Cutting out the appropriate end of the variables

[Shazzle Singh]

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(quarter Dlprod) double lrea float Dlop double inf float(Dlrgdp Dlexc)
 80             .   39.13813266666667            .  7.74278215223094            .            .
 81   -.035614014  60.410011999999995 -.0036604404  10.2464332036316   -.04316902 -.0008628368
 82    -.01580906   51.08060033333334   -.08054447   12.291933418694  -.028956413  -.020701766
 83    -.03905678           62.466026    .17828774  13.1147540983606    .08399487    .03753448
 84     .02488899   58.54461133333333   -.04705215  14.7381242387332   -.01137352    .06241119
 85   -.021674156  40.808108000000004   -.10607576                14  -.022613525    .06047547
 86    -.04679394   20.17195633333333   -.05337715  13.5689851767389  -.015737534    .06826341
 87    .001235962  12.483683466666667    .05288005  12.3745819397994    .05860233   -.04127169
 88   -.015400887  -.2111375666666667   -.08884788  11.8895966029724   -.01995945   .019032836
 89   -.028642654   .5451751000000004    .04567003  11.0423116615067  -.025244713    .02091241
 90    .020044327  -37.34087466666667  -.009387732  10.8433734939759  -.009765625    .08882523
 91     .02977276  -23.72058266666667   -.00683546  11.6071428571428    .06259918   .073213935
 92   -.070046425 -13.459465066666667    -.0800426  9.77229601518024  -.009399414  -.005073905
 93     .02050209 -12.350081633333332   .010914087   9.1078066914498   .007008553  .0083755255
 94     .04874039 -33.336627666666665    .02732563  7.78985507246374  -.017912865   .032039404
 95   .0018424988  -27.24211366666667   -.04581285  7.28888888888891   .065146446   .013346195
 96   -.002747536          -23.977575   .010084867  6.30942091616251    .00942421    .02453232
 97    .005511284          -18.353044   .002732754  6.47359454855195  -.020303726    .00676179
 98   -.017630577          -27.757518   -.04061341  6.13445378151261    .02085972    .07764363
 99    .002876282  -20.14504066666667  -.023606777  5.96520298260146    .04980183    .05618048
100   -.003787041          -25.616022 -.0044965744  5.69105691056911   .011543274    .05679584
101   -.023112297 -26.788110666666668  -.015342474  5.75999999999999  -.036082268   -.05182862
102    .002131462          -52.681589   .005304575  5.77988915281077    .01897907   -.06399655
103     .06078339  -36.48232166666667   .035532236  5.62939796716187    .06870365   -.06899428
104   -.012742996  -48.39284266666667     -.492239  5.76923076923076   -.02629471   -.06177926
105    .014677048  -65.86139466666667    -.3213961  6.05143721633889  -.005705833    .01131153
106     .02001858  -71.29853133333333            0  8.00898203592812 -.0007314682 -.0021862984
107    -.02536392          -53.494223     .1625726  8.80829015544041    .07444286   .011133432
108   -.019616127 -41.291238666666665    .19018364  9.96363636363637   -.04241371   -.05986404
109    .006961823          -25.291703    .03786588  9.70042796005709   .003426552   -.04718626
110     .05217457          -25.120687   .029469013  7.90020790020794   -.03904152  .0028271675
111  -.0039129257 -11.800554066666665  -.064515114  7.41496598639454    .09070873   -.03897548
112    -.01266098  11.860477533333333   -.11970901  7.07671957671957    -.0380516  -.016815186
113    .004841805  3.7127351333333336    .02114606  7.02210663198959   -.03488922  -.017273188
114    .017139435  -8.790290666666666   -.13296938  6.61528580603723  -.009062767    .08864713
115     .04496288   5.500527566666666  -.069060326  6.01646611779607    .06791592   -.03344822
116    -.04605198            15.59104    .25841665  4.69425571340333   -.05028057   .016834259
117    .008478165  13.602316466666666    .08156276  4.67800729040098    .02255535     .0415448
118     .01911831  2.1667307900000004   -.05910301  4.51807228915663  -.024207115   .004276037
119     .02155781  14.136153666666667   .072547674  4.30107526881723    .07222748  -.027472496
120  -.0012311935  13.700408666666668    .03893995  4.36578171091446   -.02434349   -.04696953
121   -.003965378  -6.543706466666666   -.20725727  3.83052814857803  -.025891304  -.006798387
122    -.03584957 -20.250679666666667     .4835284  3.80403458213255  -.011367798   -.05305231
123     .02464962  -8.576893003333334     .1891837    4.524627720504     .0831728   -.04859436
124    .000743866   9.022044406666666    -.4457831  3.90050876201245    -.0319252   .018657684
125   -.023163795  13.382336333333333   -.07966495  3.80100614868644   .013855934    .12299943
126    .015841484   5.221383433333333    .05793595  3.49805663520268  -.028817177   .008721352
127    .011842728           8.4833935   .022568226  2.57534246575342    .06528664  -.063886166
128   -.005056381          -9.1815819    -.1264615  2.33949945593033    .00262928  -.005768895
