xtunitroot error 'too many variables specified'

[Sebastian Kruk]

Good afternoon.

The dataset looks like this:

* Example generated by -dataex-. For more info, type help dataex
clear
input long pais float(year ln_co2pc_gr ln_gdppc_gr co2pc_gr gdppc_gr res_share_ch ei_ch)
1 1970            .           .            .            .          .             .
1 1971    .04318523           .    .04413044            .  -.6838717             .
1 1972  -.003316879           .  -.003311346            .  -.5843963             .
1 1973   .035467148           .   .036103528            . -.19065896             .
1 1974   -.01744461           .  -.017293148            .  1.1816183             .
1 1975   -.04150105           .   -.04065147            . -1.1635613             .
1 1976    .02756405           .   .027947137            .  -.4502255             .
1 1977      .027318           .    .02769508            .   .4519265             .
1 1978   -.01889515           .   -.01871785            .   .7232267             .
1 1979    .03856087           .    .03931371            . -.03159031             .
1 1980  -.033974648           .   -.03340414            .  1.6892276             .
1 1981   -.03340912           .  -.032857135            .   .3562208             .
1 1982  -.029852867           .   -.02941179            .     .22335             .
1 1983   .010718346           .   .010776284            .  .55891395             .
1 1984  -.025982857           .   -.02564822            .   .3025955             .
1 1985   -.09177828           .   -.08769248            .   .2845615             .
1 1986    .06215334           .    .06412572            . -.57471985             .
1 1987   .035816193           .     .0364652            . -.04727695             .
1 1988    .01995754           .   .020158285            . -2.0891473             .
1 1989     -.051548           .   -.05024241            .  -.8556552             .
1 1990   -.08002138           .   -.07690334            .  2.5997005             .
1 1991    .04825544   .07338333    .04943905    .07614343  -.7621235   -.003763424
1 1992   .014944077   .06285858   .015055898   .064876065   .8977495   -.004395153
1 1993   .020612717   .06581497   .020826785    .06802921   .9866562   -.005773365
1 1994    .02478409   .04406929     .0250941    .04505472  .05130513   .0004600968
1 1995  -.012306213  -.04115677  -.012230685   -.04032156   .1040608    .003823304
1 1996    .04091835   .04185772    .04176683    .04274599   -.889446  -.0021086514
1 1997  .0079956055   .06639385   .008027056    .06864754   .5504242  -.0032310374
1 1998   .014033318   .02645588   .014132484    .02680954  .14474016  -.0015993596
1 1999   .029364586  -.04557419    .02980039   -.04455157 -1.4123623    .004682073
1 2000  -.013619423 -.018927574  -.013527458  -.018749123  -.7077209    .006655703
1 2001   -.04242325  -.05601025   -.04153631   -.05447092  1.7975198  -.0001920025
1 2002  -.064777374  -.12618446   -.06272339   -.11854777   .4461319      .0078657
1 2003    .05957603   .07396126     .0613862    .07676546  -.9268353    -.00195185
1 2004    .07559109    .0758953    .07852178    .07884938 -1.3884256  .00006687366
1 2005    .02943516  .074453354   .029872544    .07729475  .53251714   -.005813921
1 2006    .04256248   .06724262    .04348141    .06955548  .10701164  -.0033557636
1 2007    .08056164   .07625961    .08389506    .07924238 -1.0392638  -.0004027938
1 2008    .02333355   .02984524    .02360851    .03029477   .4650531  -.0015919253
1 2009   -.07792091  -.07100487   -.07496273   -.06854227  1.7649453  -.0008191558
1 2010    .07104397    .0862999    .07362857    .09013356   .3953756   -.003054402
1 2011   .036629677   .04797363    .03730834      .049143  .19075124   -.001405331
1 2012   .005539894  -.02078247     .0055553  -.020567933  -.5361876   .0016440516
1 2013   .017653465  .013266563   .017810239    .01335466 .006848871   .0002165544
1 2014   -.02623844 -.035855293  -.025896933  -.035220277   .9186995   .0021337224
1 2015    .01380539  .016727448   .013901046   .016867941 -1.0696038   -.001246282
1 2016  -.014710426 -.031025887  -.014602168   -.03054932   .5231693    .001930914
1 2017  -.024267197   .01653099   -.02397574   .016668873  1.1013072  -.0032586295
1 2018   -.04940796 -.034734726   -.04820735   -.03413885  -.1671047   .0007113228
