What should the command be?

[Peter Arts]

I would like to ask about the exact command.
Assume yt= (infla t, unrate t, hwages t）is(3×1)-vector, in order to estimate the order of the VAR(p) model for yt, using a constant for the determinstic component, what should the command be?
I suppose it is varsoc command.
The data is like this (until 2021).

date	infla	unrate	hwages
1961-01-01	1.600272387	6.6	1.401869159
1961-02-01	1.462087725	6.9	1.401869159
1961-03-01	1.462087725	6.9	1.401869159
1961-04-01	0.914014895	7.0	1.869158879
1961-05-01	0.913087589	7.1	2.803738318
1961-06-01	0.776764607	6.9	2.803738318
1961-07-01	1.252115059	7.0	2.803738318
1961-08-01	1.114488349	6.6	2.34741784
1961-09-01	1.249577845	6.7	2.325581395
1961-10-01	0.773109244	6.5	3.255813953
1961-11-01	0.671591672	6.1	3.720930233
1961-12-01	0.6709158	6.0	4.147465438

[Jared Greathouse]

Please read section 12 of the FAQ to re-ask this question.

[Peter Arts]

OK, I am using the StataSE-17.

I typed this.

.varsoc infla unrate hwages
no observations
r(2000);

Below is more details.

. tsset date
Time variable: date, 01jan1961 to 01jan2022, but with gaps
        Delta: 1 day

* Example generated by -dataex-. For more info, type help dataex
clear
input int date double(infla unrate hwages)
 366 1.6002723867895696 6.6 1.4018691588784993
 397 1.4620877252641273 6.9 1.4018691588784993
 425 1.4620877252641273 6.9 1.4018691588784993
 456  .9140148950576688   7  1.869158878504673
 486  .9130875887725098 7.1 2.8037383177570208
 517  .7767646065522271 6.9 2.8037383177570208
 547 1.2521150592210928   7 2.8037383177570208
 578 1.1144883485306067 6.6 2.3474178403755985
 609  1.249577845322758 6.7 2.3255813953488413
 639  .7731092436978892 6.5 3.2558139534883956
 670  .6715916722634718 6.1 3.7209302325581506
 700  .6709158000673066   6  4.147465437788012
 731  .6702412868626828 5.8  4.608294930875578
 762  .9048257372647361 5.5  4.147465437788012
 790 1.1058981233240894 5.6  4.147465437788012
 821 1.3418316001337915 5.6  4.128440366972463
 851  1.340482573726165 5.5 3.1818181818181746
 882 1.2399463806964661 5.5 3.1818181818181746
 912 1.0026737967917532 5.4 3.1818181818181746
 943 1.1356045424182115 5.7 3.2110091743119185
 974 1.4676450967312737 5.6 3.1818181818181746
1004 1.3342228152097624 5.4  2.702702702702675
1035 1.3342228152097624 5.7  3.139013452914785
1065 1.2329223592130178 5.5  2.212389380530988
1096 1.3315579227700658 5.7 1.7621145374449254
1127 1.2288276320161717 5.9  2.212389380530988
1155 1.1269472986410678 5.7 2.6548672566371723
1186  .8937437934460002 5.7  2.643171806167399
1216  .8928571428572729 5.9  2.643171806167399
1247 1.3240648791795095 5.6  3.083700440528636
1277 1.5552614162801515 5.6  3.083700440528636
1308 1.5521796565392876 5.4 2.6666666666666616
1339  .9861932938851181 5.5  3.524229074889873
1369 1.2179065174459103 5.5  3.070175438596512
1400 1.3166556945362728 5.7   3.04347826086957
1430 1.6458196181703633 5.5  3.463203463203457
1461 1.6425755584754231 5.6  3.463203463203457
1492 1.4107611548554555 5.4 3.0303030303030276
1521  1.409373975745476 5.4  2.586206896551735
1552 1.5419947506564835 5.3 3.0042918454935563
1582 1.5404785316292857 5.1 3.0042918454935563
1613 1.3067624959159918 5.2  2.991452991453003
1643 1.0752688172047886 4.9  2.564102564102577
1674  .9756097560978505   5 3.8961038961038863
1705 1.1718749999999112 5.1 3.8297872340425476
1735  1.203252032520541 5.1 1.7021276595744705
1766  1.397011046133656 4.8 2.5316455696202445
1796 1.1981865284968585   5 2.5104602510460206
1827 1.0989010989011172 4.9 2.9288702928870203
1858 1.1970236169526638 5.1 3.3613445378151363
1886 1.1958629605690607 4.7  4.201680672268915
1917  1.389337641356847 4.8 2.9166666666666785
1947 1.6139444803096037 4.6  3.750000000000009
1978  1.934859722669935 4.6  3.319502074688807
2008   1.80528691166999 4.4 3.3333333333333437
2039 1.6103059581317858 4.4 2.9166666666666785
2070 1.7374517374519893 4.3   2.86885245901638
2100 1.7030848329047998 4.2  5.439330543933041
2131 1.7302146747839586 4.1  4.115226337448541
2161   1.92000000000061   4 3.6734693877551017
2192 1.9181585677747748   4  3.658536585365857
2223  2.557544757033292 3.8  4.065040650406515
2251 2.7786649632704608 3.8 3.2258064516129004
2282 2.8680688336521376 3.8  4.453441295546545
2312 2.7636594663277725 3.9  4.016064257028096
2343 2.4359379943062143 3.8 3.6144578313253017
2373   2.75490816972761 3.8  4.435483870967727
2404 3.4865293185423196 3.8  4.453441295546545
2435  3.573687539531689 3.7  4.382470119521931
2465  3.791469194312458 3.7  4.761904761904767
2496  3.559055118109966 3.6  4.743083003952564
2526  3.359497645211307 3.8 4.7244094488189115
2557 3.1994981179423787 3.9  4.313725490196085
2588  2.867830423940143 3.8           4.296875
2616 2.5481665630825656 3.8           4.296875
2647 2.5402726146219523 3.8  3.875968992248069
2677 2.3183925811437245 3.8 3.8610038610038755
2708 2.8412600370599117 3.9  4.651162790697683
2738 2.9275808936827463 3.8  4.247104247104261
2769  2.603369065849259 3.8  4.651162790697683
2800 2.5954198473283174 3.8  4.198473282442738
2830 2.5875190258752623   4  3.409090909090895
2861  3.102189781021991 3.9 3.7735849056603765
2891   3.28068043742451 3.8 4.8872180451127845
2922 3.6474164133742715 3.7  6.390977443609014
2953 3.6363636363640373 3.8  5.617977528089879
2982 3.9393939393937316 3.7  6.367041198501866
3013  3.927492447129688 3.5  6.343283582089554
3043  4.229607250755163 3.5  7.063197026022294
3074  4.204204204204065 3.7  6.666666666666665
3104  4.491017964071808 3.7  6.666666666666665
3135  4.477611940298498 3.5  6.296296296296289
3166  4.464285714285698 3.4  6.959706959706957
3196  4.747774480712219 3.4  7.692307692307687
3227  4.424778761061909 3.4  7.636363636363641
3257  4.705882352941226 3.4  7.168458781362008
3288   4.69208211143699 3.4 6.0070671378091856
3319  4.678362573098771 3.4  6.382978723404253
3347  5.247813411078961 3.4   5.98591549295775
3378  5.523255813953809 3.4  6.315789473684208
end
format %td date

