],dd_tb,model,kernel);
elseif nbr == 0;
{kpsstest,num,den} = pankpss(yb[.,i],0,model0,kernel);
endif;

numkpss[i]=num;
denkpss[i]=den;
m_lee_est[i,.]=kpsstest~nbr~mbic~mlwz;

i=i+1;
endo;

test_hom=meanc(numkpss)./meanc(denkpss); @ Assuming homogeneous long-run variance @
test_het=meanc(m_lee_est[.,1]); @ Assuming heterogeneous long-run variance @

m_kpss_hom[j,.]=sqrt(n)*(test_hom-test_mean)./sqrt(test_var);
m_kpss_het[j,.]=sqrt(n)*(test_het-test_mean)./sqrt(test_var);

j=j+1;
endo;

e=0.01|0.025|0.05|0.1|0.9|0.95|0.975|0.99;

print "Homogeneous";
q=quantile(m_kpss_hom,e);
e~q;

print "Heterogeneous";
q=quantile(m_kpss_het,e);
e~q;
/****************************************/
/****************************************/
#include c:\josepll\articles\mariam\breaks\brcode2.src;
#include granada.src;

so please could you tell me about the following steps to follow and how to run this code. Really, i am needy of your support. Best regards

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