Regression doesnt show all regression output

[Adam Klaas]

* Example generated by -dataex-. For more info, type help dataex
clear
input float(log_realHP log_PopulationDensity log_Population logReal_income) double Unemployment_rate float(real_consCost_p real_interest)
11.066638  8.778634  12.99987         .                  .    .          .
11.448387  8.781555 12.998393         .                  .    .          .
11.449027  8.614501 13.047265         .                  .    .          .
11.436362  8.632306 13.058484         .                  .    .          .
11.425336   8.64365 13.064938         .                  .    .          .
 11.88968  8.650149  13.07239         .                  .    .          .
11.878828  8.660427 13.068838         .                  .    .          .
  11.9539  8.656781 13.072503         . 1.1709528024032914    .          .
12.027326  8.660601 13.085417         . 1.0189597064731957    .          .
12.061186  8.680162 13.099203         .   1.65181669133134    .          .
12.058692  8.693999  13.11248         . 1.7168838356396807    .          .
 12.03159  8.707152 13.126465 10.228337 1.8364521815338959    .          .
11.994463  8.721113 13.134007  10.21136 2.3129111842105266    .          .
11.930356  8.728911 13.140085 10.195252 2.5779070224387945    .          .
  11.8488  8.734882 13.151652 10.225975 2.5307801523129543    .          .
11.817344  8.746557  13.16156 10.213326 2.3942860219851227    .          .
11.832868  8.746398  13.17093 10.241876 2.2004945873548722    .          .
11.876635  8.755737 13.185452 10.274782 1.9359284664104206    .          .
11.936114   8.77323 13.195303 10.283296 1.5915721050958196    .          .
12.051608   8.78309 13.210077  10.30509  1.363041781627516    .          .
12.139122  8.797851 13.214614         .                  .    .          .
11.373663  7.325149 11.778953         .                  .    .          .
  11.8328  7.336286  11.78826         .                  .    .          .
11.792617  7.345365 11.794346         .                  .    .          .
11.767217   7.35628  11.80194         .                  .    .          .
11.753452  7.363914  11.80543         .                  .    .          .
12.143667  7.367077  11.81093         .                  .    .          .
 12.12681  7.374629  11.81782         .                  .    .          .
12.205215  7.381502  11.82394         . 1.3994621962031346    .          .
 12.26741  7.387709 11.833377         . 1.2629286880783888    .          .
12.281482   7.40062 11.846586         . 1.9913041609661408    .          .
12.280895   7.41397 11.854997         . 1.9178043271348002    .          .
12.225747  7.424762 11.862828 10.303476 1.9098898465745313    .          .
12.191173  7.426549  11.86932 10.286084   2.37366700042712    .          .
12.096637  7.433667 11.875712  10.26936 2.7690231192558423    .          .
 12.01052  7.440146  11.87875 10.301238 2.6149684400360687    .          .
11.996036  7.218177 11.929053 10.293784 2.3745448788982113    .          .
12.025772  7.224025 11.934336 10.326927 2.2898609680403648    .          .
12.065701  7.229114 11.941026  10.35463  2.007377765032522    .          .
12.124705  7.238497 11.946038 10.368253 1.7314613663629586    .          .
 12.19029  7.245655 11.951897 10.389135 1.5859610214620499    .          .
12.223978  7.251345 11.954337         .                  .    .          .
11.289782 4.4886365 10.134916         .                  .  1.2      3.005
11.818513 4.5108595  10.14847         .                  .  1.1   .8583333
  11.7855 4.5217886 10.138758         .                  .   .6       1.59
11.753032 4.5108595 10.135313         .                  .    0      1.995
11.739367 4.5108595 10.139706         .                  .   .6      2.795
12.134277 4.5108595 10.146708         .                  .   .3  1.6741667
 12.11742 4.5217886   10.1489         .                  .  1.8   2.680833
12.226357 4.5217886  10.15027         .               1.33  2.4   2.686667
 12.26741 4.5217886 10.153273         .               1.05  2.1  1.7266667
12.273849 4.5325994   10.1489         . 1.6038806086922506   -1   2.486667
12.269553 4.5217886 10.157548         . 1.6676362226100447  -.7  1.6916667
12.249003 4.5325994 10.155724  10.33514 1.7095345403683269  -.4   .6891667
12.199078 4.5325994  10.14804 10.327867 2.3100113542930973  -.7 -.56583333
 12.11769 4.5217886  10.14081 10.308275 2.4056473557597506 -2.3  -.5391667
12.024005 4.5217886 10.134718 10.347644 2.3806689679800024  -.1       .455
11.982306 4.5108595 10.136304 10.330508 2.2976666798716474  1.3  .09083333
11.976323 4.5108595 10.138006 10.366885 2.2146642410820214  1.7     -.0075
 12.01838 4.5217886  10.14211 10.391165 2.0086648286727056   .9     -.8775
 12.01325 4.5217886 10.141953 10.399435  1.615063420783109   .8 -1.1233333
12.068002 4.5217886 10.144275 10.417065  1.218314010611122   .2      -2.67
12.101523 4.5217886 10.142465         .                  .   .9 -1.6766667
11.552146  6.891626 10.028445         .                  .    .          .
12.104395  6.900731  10.03034         .                  .    .          .
12.069604   6.96319 10.036225         .                  .    .          .
12.047832  7.008505 10.039547         .                  .    .          .
12.032354  7.011214 10.056037         .                  .    .          .
12.387163  7.028202 10.091832         .                  .    .          .
12.373962   7.06732  10.12739         .                  .    .          .
  12.3918  7.103322  10.18059         .  .7958766012279239    .          .
12.434464  7.156177 10.240174         .  .6784260515603799    .          .
12.436175  7.220374  10.28148         . 1.1649021824785006    .          .
12.440403  7.261927 10.315233         . 1.1593626817715061    .          .
12.424276  7.297768 10.321013 10.482217 1.2185482808589119    .          .
 12.37217  7.303843 10.329344  10.48165 1.4044026389705402    .          .
12.310374  7.313887 10.333938 10.450358 2.0481810201892126    .          .
 12.23276   7.31854 10.344223  10.50418 2.0915789812401457    .          .
12.226426  7.329094  10.35134  10.46103 1.9489440557206301    .          .
12.241873  7.349231 10.353703 10.506447  1.880597966404233    .          .
 12.27701    7.3518  10.35771 10.546596  1.492110860662243    .          .
12.296556   7.35628 10.364955  10.54604 1.2607160867372669    .          .
12.403312   7.36328 10.369075  10.57182 1.1927555792711635    .          .
12.442365  7.367709  10.37321         .                  .    .          .
11.127263  5.420535  9.840175         .                  .    .          .
11.796694   5.42495  9.842197         .                  .    .          .
11.756512   5.42495  9.844533         .                  .    .          .
11.724045   5.42495  9.852089         .                  .    .          .
11.710588  5.433722  9.848767         .                  .    .          .
 12.07601  5.648974 10.221068         .                  .    .          .
12.059152  5.652489 10.224483         .                  .    .          .
12.111186  5.652489  10.22441         . 1.0519442832269297    .          .
12.145667  5.652489 10.220012         .  .9472802127737094    .          .
 12.14305  5.648974  10.22194         . 1.6363636363636365    .          .
12.139817  5.652489  10.21972         . 1.4213345967418638    .          .
 12.10502  5.648974 10.214825 10.279052  1.501556491485076    .          .
 12.04164  5.645447 10.206625 10.261792  1.957019422494646    .          .
11.984159  5.638355 10.204074 10.239828  2.443268056121127    .          .
11.880217  5.634789  10.20003  10.27592 2.4531668153434434    .          .
11.833517  5.631212 10.200328 10.250465 1.9693816884661117    .          .
end