------------------ copy up to and including the previous line ------------------

Listed 100 out of 734 observations
Use the count() option to list more


Additionally, I would like to estimate the VAR(p) model fo yt using varbasic command.
I typed this.

. varbasic infla unrate hwages
no observations
r(2000);

[Jared Greathouse]

I don't understand what the problem is. Thanks for giving your data, but I still have no idea what the issue is. Like, simply explain to me, what's your research question, what variables do you want to use to estimate the model, what technique do you want to use to do so and what issue you're having exactly.

Not just for me, as I'm sure others don't know what the problem is either. Please, ask the question as if someone is asking you the question. Notice how here I explain what I want specifically, and give the context such that anyone with knowledge of the problem can assist me in solving it.


Anyways, based off the error code, presumably there's no set of observables which can be used in the model in the first place. You don't go into detail about your model or even if this is a user written command, but I suspect the issue is there. Your time series has gaps in it- perhaps this is where the issue lies, while it's quite unlikely.

[Nick Cox]

My answer arises directly from #1. You have monthly data that happen to be given as daily dates, one for the first of each month.

I imagine that you tsset or xtset your data in terms in that daily date variable -- but given that, Stata can't find previous values. For example, the date before 1 February is 31 January, the date before that is 30 January, and so on, but you don't have data for such dates. Given what you told,Stata, Stata sees mostly gaps in your data.

You need to back up and create a monthly date variable

gen mdate = mofd(date) 

format mdate %tm

and reissue your tsset or xtset. Then there is a stronger chance that your VAR modelling will work.

[Peter Arts]

Thanks for your comments.
More precisely, using a monthly time series of the growth of the hourly earnings of Manufacturing (hwages t), inflation rate (infla t) and unemployment rate (unrate t) for the United States, whose data is obtained from the FRB of St. Louis database, seasonally adjusted, monthly frequency, from 1961 to 2022, and let yt= (infla t, unrate t, hwages t）be an (3×1)-vector that collects the information concerning the variables of interest, I try to estimate the order of of the VAR(p) model for yt with a constant for the deterministic component.