[Kate Lussy]

Hello all,

I am having the same problem with "no observations." However, it only appears for some of my variables even though each variable is constructed in the same way.

My code is as follows:

tsset DSCode1 ymdate
gen F_Excess_USD_w=L.Excess_USD_w
drop if F_Excess_USD_w==.

* Dependent-sort portfolio - Size & IVOL
astile nq_Size_w = Size_w, nq(5)
bys nq_Size_w : astile IVOL_Size=IVOL_w , nq(5)
sort ymdate IVOL_Size DSCode
by ymdate IVOL_Size: drop if _n >1

* Value-weighted average excess returns of each portfolio in each month (ymdate)
bys ymdate: asgen ExUSD_1_VW_1_Size = (F_Excess_USD_w / (nq_Size_w ==1 & IVOL_Size ==1)), w(MV_USD_w)
bys ymdate: asgen ExUSD_1_VW_5_Size = (F_Excess_USD_w / (nq_Size_w ==1 & IVOL_Size ==5)), w(MV_USD_w)
bys ymdate: asgen EXUSD_1_VW_5_1_Size = ExUSD_1_VW_5 - ExUSD_1_VW_1

bys ymdate: asgen ExUSD_2_VW_1_Size = (F_Excess_USD_w / (nq_Size_w ==2 & IVOL_Size ==1)), w(MV_USD_w)
bys ymdate: asgen ExUSD_2_VW_5_Size = (F_Excess_USD_w / (nq_Size_w ==2 & IVOL_Size ==5)), w(MV_USD_w)
bys ymdate: asgen EXUSD_2_VW_5_1_Size = ExUSD_2_VW_5 - ExUSD_2_VW_1

bys ymdate: asgen ExUSD_3_VW_1_Size = (F_Excess_USD_w / (nq_Size_w ==3 & IVOL_Size ==1)), w(MV_USD_w)
bys ymdate: asgen ExUSD_3_VW_5_Size = (F_Excess_USD_w / (nq_Size_w ==3 & IVOL_Size ==5)), w(MV_USD_w)
bys ymdate: asgen EXUSD_3_VW_5_1_Size = ExUSD_3_VW_5 - ExUSD_3_VW_1

bys ymdate: asgen ExUSD_4_VW_1_Size = (F_Excess_USD_w / (nq_Size_w ==4 & IVOL_Size ==1)), w(MV_USD_w)
bys ymdate: asgen ExUSD_4_VW_5_Size = (F_Excess_USD_w / (nq_Size_w ==4 & IVOL_Size ==5)), w(MV_USD_w)
bys ymdate: asgen EXUSD_4_VW_5_1_Size = ExUSD_4_VW_5 - ExUSD_4_VW_1

bys ymdate: asgen ExUSD_5_VW_1_Size = (F_Excess_USD_w / (nq_Size_w ==5 & IVOL_Size ==1)), w(MV_USD_w)
bys ymdate: asgen ExUSD_5_VW_5_Size = (F_Excess_USD_w / (nq_Size_w ==5 & IVOL_Size ==5)), w(MV_USD_w)
bys ymdate: asgen EXUSD_5_VW_5_1_Size = ExUSD_5_VW_5 - ExUSD_5_VW_1

egen mean_Size = rowmean(EXUSD_1_VW_5_1_Size EXUSD_2_VW_5_1_Size EXUSD_3_VW_5_1_Size EXUSD_4_VW_5_1_Size EXUSD_5_VW_5_1_Size)

tsset DSCode1 ymdate
sort DSCode1 ymdate,

*Value-weighted average excess returns & t-statistics of each portfolio
newey EXUSD_1_VW_5_1_Size, lag(6) force
estimates table, star(.05 .01 .001)
newey EXUSD_2_VW_5_1_Size, lag(6) force
estimates table, star(.05 .01 .001)
newey EXUSD_3_VW_5_1_Size, lag(6) force
estimates table, star(.05 .01 .001)
newey EXUSD_4_VW_5_1_Size, lag(6) force
estimates table, star(.05 .01 .001)
newey EXUSD_5_VW_5_1_Size, lag(6) force
estimates table, star(.05 .01 .001)
newey mean_Size, lag(6) force
estimates table, star(.05 .01 .001)

For EXUSD_1_VW_5_1_Size and EXUSD_2_VW_5_1_Size and mean_Size, I get an output. But for the rest I do not. I have no missing observations and no string variables.

