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From 1 std error bands to 2 std error bands - Imp. Respons

February 6, 2016, 7:21 am
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Hi, I am working on eviews for the first time with a replication package which generates impulse responses with one standard error bands. I would like to see the same graphs with a two standard error bands. I tried to find the solution in the code but I failed (I modified the value after "quantile" with 0.95 at each line and the error bands disappeared...).

So, how I can modify the error bands using this code ? (I'm pretty sure that I can modify the error bands with this program)

Thanks,
V.A.


Code:
'***************************************************************
'*     IMPULSE RESPONSE ON TRUNCATED MA       *
'***************************************************************
PAGESELECT TIMESERIES 
coef(1000) C1
coef(1000) C2
coef(1000) C3
coef(1000) C4

!horz = 3+2
!reps = 1000
!year = 2007  
!start = 1981   

'set variables

%MODEL = "EB_SD2"         '  EB_AT      EB_ATHD   EB_ATLD  EB_STY
%LIST_YEARS = "1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007"'  
%LIST ="AUS AUT BEL CAN  DNK ESP FRA GBR IRL ITA JPN PRT USA DEU"
'%LIST ="AUS AUT BEL CAN  DNK FRA GBR IRL JPN PRT USA DEU"  'ESP  ITA
!m=0
FOR %IDC  {%LIST}
        !m=!m+1
NEXT
IF !m>9 then 
        !m=9
ENDIF

smpl !start !year 

'create storage matrix
FOR %IDC  {%LIST}
        FOR %VAR {%VAR_to_PLOT} 
                matrix(!horz,!reps) S_{%VAR}_{%IDC}=NA
        NEXT 
NEXT

SCALAR A_FAIL=0
SCALAR A_COUNTER=0

rndseed(type=mt) 123456

'set the systems
%sys="sys_"+%VAR_to_PLOT+%MODEL
%sys2="sys_"+%VAR_to_PLOT+%MODEL+"2"

'generating the series
FOR %IDC  {%LIST}
        SMPL @ALL
        GENR IMF_TOT_UT_S{%idc} = IMF_TOT_UT{%idc}
        GENR IMF_TOT_ATT_S{%idc} = IMF_TOT_ATT{%idc}
        GENR IMF_TOT_ATT1_S{%idc} = IMF_TOT_ATT1{%idc}
        GENR IMF_TOT_ATT2_S{%idc} = IMF_TOT_ATT2{%idc}
        'GENR IMF_TOT_ATT3_S{%idc} = IMF_TOT_ATT3{%idc}
        'GENR IMF_TOT_ATT4_S{%idc} = IMF_TOT_ATT4{%idc}
        'GENR IMF_TOT_ATT5_S{%idc} = IMF_TOT_ATT5{%idc}
        GENR IMF_TB_S{%IDC} = IMF_TB{%idc}
        GENR IMF_EB_S{%IDC} = IMF_EB{%idc}
NEXT