-----------

xtreg log_realHP log_PopulationDensity log_Population logReal_income Unemployment_ratereal_consCost_p real_interest,fe vce(robust)

I have a panel data set consisting of cities for the years 2000-2020, I have run the xtreg however if i get insufficient observations. when include my dummy i do get results but only coefficients nothing else. Furthermore, the coefficient of log real income is not correct its -1.134 which means 1 % increase in income means house prices decreases by -1.134. As I have read other papers they all conclude that income has a positive effect on house prices( higher income allows to buy more expensive houses)

[Carlo Lazzaro]

Adam:
despite you did not provide details that allow to -xtset- your -dataex- excerpt, the huge number of missing values can actually play a role in reducing the sample to an extent that makes the regression unfeasible.

[Adam Klaas]

Adam:
despite you did not provide details that allow to -xtset- your -dataex- excerpt, the huge number of missing values can actually play a role in reducing the sample to an extent that makes the regression unfeasible.

Hello Sir Lazzaro, I was trying to put the xtreg results here but it doesnt allow me if i use this command:

. xtreg HousePrice log_PopulationDensity log_Population Unemployment_rate real_consCost_p
>  logReal_income real_interest if low_devA==1 ,fe vce(robust)

Fixed-effects (within) regression               Number of obs     =          9
Group variable: GM_code                         Number of groups  =          1

R-sq:                                           Obs per group:
     within  = 0.9773                                         min =          9
     between =      .                                         avg =        9.0
     overall = 0.9773                                         max =          9

                                                F(0,0)            =          .
corr(u_i, Xb)  =      .                         Prob > F          =          .

                                         (Std. Err. adjusted for 1 clusters in GM_code)
---------------------------------------------------------------------------------------
                      |               Robust
           HousePrice |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
log_PopulationDensity |   832636.7          .        .       .            .           .
       log_Population |   963523.6          .        .       .            .           .
    Unemployment_rate |  -11603.33          .        .       .            .           .
      real_consCost_p |  -1026.872          .        .       .            .           .
       logReal_income |  -192915.4          .        .       .            .           .
        real_interest |  -3932.453          .        .       .            .           .
                _cons |  -1.13e+07          .        .       .            .           .
----------------------+----------------------------------------------------------------
              sigma_u |          .
              sigma_e |  4827.9681
                  rho |          .   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