129    -.01706028 -19.907728666666667     .1171906   2.4232633279483  -.030332565  -.006651998
130    .005515099  -32.55594233333333   .015793324  2.30686695278971   .008387566   -.08576798
131    .014642715 -20.428037666666665   -.05341005  2.24358974358973    .05217743    .09891403
132  -.0008001328  -8.793030700000001   -.05233955  2.60499734183944   -.03020191    .08356285
133   -.017170906  4.2965170666666666 .00018525124  2.41850683491062  -.012756348   -.01551175
134    .006093025  -6.222860533333333      -.10499  2.14997378080752   .016050339    .05739963
135    .009690285 -13.556194533333333   -.07940865  1.98537095088822     .0934496    .01007378
136     .00741005 -23.626597333333333   -.07587433   1.2435233160622   -.04366684   .015948415
137  -.0029649734         -8.37051217    .15959454  .975359342915802    .00847435  -.031848192
138   .0015182495 -1.6039786999999996    .04682732  1.54004106776183   -.02486992   -.05193007
139    .017782211  23.068966333333332  -.026546717  1.74180327868853    .09761047  -.016186953
140  -.0010061264  31.917483333333337   .036661625  2.66120777891503   -.02917862  -.032115936
141    .003125191   36.41205033333333    .05387354  2.69445856634468  -.031635284   -.04464781
142     .00968361  29.452066666666667    -.1004212   2.3255813953488    .02602291   .010604978
143    .003537178   9.859649666666668   .032122374  2.16515609264855    .07105446 -.0038758516
144    .007936478 -3.9989844666666663     .0757897  .897308075772663  -.011328697    .02155018
145 -.00010108948  -9.716630499999999    .06210089  .990099009900991  -.030052185   .018499613
146     .00338459  -34.44321300000001     .0570159  1.38339920948619   .029580116   -.01876986
147    .016378403 -12.624126133333334    .11149788  1.77427304090683    .04030895   .003127813
148    .010835648          -4.8125161   -.08976126  3.06324110671939  -.017730713     .0299412
149   -.000919342 -15.160078333333333   -.13125277  2.69607843137256    .03354168    .06529093
150    .006411552          -15.143239    .00897336  2.29044834307992  -.025523186    .05278087
151    .013442993 -18.731099999999998   .009778738  2.22760290556901     .0763483   -.04578638
152    .018574715  -35.81994366666667   -.28532577  2.15723873441995   -.01279831    .05636358
153   -.007968903  -46.57734100000001  -.064397335   2.2434367541766   -.02697754  -.003437281
154   -.016814232           -59.66092  -.021053314  2.28680323963794   -.02005291    .01630497
155    .005273819  -49.17695533333333   -.09287786  2.32117479867363    .06525898  -.019602537
156    .007332802  -56.07041733333333  -.017594099  2.25246363209761   -.02033615   .022954226
157   -.030688286 -42.895834666666666     .3197281  2.47432306255837  -.023492813   .017477036
158    .010526657  -36.91814933333333     .2431948  2.04937121564973   .013630867   .005753994
159    .003252983          -13.267707    .15230894  2.68518518518515    .07452679   .006227255
160     .01356125  -5.286501533333333    .11157179  2.89123451124368 -.0006017685    .04070139
161     .01726532   4.948936066666667   .005619764  2.91571753986331    -.0603199    .06567001
162    .017752647   6.775513666666666    .11014366  3.42309447740759   .008555412   .019916296
163     .00973606   8.435973533333334  -.007164478  3.11091073038775    .06538296   .032980442
164   -.011986732  -2.792858986666667   -.12935114  3.52363960749332  -.005785942   -.04024959
165    -.02381325          -9.4281721   .024876595  3.93979637007527   -.04561424   .032016277
166    .009342194  -40.64816866666667   -.05828905   2.5595763459841   .010370255  -.020018816
167   -.003803253 -57.250793666666674   -.26657724   2.0113686051596    .06894302  -.010724306
168   -.013516426  -45.27946333333333     .0800693   1.0340370529944   -.04633999  .0009560585
169   -.004775047 -40.545537333333336     .1861112  .468483816013621    .02115917   -.08499837
170    .008533478  -38.74795833333334   .066512346  1.41996557659209   -.03976345   -.08406281
171    .018037796          -4.0900479  -.007452488  2.22888984140592    .07687569   -.02767825
172    .008569717          10.6210139     .1587529  4.60554371002131   -.02139473   -.03637564
173   -.006990433   32.22018033333333   -.16814756  2.24671470962272   -.04574585  -.006904006
174   .0085430145   39.52709866666667     .0691607  1.90920661858297   .011060715    .04578185
175    .029878616  102.57146666666665    .03383112  1.21593291404613   .064902306     -.058622
176    .009285927           121.67917    .09015703 -1.42682429677945   .014725685  -.001775503
177    .002621651           83.438888    .10330415  .829187396351576   -.03930473  -.006779671
178    .013513565           94.319298    .12952352  1.20732722731057  -.018507004 .00048577785
179    .005133629  117.48598333333335    .05228329  1.24275062137531    .08005524    -.0803665
end
format %tq quarter