FOR %YEAR {%LIST_YEARS}
        GENR DUMMY_S{%YEAR} = DUMMY_{%YEAR}
NEXT

'delete {%sys}*
system {%sys}
!J= 0
!I=0
!K=0
FOR %IDC {%LIST}
        {%sys}.append {%VAR_to_PLOT}{%IDC}  =  C1(1+!I) + C1(2+!J)*IMF_TOT_UT_S{%IDC}*IMF_TB_S{%IDC} + C1(3+!J)*IMF_TOT_UT_S{%IDC}*IMF_EB_S{%IDC} + C1(4+!J)*IMF_TOT_ATT_S{%IDC}*IMF_TB_S{%IDC} + C1(5+!J)*IMF_TOT_ATT_S{%IDC}*IMF_EB_S{%IDC} + C1(6+!J)*IMF_TOT_UT_S{%IDC}(-1)*IMF_TB_S{%IDC}(-1) + C1(7+!J)*IMF_TOT_UT_S{%IDC}(-1)*IMF_EB_S{%IDC}(-1) + C1(8+!J)*IMF_TOT_ATT_S{%IDC}(-1)*IMF_TB_S{%IDC}(-1) + C1(9+!J)*IMF_TOT_ATT_S{%IDC}(-1)*IMF_EB_S{%IDC}(-1) + C1(10+!J)*IMF_TOT_UT_S{%IDC}(-2)*IMF_TB_S{%IDC}(-2) + C1(11+!J)*IMF_TOT_UT_S{%IDC}(-2)*IMF_EB_S{%IDC}(-2) + C1(12+!J)*IMF_TOT_ATT_S{%IDC}(-2)*IMF_TB_S{%IDC}(-2) + C1(13+!J)*IMF_TOT_ATT_S{%IDC}(-2)*IMF_EB_S{%IDC}(-2) + C1(14+!J)*IMF_TOT_UT_S{%IDC}(-3)*IMF_TB_S{%IDC}(-3) + C1(15+!J)*IMF_TOT_UT_S{%IDC}(-3)*IMF_EB_S{%IDC}(-3) + C1(16+!J)*IMF_TOT_ATT_S{%IDC}(-3)*IMF_TB_S{%IDC}(-3) + C1(17+!J)*IMF_TOT_ATT_S{%IDC}(-3)*IMF_EB_S{%IDC}(-3) + C1(18+!J)*IMF_TOT_ATT1_S{%IDC}*IMF_TB_S{%IDC} + C1(19+!J)*IMF_TOT_ATT1_S{%IDC}*IMF_EB_S{%IDC} + C1(20+!J)*IMF_TOT_ATT2_S{%IDC}*IMF_TB_S{%IDC} + C1(21+!J)*IMF_TOT_ATT2_S{%IDC}*IMF_EB_S{%IDC}  + C1(26+!J)*dummy_s1983+ C1(27+!J)*dummy_s1984+ C1(28+!J)*dummy_s1985+ C1(29+!J)*dummy_s1986+ C1(30+!J)*dummy_s1987+ C1(31+!J)*dummy_s1988+ C1(32+!J)*dummy_s1989+ C1(33+!J)*dummy_s1990+ C1(34+!J)*dummy_s1991+ C1(35+!J)*dummy_s1992+ C1(36+!J)*dummy_s1993+ C1(37+!J)*dummy_s1994+ C1(38+!J)*dummy_s1995+ C1(39+!J)*dummy_s1996+ C1(40+!J)*dummy_s1997+ C1(41+!J)*dummy_s1998+ C1(42+!J)*dummy_s1999+ C1(43+!J)*dummy_s2000+ C1(44+!J)*dummy_s2001+ C1(45+!J)*dummy_s2002+ C1(46+!J)*dummy_s2003+ C1(47+!J)*dummy_s2004+ C1(48+!J)*dummy_s2005 + C1(49+!J)*dummy_s2006 + C1(50+!J)*dummy_s2007 '+ C2(24+!J)*IMF_TOT_ATT4_S{%IDC}*IMF_TB_S{%IDC} + C(25+!J)*IMF_TOT_ATT4_S{%IDC}*IMF_EB_S{%IDC} '+ C(26+!J)*IMF_TOT_ATT5_S{%IDC}*IMF_TB_S{%IDC} + C(27+!J)*IMF_TOT_ATT5_S{%IDC}*IMF_EB_S{%IDC}    + C(24+!J)*dummy_s1981    C1(22+!J)*IMF_TOT_ATT3_S{%IDC}*IMF_TB_S{%IDC} + C1(23+!J)*IMF_TOT_ATT3_S{%IDC}*IMF_EB_S{%IDC} +
        !I=!I+50
        '!K=!K+7    
        '!J = !J +7 '***      'if skipped, then panel restrictions are imposed on the GVAR  
NEXT