This is an example of all the variables in my model

* Example generated by -dataex-. For more info, type help dataex
clear
input float GM_code str29 GM_naam str6 RegioS int Year double(Unemployment_rate Built_area Agri_area NaturalForest_area CPI_index) float(log_PopulationDensity log_Population log_realHP logReal_income real_consCost)
1 "'s-Gravenhage"    "GM0518" 2000                  .    .    .    .                100  8.778634  12.99987 11.066638         .         .
1 "'s-Gravenhage"    "GM0518" 2001                  .    .    .    .              102.4  8.781555 12.998393 11.448387         .         .
1 "'s-Gravenhage"    "GM0518" 2002                  .    .    .    .           106.5984  8.614501 13.047265 11.449027         .         .
1 "'s-Gravenhage"    "GM0518" 2003                  .    .    .    . 110.11614719999999  8.632306 13.058484 11.436362         .         .
1 "'s-Gravenhage"    "GM0518" 2004                  .    .    .    . 112.42858629119998   8.64365 13.064938 11.425336         .         .
1 "'s-Gravenhage"    "GM0518" 2005                  .    .    .    . 113.89015791298557  8.650149  13.07239  11.88968         .         .
1 "'s-Gravenhage"    "GM0518" 2006                  .    .    .    . 115.82629059750631  8.660427 13.068838 11.878828         .         .
1 "'s-Gravenhage"    "GM0518" 2007 1.1709528024032914    .    .    . 117.10037979407888  8.656781 13.072503   11.9539         .         .
1 "'s-Gravenhage"    "GM0518" 2008 1.0189597064731957    .    .    . 118.97398587078413  8.660601 13.085417 12.027326         .         .
1 "'s-Gravenhage"    "GM0518" 2009   1.65181669133134    .    .    . 121.94833551755373  8.680162 13.099203 12.061186         .         .
1 "'s-Gravenhage"    "GM0518" 2010 1.7168838356396807    .    .    . 123.41171554376437  8.693999  13.11248 12.058692         .         .
1 "'s-Gravenhage"    "GM0518" 2011 1.8364521815338959    .    .    . 125.01606784583329  8.707152 13.126465  12.03159 10.228337         .
1 "'s-Gravenhage"    "GM0518" 2012 2.3129111842105266    .    .    . 127.89143740628745  8.721113 13.134007 11.994463  10.21136         .
1 "'s-Gravenhage"    "GM0518" 2013 2.5779070224387945    .    .    . 131.08872334144462  8.728911 13.140085 11.930356 10.195252         .
1 "'s-Gravenhage"    "GM0518" 2014 2.5307801523129543    .    .    . 134.36594142498072  8.734882 13.151652   11.8488 10.225975         .
1 "'s-Gravenhage"    "GM0518" 2015 2.3942860219851227 58.2    2   13 135.70960083923052  8.746557  13.16156 11.817344 10.213326         .
1 "'s-Gravenhage"    "GM0518" 2016 2.2004945873548722    .    .    .  136.5238584442659  8.746398  13.17093 11.832868 10.241876         .
1 "'s-Gravenhage"    "GM0518" 2017 1.9359284664104206    .    .    .  136.9334300195987  8.755737 13.185452 11.876635 10.274782         .
1 "'s-Gravenhage"    "GM0518" 2018 1.5915721050958196    .    .    . 138.85049803987306   8.77323 13.195303 11.936114 10.283296         .
1 "'s-Gravenhage"    "GM0518" 2019  1.363041781627516    .    .    .  141.2109565065509   8.78309 13.210077 12.051608  10.30509         .
1 "'s-Gravenhage"    "GM0518" 2020                  .    .    .    .  144.8824413757212  8.797851 13.214614 12.139122         .         .
2 "'s-Hertogenbosch" "GM0796" 2000                  .    .    .    .                100  7.325149 11.778953 11.373663         .         .
2 "'s-Hertogenbosch" "GM0796" 2001                  .    .    .    .              102.4  7.336286  11.78826   11.8328         .         .