Hi guys, I'm a little confused with the estimation of the var model. Basically, I did the ADF, DF-GLS and Phillips-Perron tests to check for stationarity of the variables and found only two variables in my var model to be stationary (i.e. lrea - log of real economic activity index & inf - inflation rate (%)).The rest of the variables are first differenced in order to induce stationarity. However, in doing so, expectedly, I have different number of observations for my my differenced variables and the ones that are non-differenced. How do remove the last observation for the undifferenced variables (lrea and inf)

summarize Dlprod lrea Dlop Dlrgdp inf Dlexc

[William Lisowski]

It's not clear to me that you want to remove the last observation for the undifferenced variables.

Look at your first observation - that's where you have two lrea and inf with the differenced ones are missing.

You don't tell us why you want to remove the observation. In general it's not necessary - Stata does "casewise omission" and will omit any observation from estimation where one or more of the variables is mssing.

Because summarize is not an estimation command it computes the summary statistics separately for each variable. Below I show two summary commands - the one you used, and the one that includes only complete observations.

. summarize Dlprod lrea Dlop Dlrgdp inf Dlexc

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      Dlprod |         99    .0016682    .0205442  -.0700464   .0607834
        lrea |        100   -3.904499    37.65133  -71.29853   121.6792
        Dlop |         99    .0018497    .1366445   -.492239   .4835284
      Dlrgdp |         99    .0072069    .0422466  -.0603199   .0976105
         inf |        100    4.729691    3.582737  -1.426824   14.73812
-------------+---------------------------------------------------------
       Dlexc |         99    .0024959    .0447392   -.085768   .1229994

. summarize Dlprod lrea Dlop Dlrgdp inf Dlexc if Dlprod!=.

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      Dlprod |         99    .0016682    .0205442  -.0700464   .0607834
        lrea |         99   -4.339273    37.58979  -71.29853   121.6792
        Dlop |         99    .0018497    .1366445   -.492239   .4835284
      Dlrgdp |         99    .0072069    .0422466  -.0603199   .0976105
         inf |         99    4.699256    3.587954  -1.426824   14.73812
-------------+---------------------------------------------------------
       Dlexc |         99    .0024959    .0447392   -.085768   .1229994

[Shazzle Singh]

Thanks William, makes sense, I was just worried it might cause problems with my estimation.