1 2019   -.03939629 -.031279564      -.03863   -.03079537  -.0826373  .00057273713
1 2020            .           .            .            .          .             .
2 1970            .           .            .            .          .             .
2 1971    .03735685           .    .03806343            .  -3.583941             .
2 1972   .006715775           .   .006738317            .  -3.595976             .
2 1973     .0723815           .    .07506522            .  1.0047908             .
2 1974  -.013946533           .  -.013849764            .  -2.443425             .
2 1975    .06486988           .   .067020305            .  -5.076143             .
2 1976    .06060696           .    .06248108            .   1.480327             .
2 1977    .15053034           .     .1624507            .  -.9558575             .
2 1978    .05724525           .    .05891533            .   -5.38981             .
2 1979     .2042713           .      .226631            .  1.2600955             .
2 1980   .064723015           .   .066863455            .   2.294343             .
2 1981  .0004091263           .  .0004090958            . -4.1512866             .
2 1982  -.010876656           .  -.010817155            .  -1.286386             .
2 1983   -.08180618           .   -.07854988            .  -.2205604             .
2 1984    .02359295           .    .02387344            .  .52316314             .
2 1985   -.05776501           .   -.05612823            .  -.6478212             .
2 1986      .281785           .     .3254935            .  -.4750064             .
2 1987   .062625885           .      .064629            .  -5.436717             .
2 1988   .008115768           .   .008148801            .   .7065676             .
2 1989    .05866051           .    .06041504            . -1.0042635             .
2 1990   -.02052593           .   -.02031715            .   .9601043             .
2 1991 -.0022001266   -.0434866  -.002197118    -.0425546   -.832518     .01618729
2 1992   -.12391949  -.06234837   -.11654889   -.06044444  -1.431724   -.023318866
2 1993  -.013767242 .0041484833   -.01367371  .0041567744  -1.408625   .0024125425
2 1994     .1101904  .016054153      .116491   .016183637   1.988639    .008190635
2 1995   .034041405  .015937805    .03462734     .0160664 -1.8642867    .006239843
2 1996     .0448246  .034890175    .04584408   .035505243   .2397784   .0004650287
2 1997    .12469387   .04205894     .1328022    .04295658  -.6353271      .0079945
2 1998   -.02123642  .032331467  -.021012744   .032859545  .59386986    -.00912828
2 1999    .05735397 -.000790596    .05903048 -.0007899858   -2.49613    .004564094
2 2000    .04701233   .03958511    .04813543    .04037887   2.706013    .004879483
2 2001   -.04536247 -.027560234   -.04434931   -.02718382 -1.0512298    -.00504469
2 2002  -.015072823 .0044546127  -.014959444   .004464366  .05328191   -.002887168
2 2003  .0005598068  .018220901 .00055978925   .018387806 -.10914221   -.002539354
2 2004   .012296677  .010541916   .012372726   .010597872  -.2341628 -.00010549345
2 2005   .017718315  .035025597   .017876115   .035645753   .7751106   -.003184567
2 2006   .008773804    .0531826   .008812081    .05462261 -1.2377497    -.00716155
2 2007   .010412216   .01738453   .010466738   .017535998   .3962357  -.0003483627
2 2008    .15820885  .002524376     .1714113   .002528249   -.927561     .01863219
2 2009  -.035902023  -.05624008   -.03526547   -.05468835   .4229553    .003304608
2 2010   -.07738018  -.02677727   -.07446237   -.02642146  -.8142483    -.00818127
2 2011     .0507679 -.009784698    .05207909    -.0097374  -.3567042    .007837007
2 2012   -.06547451 -.007086754   -.06337695   -.00706133 -.08397305  -.0083234655
2 2013   .010436058 -.016260147   .010490787  -.016129091  -1.083137   .0025542805
2 2014   -.09462738 -.003108978   -.09028804  -.003103939  .20679207    -.01407241
2 2015  -.010979652  .022444725  -.010919874   .022698896 -1.1899234   -.004809058
2 2016   -.06432438  .022831917   -.06229942   .023094524   .5234437   -.009212221
2 2017   .026329994   .00321579   .026679393    .00322063   .5948993    .004054799
2 2018    .01234913 -.007247925   .012426493  -.007221923   .7516962    .000632762
end
label values pais pais
label def pais 1 "Argentina", modify
label def pais 2 "Barbados", modify

In the example appear two country, but I have many more, 25.