2 "'s-Hertogenbosch" "GM0796" 2002                  .    .    .    .           106.5984  7.345365 11.794346 11.792617         .         .
2 "'s-Hertogenbosch" "GM0796" 2003                  .    .    .    . 110.11614719999999   7.35628  11.80194 11.767217         .         .
2 "'s-Hertogenbosch" "GM0796" 2004                  .    .    .    . 112.42858629119998  7.363914  11.80543 11.753452         .         .
2 "'s-Hertogenbosch" "GM0796" 2005                  .    .    .    . 113.89015791298557  7.367077  11.81093 12.143667         .         .
2 "'s-Hertogenbosch" "GM0796" 2006                  .    .    .    . 115.82629059750631  7.374629  11.81782  12.12681         .         .
2 "'s-Hertogenbosch" "GM0796" 2007 1.3994621962031346    .    .    . 117.10037979407888  7.381502  11.82394 12.205215         .         .
2 "'s-Hertogenbosch" "GM0796" 2008 1.2629286880783888    .    .    . 118.97398587078413  7.387709 11.833377  12.26741         .         .
2 "'s-Hertogenbosch" "GM0796" 2009 1.9913041609661408    .    .    . 121.94833551755373   7.40062 11.846586 12.281482         .         .
2 "'s-Hertogenbosch" "GM0796" 2010 1.9178043271348002    .    .    . 123.41171554376437   7.41397 11.854997 12.280895         .         .
2 "'s-Hertogenbosch" "GM0796" 2011 1.9098898465745313    .    .    . 125.01606784583329  7.424762 11.862828 12.225747 10.303476         .
2 "'s-Hertogenbosch" "GM0796" 2012   2.37366700042712    .    .    . 127.89143740628745  7.426549  11.86932 12.191173 10.286084         .
2 "'s-Hertogenbosch" "GM0796" 2013 2.7690231192558423    .    .    . 131.08872334144462  7.433667 11.875712 12.096637  10.26936         .
2 "'s-Hertogenbosch" "GM0796" 2014 2.6149684400360687    .    .    . 134.36594142498072  7.440146  11.87875  12.01052 10.301238         .
2 "'s-Hertogenbosch" "GM0796" 2015 2.3745448788982113   27 49.7  5.6 135.70960083923052  7.218177 11.929053 11.996036 10.293784         .
2 "'s-Hertogenbosch" "GM0796" 2016 2.2898609680403648    .    .    .  136.5238584442659  7.224025 11.934336 12.025772 10.326927         .
2 "'s-Hertogenbosch" "GM0796" 2017  2.007377765032522    .    .    .  136.9334300195987  7.229114 11.941026 12.065701  10.35463         .
2 "'s-Hertogenbosch" "GM0796" 2018 1.7314613663629586    .    .    . 138.85049803987306  7.238497 11.946038 12.124705 10.368253         .
2 "'s-Hertogenbosch" "GM0796" 2019 1.5859610214620499    .    .    .  141.2109565065509  7.245655 11.951897  12.19029 10.389135         .
2 "'s-Hertogenbosch" "GM0796" 2020                  .    .    .    .  144.8824413757212  7.251345 11.954337 12.223978         .         .
3 "Aa en Hunze"      "GM1680" 2000                  .    .    .    .                100 4.4886365 10.134916 11.289782         .       100
3 "Aa en Hunze"      "GM1680" 2001                  .    .    .    .              102.4 4.5108595  10.14847 11.818513         . 102.73438
3 "Aa en Hunze"      "GM1680" 2002                  .    .    .    .           106.5984 4.5217886 10.138758   11.7855         . 102.53437
3 "Aa en Hunze"      "GM1680" 2003                  .    .    .    . 110.11614719999999 4.5108595 10.135313 11.753032         . 101.25672
3 "Aa en Hunze"      "GM1680" 2004                  .    .    .    . 112.42858629119998 4.5108595 10.139706 11.739367         . 101.13087
3 "Aa en Hunze"      "GM1680" 2005                  .    .    .    . 113.89015791298557 4.5108595 10.146708 12.134277         . 101.76472
3 "Aa en Hunze"      "GM1680" 2006                  .    .    .    . 115.82629059750631 4.5217886   10.1489  12.11742         . 102.99907