delete {%sys2}* 
system {%sys2} 
!J= 0
!I=0
!K=0
FOR %IDC {%LIST}
        {%sys2}.append {%VAR_to_PLOT}{%IDC}_0m =  C2(1+!I) + C2(2+!J)*IMF_TOT_UT_S{%IDC}*IMF_TB_S{%IDC} + C2(3+!J)*IMF_TOT_UT_S{%IDC}*IMF_EB_S{%IDC} + C2(4+!J)*IMF_TOT_ATT_S{%IDC}*IMF_TB_S{%IDC} + C2(5+!J)*IMF_TOT_ATT_S{%IDC}*IMF_EB_S{%IDC} + C2(6+!J)*IMF_TOT_UT_S{%IDC}(-1)*IMF_TB_S{%IDC}(-1) + C2(7+!J)*IMF_TOT_UT_S{%IDC}(-1)*IMF_EB_S{%IDC}(-1) + C2(8+!J)*IMF_TOT_ATT_S{%IDC}(-1)*IMF_TB_S{%IDC}(-1) + C2(9+!J)*IMF_TOT_ATT_S{%IDC}(-1)*IMF_EB_S{%IDC}(-1) + C2(10+!J)*IMF_TOT_UT_S{%IDC}(-2)*IMF_TB_S{%IDC}(-2) + C2(11+!J)*IMF_TOT_UT_S{%IDC}(-2)*IMF_EB_S{%IDC}(-2) + C2(12+!J)*IMF_TOT_ATT_S{%IDC}(-2)*IMF_TB_S{%IDC}(-2) + C2(13+!J)*IMF_TOT_ATT_S{%IDC}(-2)*IMF_EB_S{%IDC}(-2) + C2(14+!J)*IMF_TOT_UT_S{%IDC}(-3)*IMF_TB_S{%IDC}(-3) + C2(15+!J)*IMF_TOT_UT_S{%IDC}(-3)*IMF_EB_S{%IDC}(-3) + C2(16+!J)*IMF_TOT_ATT_S{%IDC}(-3)*IMF_TB_S{%IDC}(-3) + C2(17+!J)*IMF_TOT_ATT_S{%IDC}(-3)*IMF_EB_S{%IDC}(-3) + C2(18+!J)*IMF_TOT_ATT1_S{%IDC}*IMF_TB_S{%IDC} + C2(19+!J)*IMF_TOT_ATT1_S{%IDC}*IMF_EB_S{%IDC} + C2(20+!J)*IMF_TOT_ATT2_S{%IDC}*IMF_TB_S{%IDC} + C2(21+!J)*IMF_TOT_ATT2_S{%IDC}*IMF_EB_S{%IDC} + C2(26+!J)*dummy_s1983+ C2(27+!J)*dummy_s1984+ C2(28+!J)*dummy_s1985+ C2(29+!J)*dummy_s1986+ C2(30+!J)*dummy_s1987+ C2(31+!J)*dummy_s1988+ C2(32+!J)*dummy_s1989+ C2(33+!J)*dummy_s1990+ C2(34+!J)*dummy_s1991+ C2(35+!J)*dummy_s1992+ C2(36+!J)*dummy_s1993+ C2(37+!J)*dummy_s1994+ C2(38+!J)*dummy_s1995+ C2(39+!J)*dummy_s1996+ C2(40+!J)*dummy_s1997+ C2(41+!J)*dummy_s1998+ C2(42+!J)*dummy_s1999+ C2(43+!J)*dummy_s2000+ C2(44+!J)*dummy_s2001+ C2(45+!J)*dummy_s2002+ C2(46+!J)*dummy_s2003+ C2(47+!J)*dummy_s2004+ C2(48+!J)*dummy_s2005 + C2(49+!J)*dummy_s2006 + C2(50+!J)*dummy_s2007'+ C2(24+!J)*IMF_TOT_ATT4_S{%IDC}*IMF_TB_S{%IDC} + C2(25+!J)*IMF_TOT_ATT4_S{%IDC}*IMF_EB_S{%IDC} '+ C2(26+!J)*IMF_TOT_ATT5_S{%IDC}*IMF_TB_S{%IDC} + C2(27+!J)*IMF_TOT_ATT5_S{%IDC}*IMF_EB_S{%IDC}     + C2(24+!J)*dummy_s1981    C2(22+!J)*IMF_TOT_ATT3_S{%IDC}*IMF_TB_S{%IDC} + C2(23+!J)*IMF_TOT_ATT3_S{%IDC}*IMF_EB_S{%IDC} + 
        !I=!I+50
        '!K=!K+7
        '!J = !J +7 '*** 'if skipped, then panel restrictions are imposed on the GVAR  
NEXT

smpl !start !year
{%sys}.sur 


delete resid1* resid2* resid3* resid4* resid6* resid7* resid8* resid9* resid0*

{%sys}.makeresids(n=gres)

FOR !i = 1 TO !m
        series resid{!i}=resid0{!i}
        delete resid0{!i}
NEXT

scalar temp_res = 0

FOR %IDC  {%LIST}
        FOR %VAR {%VAR_to_PLOT} 
                temp_res = temp_res + 1
                !p = temp_res
                genr res_{%VAR}_{%IDC} = resid{!p}
        NEXT
NEXT
delete temp_res* 
'********************************************************************************
'   BOOTSTRAP (SMALL SAMPLE CORRECTION)
'********************************************************************************
model model
model.merge {%sys}

FOR %IDC {%LIST}
        FOR %VAR {%VAR_to_PLOT} 
                model.addassign {%VAR}{%IDC} 
         NEXT
NEXT