My problem is the following.

When I execute:

xtunitroot ips ln_co2pc_gr ln_gdppc_gr if year > 1990 & year <2020

I receive a message:

too many variables specified
r(103);

It only works if I use one variable:

xtunitroot ips ln_co2pc_gr if year > 1990 & year <2020

Is It equivalent if I do one test by variable to do one test with all variables?

What's the problem?

Thank you for any answers and help!

[Fei Wang]

Sebastian, the unit-root test, by definition, is for a single variable. You may test whichever variable you'd like to test, one by one.

[Sebastian Kruk]

Thank you!

[RAKOTONDRAZAKA Tojo]

Sebastian, the unit-root test, by definition, is for a single variable. You may test whichever variable you'd like to test, one by one.

Dear Fei Wang,

I am conducting my research about CHINESE FDI IN AFRICA, my main purpose of the study is: Does Chinese FDI bring some technological spillover into African countries? thus my data consist of N=32 African countries in a period of time t=14 years. My 1st dependant variable is y1=TFP (Total Factor Productivity) change of each country, and the main independent variables are x1= Chinese FDI stock, x2= technological difference, x3=human capital, and some control variable as x4=resource rent by an African country, x5=industries output / GDP, x6= economic openness.

First, I executed UNIT ROOT TEST to all my variables using the LLC test and found that x3 and x2 are non-stationary, both are the key independent variables ( I also run the HT test and get the same result). so I am asking how to conduct the COINTEGRATION test for this issue, I used the KAO test and use my y1 as the dependant variable and both x2 and x3 as the independent variable, the KAO test result shows that both variables are cointegrated with my y1 dependant variable.

So my question is, after the UNIT ROOT TEST, y1 is stationary and only x3 and x2 are not, so is it right to include y1 in my KAO test? otherwise how I am supposed to deal with my two non-stationary key independent variables?

Thank you very much.

[Fei Wang]

Dear Fei Wang,

I am conducting my research about CHINESE FDI IN AFRICA, my main purpose of the study is: Does Chinese FDI bring some technological spillover into African countries? thus my data consist of N=32 African countries in a period of time t=14 years. My 1st dependant variable is y1=TFP (Total Factor Productivity) change of each country, and the main independent variables are x1= Chinese FDI stock, x2= technological difference, x3=human capital, and some control variable as x4=resource rent by an African country, x5=industries output / GDP, x6= economic openness.

First, I executed UNIT ROOT TEST to all my variables using the LLC test and found that x3 and x2 are non-stationary, both are the key independent variables ( I also run the HT test and get the same result). so I am asking how to conduct the COINTEGRATION test for this issue, I used the KAO test and use my y1 as the dependant variable and both x2 and x3 as the independent variable, the KAO test result shows that both variables are cointegrated with my y1 dependant variable.

So my question is, after the UNIT ROOT TEST, y1 is stationary and only x3 and x2 are not, so is it right to include y1 in my KAO test? otherwise how I am supposed to deal with my two non-stationary key independent variables?

Thank you very much.

I would, without testing, directly control for time variables (e.g., time trends if time dummies are too strong for your sample size).

[RAKOTONDRAZAKA Tojo]

I would, without testing, directly control for time variables (e.g., time trends if time dummies are too strong for your sample size).

so you mean adding -i.year- in my regression comand? To get my result to follow my expected result, I better use the command: < xtreg y1 x1 x2 x3 x4 x5 x6 i.COUNTRY i.year, fe >

[Fei Wang]

so you mean adding -i.year- in my regression comand? To get my result to follow my expected result, I better use the command: < xtreg y1 x1 x2 x3 x4 x5 x6 i.COUNTRY i.year, fe >

You don't need to specify "i.COUNTRY" and "fe" simultaneously.

xtset COUNTRY
xtreg y1 x1 x2 x3 x4 x5 x6 i.year, fe vce(cluster COUNTRY)