3 "Aa en Hunze"      "GM1680" 2007               1.33    .    .    . 117.10037979407888 4.5217886  10.15027 12.226357         . 105.97746
3 "Aa en Hunze"      "GM1680" 2008               1.05    .    .    . 118.97398587078413 4.5217886 10.153273  12.26741         . 109.09948
3 "Aa en Hunze"      "GM1680" 2009 1.6038806086922506    .    .    . 121.94833551755373 4.5325994   10.1489 12.273849         . 106.60252
3 "Aa en Hunze"      "GM1680" 2010 1.6676362226100447    .    .    . 123.41171554376437 4.5217886 10.157548 12.269553         .  105.9867
3 "Aa en Hunze"      "GM1680" 2011 1.7095345403683269    .    .    . 125.01606784583329 4.5325994 10.155724 12.249003  10.33514 106.70628
3 "Aa en Hunze"      "GM1680" 2012 2.3100113542930973    .    .    . 127.89143740628745 4.5325994  10.14804 12.199078 10.327867 106.10562
3 "Aa en Hunze"      "GM1680" 2013 2.4056473557597506    .    .    . 131.08872334144462 4.5217886  10.14081  12.11769 10.308275 103.74653
3 "Aa en Hunze"      "GM1680" 2014 2.3806689679800024    .    .    . 134.36594142498072 4.5217886 10.134718 12.024005 10.347644  102.1092
3 "Aa en Hunze"      "GM1680" 2015 2.2976666798716474  3.5 67.9 23.9 135.70960083923052 4.5108595 10.136304 11.982306 10.330508 103.01408
3 "Aa en Hunze"      "GM1680" 2016 2.2146642410820214    .    .    .  136.5238584442659 4.5108595 10.138006 11.976323 10.366885  104.4506
3 "Aa en Hunze"      "GM1680" 2017 2.0086648286727056    .    .    .  136.9334300195987 4.5217886  10.14211  12.01838 10.391165 106.54813
3 "Aa en Hunze"      "GM1680" 2018  1.615063420783109    .    .    . 138.85049803987306 4.5217886 10.141953  12.01325 10.399435 107.74178
3 "Aa en Hunze"      "GM1680" 2019  1.218314010611122    .    .    .  141.2109565065509 4.5217886 10.144275 12.068002 10.417065 108.91506
3 "Aa en Hunze"      "GM1680" 2020                  .    .    .    .  144.8824413757212 4.5217886 10.142465 12.101523         . 108.50176
4 "Aalsmeer"         "GM0358" 2000                  .    .    .    .                100  6.891626 10.028445 11.552146         .         .
4 "Aalsmeer"         "GM0358" 2001                  .    .    .    .              102.4  6.900731  10.03034 12.104395         .         .
4 "Aalsmeer"         "GM0358" 2002                  .    .    .    .           106.5984   6.96319 10.036225 12.069604         .         .
4 "Aalsmeer"         "GM0358" 2003                  .    .    .    . 110.11614719999999  7.008505 10.039547 12.047832         .         .
4 "Aalsmeer"         "GM0358" 2004                  .    .    .    . 112.42858629119998  7.011214 10.056037 12.032354         .         .
4 "Aalsmeer"         "GM0358" 2005                  .    .    .    . 113.89015791298557  7.028202 10.091832 12.387163         .         .
4 "Aalsmeer"         "GM0358" 2006                  .    .    .    . 115.82629059750631   7.06732  10.12739 12.373962         .         .
4 "Aalsmeer"         "GM0358" 2007  .7958766012279239    .    .    . 117.10037979407888  7.103322  10.18059   12.3918         .         .
4 "Aalsmeer"         "GM0358" 2008  .6784260515603799    .    .    . 118.97398587078413  7.156177 10.240174 12.434464         .         .
4 "Aalsmeer"         "GM0358" 2009 1.1649021824785006    .    .    . 121.94833551755373  7.220374  10.28148 12.436175         .         .
4 "Aalsmeer"         "GM0358" 2010 1.1593626817715061    .    .    . 123.41171554376437  7.261927 10.315233 12.440403         .         .