'*******************************************************************************
FOR !j=1 TO !reps
    A_COUNTER = A_COUNTER + 1
    'resampling residuals
    smpl !start !year
    delete g1*
    group g1
    FOR %IDC {%LIST}
        FOR %VAR {%VAR_to_PLOT} 
            g1.add res_{%VAR}_{%IDC}
        next 
    next  
    smpl 1992 2007
    scalar sigma=@var(res_{%VAR_to_PLOT}_deu)
    smpl !start 1991
    res_{%VAR_to_PLOT}_deu=(sigma^0.5)*nrnd     
    smpl !start !year
    g1.resample(block = 2,dropna)
    FOR %IDC {%LIST}
        FOR %VAR {%VAR_to_PLOT} 
            series {%VAR}{%IDC}_a=res_{%VAR}_{%IDC}_b
            delete res_{%VAR}_{%IDC}_b
        next 
    next

    '    FOR %IDC {%LIST}
    '            FOR %VAR {%VAR_to_PLOT} 
    '                    res_{%VAR}_{%IDC}.resample(dropna) {%VAR}{%IDC}_A
    '            NEXT
    '    NEXT

    model.stochastic(i=b, v=t, r=100, s=!START+1 !YEAR, f=.2, b=.83,c=F)
    model.solveopt(s=m)
    'model.append assign @all  _0m 'to be activated only if sol=det) 
    smpl !START  !YEAR 
    model.solve

    smpl !START !YEAR 
    {%sys2}.sur 
    SMPL @ALL

        '***** SCENARIO
    model model_2
    model_2.merge {%sys2}
    model_2.append assign @all  _ALT
    model_2.scenario(n, a=_1) "Alternative"
    model_2.scenario "Alternative"

    FOR %YEAR {%LIST_YEARS}
        SMPL @ALL
        GENR DUMMY_S{%YEAR}_1 = DUMMY_S{%YEAR}
    NEXT
    
    FOR %IDC  {%LIST}
            SMPL @ALL
            GENR IMF_TOT_UT_S{%idc}_1 = IMF_TOT_UT_S{%idc}
            GENR IMF_TOT_ATT_S{%idc}_1 = IMF_TOT_ATT_S{%idc}
            GENR IMF_TOT_ATT1_S{%idc}_1 = IMF_TOT_ATT1_S{%idc}
            GENR IMF_TOT_ATT2_S{%idc}_1 = IMF_TOT_ATT2_S{%idc}
            'GENR IMF_TOT_ATT3_S{%idc}_1 = IMF_TOT_ATT3_S{%idc}
            'GENR IMF_TOT_ATT4_S{%idc}_1 = IMF_TOT_ATT4_S{%idc}
            'GENR IMF_TOT_ATT5_S{%idc}_1 = IMF_TOT_ATT5_S{%idc}
            GENR IMF_TB_S{%idc}_1 = IMF_TB_S{%idc}
            GENR IMF_EB_S{%idc}_1 = IMF_EB_S{%idc}
            
            smpl !YEAR+1 !YEAR+!horz
            IMF_TOT_UT_S{%idc}_1 =0
            IMF_TOT_UT_S{%idc} =0
            IMF_TOT_ATT_S{%idc}_1 = 0
            IMF_TOT_ATT_S{%idc} = 0
            IMF_TOT_ATT1_S{%idc}_1=0
            IMF_TOT_ATT1_S{%idc}=0
            IMF_TOT_ATT2_S{%idc}_1=0
            IMF_TOT_ATT2_S{%idc}=0
            'IMF_TOT_ATT3_S{%idc}_1=0
            'IMF_TOT_ATT3_S{%idc}=0
            IMF_EB_S{%idc}_1 = 1
            IMF_EB_S{%idc} = 0
            IMF_TB_S{%idc}_1 = 0
            IMF_TB_S{%idc} = 0
                
            smpl !YEAR+1 !YEAR+1
            IMF_TOT_UT_S{%idc}_1 =1
            IMF_TOT_ATT1_S{%idc}_1= eq_style{%IDC}_3.@coefs(2)*IMF_TOT_UT_S{%idc}_1
            IMF_TOT_ATT2_S{%idc}_1=eq_style{%IDC}_4.@coefs(2)*IMF_TOT_UT_S{%idc}_1
            