[Michelle Ribeiro]

I am also having the same trouble with my regression by using this command. regress LMS PDwages WAwages Ewages PDgender Egender WAgender PDrace Erace WArace
This is the outcome: no observations r(2000) but there are clearly 21 observations

However, with using Stata's help option, to carry out a regression it says to use this command -
regress depvar [indepvars] [if] [in] [weight] [, options]

The outcome of this is that one of my independent variables come up with an error - unknown weight type

I am relatively new to Stata but it is essential for my dissertation project and I am not too sure how to solve this issue

Also I would like to note that LMS values are missing as I am predicting the dependent variable

Any help would be amazing, thanks!

Michelle

[Rich Goldstein]

1. whenever any variable in a regression model has a missing value, that observation will be excluded;

2. please read the FAQ and present a data example using -dataex- and posting within CODE blocks

3. do you really have only 21 observations in total?

[Michelle Ribeiro]

Hi, many thanks for your response

1. Does that mean I won't be able to carry out a regression? I don't know if this is a stupid question but do I need values for dependent variables? Or could I do a regression without have dependent variable values?

2. I am unaware of what you mean by -dataex-

3. Yes, for my project I have 21 observations for each variable because it's from 1999 to 2019. So 21 observations for PDwages, 21 for Ewages and so on..

[Nick Cox]

Rich Goldstein is alluding to https://www.statalist.org/forums/help which all members are asked to read before posting. This is explained on the home page and on the prompt before posting. Specifically

https://www.statalist.org/forums/help#stata

explains about dataex.

To help us help you, you need to show a data example. Type this please in the Command window

dataex LMS PDwages WAwages Ewages PDgender Egender WAgender PDrace Erace WArace

and then copy and paste the results into an answer here. Do not please show a screenshot, which is almost useless. If Stata complains about dataex not being found then you are using an older version of Stata and you need to install dataex. The FAQ link above explains.

It's possible that there is a misunderstanding, say that

1. one or more of your variables are string

2. your wages, gender and race variables come in threes and it's possible that's wrong or problematic somehow but perhaps fixable

In general regression is impossible without data on a dependent variable. What you can do if you have a predictive formula is to calculate predicted values in exactly the same way as a formula y = 1 + 2x lets you calculate y if you know x. But that is not what regress does.

21 observations for any regression is a small sample and a very small sample for 9 predictors.

But most of this should be a matter between you and your dissertation advisor or supervisor. We can't know what the rules and expectations are at your institution. There's a general presumption here that if you're trying a specific technique then you have looked carefully at course materials or texts explaining it.

[Delali Tunyo]

Hello all,

I am having a similar problem with running a regression for total factor productivity using the "levpet" command. However, I keep getting "(r2000) no observations." for both balanced and unbalanced panel dataset.

tsset Firmid year

Panel variable: Firmid (unbalanced)
Time variable: year, 2009 to 2015, but with gaps
Delta: 1 unit

levpet lnyva,free( lnLab) proxy ( lninterm) capital ( lnCap) valueadded
no observations
r(2000);

count if !missing
missing not found
r(111);

[Nick Cox]

levpet is from the Stata Journal, as you are asked to explain (FAQ Advice #12).

I've never used it, but my guess is that the problem lies in your gaps, so that the command wants to use data for the previous year and can't find any.

[Dr. Iqra Yaseen]

hello all experts, the problem i face is that while using any xt command i get the same error message of no observations r 2000. my data has no missing values. probably the problem lies with panelid that shows only 1 frequency for each panel and as stata says there should be atleast a frequency of 4 to run any xtcommand .