4 "Aalsmeer"         "GM0358" 2011 1.2185482808589119    .    .    . 125.01606784583329  7.297768 10.321013 12.424276 10.482217         .
4 "Aalsmeer"         "GM0358" 2012 1.4044026389705402    .    .    . 127.89143740628745  7.303843 10.329344  12.37217  10.48165         .
4 "Aalsmeer"         "GM0358" 2013 2.0481810201892126    .    .    . 131.08872334144462  7.313887 10.333938 12.310374 10.450358         .
4 "Aalsmeer"         "GM0358" 2014 2.0915789812401457    .    .    . 134.36594142498072   7.31854 10.344223  12.23276  10.50418         .
4 "Aalsmeer"         "GM0358" 2015 1.9489440557206301 37.8 39.7  6.8 135.70960083923052  7.329094  10.35134 12.226426  10.46103         .
4 "Aalsmeer"         "GM0358" 2016  1.880597966404233    .    .    .  136.5238584442659  7.349231 10.353703 12.241873 10.506447         .
4 "Aalsmeer"         "GM0358" 2017  1.492110860662243    .    .    .  136.9334300195987    7.3518  10.35771  12.27701 10.546596         .
4 "Aalsmeer"         "GM0358" 2018 1.2607160867372669    .    .    . 138.85049803987306   7.35628 10.364955 12.296556  10.54604         .
4 "Aalsmeer"         "GM0358" 2019 1.1927555792711635    .    .    .  141.2109565065509   7.36328 10.369075 12.403312  10.57182         .
4 "Aalsmeer"         "GM0358" 2020                  .    .    .    .  144.8824413757212  7.367709  10.37321 12.442365         .         .
5 "Aalten"           "GM0197" 2000                  .    .    .    .                100  5.420535  9.840175 11.127263         .         .
5 "Aalten"           "GM0197" 2001                  .    .    .    .              102.4   5.42495  9.842197 11.796694         .         .
5 "Aalten"           "GM0197" 2002                  .    .    .    .           106.5984   5.42495  9.844533 11.756512         .         .
5 "Aalten"           "GM0197" 2003                  .    .    .    . 110.11614719999999   5.42495  9.852089 11.724045         .         .
5 "Aalten"           "GM0197" 2004                  .    .    .    . 112.42858629119998  5.433722  9.848767 11.710588         .         .
5 "Aalten"           "GM0197" 2005                  .    .    .    . 113.89015791298557  5.648974 10.221068  12.07601         .         .
5 "Aalten"           "GM0197" 2006                  .    .    .    . 115.82629059750631  5.652489 10.224483 12.059152         .         .
5 "Aalten"           "GM0197" 2007 1.0519442832269297    .    .    . 117.10037979407888  5.652489  10.22441 12.111186         .         .
5 "Aalten"           "GM0197" 2008  .9472802127737094    .    .    . 118.97398587078413  5.652489 10.220012 12.145667         .         .
5 "Aalten"           "GM0197" 2009 1.6363636363636365    .    .    . 121.94833551755373  5.648974  10.22194  12.14305         .         .
5 "Aalten"           "GM0197" 2010 1.4213345967418638    .    .    . 123.41171554376437  5.652489  10.21972 12.139817         .         .
5 "Aalten"           "GM0197" 2011  1.501556491485076    .    .    . 125.01606784583329  5.648974 10.214825  12.10502 10.279052         .
5 "Aalten"           "GM0197" 2012  1.957019422494646    .    .    . 127.89143740628745  5.645447 10.206625  12.04164 10.261792         .
5 "Aalten"           "GM0197" 2013  2.443268056121127    .    .    . 131.08872334144462  5.638355 10.204074 11.984159 10.239828         .
5 "Aalten"           "GM0197" 2014 2.4531668153434434    .    .    . 134.36594142498072  5.634789  10.20003 11.880217  10.27592         .
5 "Aalten"           "GM0197" 2015 1.9693816884661117    7 83.5  4.3 135.70960083923052  5.631212 10.200328 11.833517 10.250465         .
end