            smpl !YEAR+2 !YEAR+2
            IMF_TOT_ATT1_S{%idc}_1=IMF_TOT_ATT2_S{%idc}_1(-1)
                IMF_TOT_ATT_S{%idc}_1=IMF_TOT_ATT1_S{%idc}_1(-1)
            
            smpl !YEAR+3 !YEAR+3
            IMF_TOT_ATT_S{%idc}_1=IMF_TOT_ATT1_S{%idc}_1(-1)
            smpl !YEAR+1 !YEAR+!horz
            model_2.override(m) IMF_EB_S{%idc} IMF_TOT_ATT_S{%idc} IMF_TOT_ATT1_S{%idc} IMF_TOT_ATT2_S{%idc} IMF_TOT_UT_S{%idc}
    NEXT        

    model_2.scenario(C) "Baseline"
    model_2.stochastic(i=b, d=T, v=t, r=100, s=!START+1 !YEAR, f=.1, b=.83,c=F)
    model_2.solveopt(s=m,d=d, A=T)
    smpl !YEAR+1 !YEAR+!horz

    model_2.solve

    'if cumulated IR are needed uncomment this
    FOR %IDC {%LIST}

             smpl !year+1 !year+1
            genr {%VAR}{%IDC}_0m_1_ALTM_c = {%VAR}{%IDC}_0m_1_ALTM
             smpl !year+2 !year+!horz
            series {%VAR}{%IDC}_0m_1_ALTM_c = {%VAR}{%IDC}_0m_1_ALTM_c(-1)+{%VAR}{%IDC}_0m_1_ALTM
             
             smpl !year+1 !year+!horz 
             FOR %VAR {%VAR_to_PLOT} 
                 'stomNA( {%VAR}{%IDC}_0m_1_ALTM,temp) 'In the case of spread
                'activate if cumulated IR are needed
                stomNA( {%VAR}{%IDC}_0m_1_ALTM_c,temp) 
                 colplace(S_{%VAR}_{%IDC},temp,!j)
             NEXT 
    NEXT
NEXT

'************************************************************
'     END OF THE BOOTSTRAP          *
'************************************************************
'find bootstrap percentiles for each horizon
FOR %IDC {%LIST}
    FOR %VAR {%VAR_to_PLOT}     
        matrix(!horz,3) rsp_{%VAR}_{%MODEL}_{%IDC}
        delete rtmp*
        rowvector rtmp
            FOR !i=1 TO !horz
                rtmp = @rowextract(S_{%VAR}_{%IDC},!i)
                rsp_{%VAR}_{%MODEL}_{%IDC}(!i,1) = @quantile(rtmp,0.84)
                rsp_{%VAR}_{%MODEL}_{%IDC}(!i,2) = @quantile(rtmp,0.50)
                rsp_{%VAR}_{%MODEL}_{%IDC}(!i,3) = @quantile(rtmp,0.16)
            NEXT
    NEXT
NEXT


FOR %IDC {%LIST}
        delete {%VAR_to_PLOT}{%IDC}_*
        delete {%VAR_to_PLOT}_{%IDC}_a*
NEXT
delete  s_* rtmp temp model* %sys2 resid* res* TOT_SHOCK_S* IMF_TOT_S* IMF_EB_S* IMF_TB_S* IMF_EB1_S* IMF_TB1_S* IMF_TOT_UT_S* IMF_TOT_ATT_S* IMF_TOT_ATT1_S* IMF_TOT_ATT2_S* IMF_TOT_ATT3_S* IMF_TOT_ATT4_S* IMF_TOT_ATT5_S* DUMMY_S* 'rsp*



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Running code when model does not solve

February 6, 2016, 9:15 am
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We'll need a lot more information.

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New Series By Sort

February 6, 2016, 9:17 am
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The only way I think is by converting to vectors, or by doing it element by element.

Wanting to reverse the order of observations is somewhat alien in time series.

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New Series By Sort

February 6, 2016, 9:36 am
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For sure, it's not for estimation or anything. I'm doing this data manipulation purely for a supplementary visualization.

I have unstructured/undated data, and would like to sort them from high to low (value-wise). How would a vector help with that? I noticed vector and matrix objects don't support the .sort extension. Or is it something different?

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New Series By Sort

February 6, 2016, 9:43 am
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New Series By Sort

February 6, 2016, 9:46 am
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Cool now I'm on to something :)

Thanks to you

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New Series By Sort

February 6, 2016, 9:57 am
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Maybe I'm not out of the woods yet... If @rapplyranks needs a vector with unique integers, then I will need to fill that vector with the right numbers. Does eviews have a way to retrieve an element's magnitude rank within the sample?