this is my sample data set

* Example generated by -dataex-. To install: ssc install dataex
clear
input int year str9 iso3_d str11 minerals str33 iso3_o double STI_HF float(trade_minerals patent_app_d currency_exchange_d income_diff WTUI_IMF discap)
2015 "IND" "graphite"    "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "gallium"     "AUS" 47.7  52337692 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "zirconium"   "AUS" 47.7  53090864 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "phospherous" "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "chromium"    "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "rare_earth"  "AUS" 47.7  54472868 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "nickel"      "AUS" 47.7  27250628 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "anitomy"     "AUS" 47.7   2644362 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "manganese"   "AUS" 47.7  72888896 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "tungsten"    "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "germanium"   "AUS" 47.7  53090864 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "magnesium"   "AUS" 47.7  13408621 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "lithium"     "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "cobalt"      "AUS" 47.7     51265 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "copper"      "AUS" 47.7 431016448 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "boron"       "AUS" 47.7  38519056 45658 64.15195  42876.12 .0348236 10342
2015 "IND" "copper"      "CAN" 47.7 305151616 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "manganese"   "CAN" 47.7        85 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "rare_earth"  "CAN" 47.7   4287451 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "anitomy"     "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "zirconium"   "CAN" 47.7     97772 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "nickel"      "CAN" 47.7      7127 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "vanadium"    "CAN" 47.7        90 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "graphite"    "CAN" 47.7     38899 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "phospherous" "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "cobalt"      "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "germanium"   "CAN" 47.7     97862 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "gallium"     "CAN" 47.7     97862 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "boron"       "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "magnesium"   "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "lithium"     "CAN" 47.7  18213486 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "chromium"    "CAN" 47.7     10816 45658 64.15195  33882.56 .0348236 11357
2015 "IND" "gallium"     "DEU" 47.7   1151561 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "graphite"    "DEU" 47.7    565118 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "chromium"    "DEU" 47.7   1071401 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "lithium"     "DEU" 47.7     58287 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "tungsten"    "DEU" 47.7      3045 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "cobalt"      "DEU" 47.7     60664 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "anitomy"     "DEU" 47.7     88668 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "phospherous" "DEU" 47.7 532659.94 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "magnesium"   "DEU" 47.7    233148 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "vanadium"    "DEU" 47.7   1151561 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "manganese"   "DEU" 47.7     87526 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "nickel"      "DEU" 47.7      1363 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "boron"       "DEU" 47.7      8487 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "germanium"   "DEU" 47.7    777573 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "copper"      "DEU" 47.7 532659.94 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "zirconium"   "DEU" 47.7   1151561 45658 64.15195  33209.56 .0348236  5786
2015 "IND" "vanadium"    "EUU" 47.7      3776 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "copper"      "EUU" 47.7 1538.4706 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "rare_earth"  "EUU" 47.7         3 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "manganese"   "EUU" 47.7      3042 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "chromium"    "EUU" 47.7      3977 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "gallium"     "EUU" 47.7      3977 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "magnesium"   "EUU" 47.7      9049 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "phospherous" "EUU" 47.7 1538.4706 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "boron"       "EUU" 47.7        10 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "zirconium"   "EUU" 47.7      3776 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "graphite"    "EUU" 47.7       885 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "cobalt"      "EUU" 47.7      1987 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "nickel"      "EUU" 47.7      2232 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "germanium"   "EUU" 47.7      3855 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "tungsten"    "EUU" 47.7         3 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "lithium"     "EUU" 47.7      1209 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "anitomy"     "EUU" 47.7       996 45658 64.15195   23700.2 .0348236  6422
2015 "IND" "boron"       "FIN" 47.7       511 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "phospherous" "FIN" 47.7  59222.82 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "cobalt"      "FIN" 47.7   1006208 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "germanium"   "FIN" 47.7  59222.82 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "nickel"      "FIN" 47.7        69 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "chromium"    "FIN" 47.7  59222.82 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "magnesium"   "FIN" 47.7  59222.82 45658 64.15195 33760.773 .0348236  5222
2015 "IND" "copper"      "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "graphite"    "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "lithium"     "FRA" 47.7    286861 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "chromium"    "FRA" 47.7   1018187 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "germanium"   "FRA" 47.7   1155035 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "anitomy"     "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "phospherous" "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "rare_earth"  "FRA" 47.7      3509 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "magnesium"   "FRA" 47.7     18480 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "vanadium"    "FRA" 47.7   1018187 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "boron"       "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "nickel"      "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "zirconium"   "FRA" 47.7   1018187 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "manganese"   "FRA" 47.7    838169 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "cobalt"      "FRA" 47.7        35 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "gallium"     "FRA" 47.7   1155035 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "tungsten"    "FRA" 47.7  193813.4 45658 64.15195 29487.016 .0348236  6596
2015 "IND" "lithium"     "GBR" 47.7     12129 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "cobalt"      "GBR" 47.7     10245 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "zirconium"   "GBR" 47.7   1469164 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "chromium"    "GBR" 47.7   1458052 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "boron"       "GBR" 47.7    152645 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "manganese"   "GBR" 47.7      1926 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "vanadium"    "GBR" 47.7   1469164 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "copper"      "GBR" 47.7    152645 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "nickel"      "GBR" 47.7      1030 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "gallium"     "GBR" 47.7   1891575 45658 64.15195 36255.297 .0348236  6720
2015 "IND" "magnesium"   "GBR" 47.7    553360 45658 64.15195 36255.297 .0348236  6720
end
format %ty year

the panelid i set is by grouping year minerals and i so3_o together, as they uniquely identify my dataset.

please tell me how to overcome this "no observation" problem with xt xommands.

data dimensions: 9 countries studied over 2015-2023 (unbalanced)

[Carlo Lazzaro]

Sybil:
your excerpt presents two issues:
1) a single wave of data (2015) which is iconsistent with the panel data idea;
2) -repeated time values within panel-. Provided that you do not plan to use time-series operator, this one can be easily fixed just -xtset-ting your dataset with -panelid- only.

[Dr. Iqra Yaseen]

thanks Carlo Lazzaro for your prompt response.

1. No, the data doesn't contain a single wave of data. the example was generated using dataex. the data actually ranges from 2015 to 2023.
2. yes, i tried xtset-ting the data with panelid only, but while using

xtcdf

, the error i get is

time variable not set, use tsset varname ...

3. after xtset-ting with panelid only i still get the error(2001) of no observations, checking with xtreg command.
4. following are the data dimensions, in case you need them

 sum STI_HF ln_trade_minerals ln_patent_app_d ln_currency_exchange_d ln_income_diff ln_WTUI_IMF ln_discap

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      STI_HF |      1,836    37.78889     15.6415       11.7       50.6
ln_trade_m~s |      1,836    12.62474    3.652864  -.7015861   22.18631
ln_patent_~d |      1,836    10.84124    .0976205   10.71568   11.02798
ln_currenc~d |      1,836    4.249944     .062126   4.161254   4.364429
ln_income_~f |      1,836    10.39513    .2195758   10.00067   11.06916
-------------+---------------------------------------------------------
 ln_WTUI_IMF |      1,836   -3.123558    .4954082  -3.844612  -2.193632
   ln_discap |      1,836    8.857413    .2861847   8.453614   9.397981

desc STI_HF ln_trade_minerals ln_patent_app_d ln_currency_exchange_d ln_income_diff ln_WTUI_IMF ln_discap

              storage   display    value
variable name   type    format     label      variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
STI_HF          double  %10.0g                overall index (score)
ln_trade_mine~s float   %9.0g                 
ln_patent_app_d float   %9.0g                 
ln_currency_e~d float   %9.0g                 
ln_income_diff  float   %9.0g                 
ln_WTUI_IMF     float   %9.0g                 
ln_discap       float   %9.0g

[Carlo Lazzaro]

Sybil:
1) -xtcdf- needs a -timevar- to work properly, but you have a -repeated time values within panel- issue, that makes -xtset-ting your dataset with a -timevar- unfeasible;
2) in addition, you're using a community-contributed module for panel data regression that is conceived for T>N array (just take a look at -xtline1.dta- dataset, that comes as a companion of -xtcdf- helpfile). However, your panel dataset seems to be a N>T one, that requires -xtreg-, if your dependent variable is continuous;
3) my guess is that your problems are related to your -panelid-, that seems to group "year minerals and i so3_o together" as per your description;
4) I do not know whether you used the -fe- or the -re- specification with -xtreg-;
5) eventually, if you plan to start with -xtreg,fe- (as I would do), please note that -ln_currency_exchange_d- and -ln_income_diff- show a limited variation, and this may have a bearing on the -fe- estimator machinery (that requires a remarkable within panel variation in time-variant variables to work as its best).

[Dr. Iqra Yaseen]

Thanks Carlo Lazzaro for your time and effort

at last, may you please suggest me some commands to estimate cross section dependency for N > T.

[Carlo Lazzaro]

Iqra/Sybil (I guess i'm replying to the the very same person):
see the community-contributed module -xttest2- (-fe- specification only, though), but it too requires -timevar-.
Therefore, you have two options:
1) forget cross sectional correlation;
2) change the -timevar- with a more precise date format (say, d/m/year) if feasible and -xtset- your dataset accordingly.

[Dr. Iqra Yaseen]

Carlo Lazzaro i just changed my pen name to my official name to fit with community guidelines.

both of the alternatives seems not feasible to me at this moment of time.

anyways witl try with xttest2 command.

thanks a lot.

stay blessed

[Carlo Lazzaro]

Iqra:
usually, in a N>T pamel dataset is the within panel autocorrelation of the epsilon that may be of some relevance (you can easily address this issue in -xtreg- via the options -robust- or -vce(cluster panelid)- that, unlike with -regress-, do the very same job, as they both return cluster-robust standard errors).