Say if I have a continuous series that ranges from 100 (min)to 1000(max) with 200 observations, I'd need some command to know how large that element was. Say if the element was 1000, then it'd be the highest, so 1. Or if it was 945, maybe it'd be something like 7, and so forth all the way to x=100, which should be the lowest, so 200 -- in keeping with the descending order example. Otherwise I'd have no way to assign the right value to the order vector for the @rapplyranks command.

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New Series By Sort

February 6, 2016, 11:46 am
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@ranks (as mentioned in the example)

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Modelling Reflexivity with a Time Series Model

February 6, 2016, 11:59 am
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I'm new to this forum so hi to everyone!

I would like to find a time series model that is capable of modelling two time series with following properties:

1) consider time series X and Y. X is influencing Y positively and vice versa so that they move in the same direction. However one time series is influencing the other one stronger, so that the move more and more apart.

2) if they have moved too much apart a trend reversal sets in both time series.

You can find an illustration of the two time series here: http://www.georgesoros.com/wp-content/u ... 0004_c.gif

Does anyone know a proper time series model for this dynamik? Thanks in advance!

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Running code when model does not solve

February 6, 2016, 1:09 pm
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Ok sorry I am not very good at explaining with the correct terminology. I will post again tomorrow when I can log on and show you the code. Sorry for poor query.

NG

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Using functions on groups

February 6, 2016, 2:27 pm
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I have a group and I would like to calculate statistics for the series in the group. So for example
Code:

group group_return rm

defines the group group_return and contains the series rm. In Eveiws, I can execute
Code:

@mean(rm)

but I cannot execute
Code:

@mean(group_return(1))

Although, I can do
Code:

ls group_return(1) c

in place of
Code:

ls rm c

So I would like to know why I cannot use / how do I use function commands like @mean() on series in a group. I.e. I would like to use commands like 
Code:

@mean(group_return(1))


Thanks for the help.

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Using functions on groups

February 6, 2016, 3:11 pm
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Code:

show @mean(group_return(1))


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I need help in estimating the CCC model

February 6, 2016, 3:26 pm
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EViews Gareth wrote:Update to the latest version,


So, it is a problem of the software version or is a procedural problem?

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I need help in estimating the CCC model

February 6, 2016, 4:17 pm
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How to create a matrix out of various test results?

February 6, 2016, 5:17 pm
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Hi,
I need to extract two elements from ARCH-LM test results and store them in a matrix. What I did so far is I could just manage to extract the F-stat and F-prob values from the tables of test results, but I could not merge the results and store them into a single matrix. It extracts the values and stores them into different matrices: 
Code:
smpl @all
for %k series01 series02 series03
      equation eq1.ls {%k} c  
      eq1.archtest 
      freeze(ARCH_LM_{%k}) eq1.archtest 
      matrix(3,2) ArchLM_{%k}
      !rowcounter=1
      ArchLM_{%k}(!rowcounter,1) = @val(ARCH_LM_{%k}(3,2)) 
      ArchLM_{%k}(!rowcounter,2) = @val(ARCH_LM_{%k}(3,5))    
      !rowcounter=!rowcounter+1  
next


Could anyone please help me on this issue?
Many thanks in advance ....

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How to create a matrix out of various test results?

February 6, 2016, 5:57 pm
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I'm not sure I understand. You appear to be deliberately storing the results into different matrices, rather than storing them into the same one.

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How to create a matrix out of various test results?

February 7, 2016, 7:58 am
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I want to store the test results separately and also I need to store the F-stat and F-prob values of all tests in one matrix.

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How to create a matrix out of various test results?

February 7, 2016, 8:28 am
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Using functions on groups

February 7, 2016, 9:45 am
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That's great, thank you. How would I store the value. For example, I can issue the command
Code:

scalar t = @mean(rm)

but the following command does not
Code:

scalar t = @mean(group_return(1))


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Importing data description bug

February 7, 2016, 12:44 pm
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There is a bug in Eview's importing feature. I have an excel sheet where the eviews name of the variable are in line 1 and the eview's description of the variable are in line 2. When the variable does not exist in Eviews, and I import the data, Eviews will correctly import both the name and description. However, if the variable already exists, and I try to import the variable and overwrite the existing variable, Eviews will not import the description.

Attached are an Excel and Eviews file to replicate the bug. Try importing the data from the Excel file, and you'll see Eviews will fail to import the description.

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