[Carlo Lazzaro]

Adam:
you do not explain want the -if- clause is intended to do with your sample.
I guess that you're restricting your sample at 1 panel only (Number of groups = 1) making -xtreg,fe- unfeasible (as expected, by the way).

[Adam Klaas]

Adam:
you do not explain want the -if- clause is intended to do with your sample.
I guess that you're restricting your sample at 1 panel only (Number of groups = 1) making -xtreg,fe- unfeasible (as expected, by the way).

Yes, sorry forgot to say that I indeed want to restrict my sample to the cities that are equal to the dummy low_dev==1. What is the best way to deal with this or how can I solve this problem ?

. gen e_sample = e(sample)

.
. tab e_sample

   e_sample |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,383       99.88       99.88
          1 |          9        0.12      100.00
------------+-----------------------------------
      Total |      7,392      100.00
end

If I use this command it shows that 0s freq is 7383
Thanks,

Klaas

[Leonardo Guizzetti]

What do you hope to learn from 9 observations.Furthermore, it's hopeless to run a regression model with just as many, if not more, parameters to estimate as observations.

I would take a big step back and examine the quality of your data, any transformations you use, and what is the reason for the many missing observations.

[Carlo Lazzaro]

Adam:
via the -if- clause you are asking Stata to run -xtreg,fe- on a subsample of 9 observations belonging to the same panel.
As expected, there's no chance for this approach to work.

[Adam Klaas]

Adam:
via the -if- clause you are asking Stata to run -xtreg,fe- on a subsample of 9 observations belonging to the same panel.
As expected, there's no chance for this approach to work.

Hmmm I see, but what should I do if I want to run a regression based on a subsample when the dummy is equal? Because I want to analyse the different output regressions between the two dummys(Low and high)?

[Carlo Lazzaro]

Adam:
try:

xtreg HousePrice log_PopulationDensity log_Population Unemployment_rate real_consCost_p logReal_income real_interest i.low_devA ,fe vce(robust)

[Adam Klaas]

Adam:
try:

xtreg HousePrice log_PopulationDensity log_Population Unemployment_rate real_consCost_p logReal_income real_interest i.low_devA ,fe vce(robust)

Hi Sir,

I am getting this:

. xtreg log_realHP log_PopulationDensity log_Population Unemployment_rate real_consCost_p
>  logReal_income real_interest i.low_devA,fe vce(robust)
note: 1.low_devA omitted because of collinearity

Fixed-effects (within) regression               Number of obs     =      3,020
Group variable: GM_code                         Number of groups  =        350

R-sq:                                           Obs per group:
     within  = 0.6426                                         min =          1
     between = 0.0107                                         avg =        8.6
     overall = 0.0055                                         max =          9

                                                F(6,349)          =    1227.70
corr(u_i, Xb)  = -0.3661                        Prob > F          =     0.0000

                                       (Std. Err. adjusted for 350 clusters in GM_code)
---------------------------------------------------------------------------------------
                      |               Robust
           log_realHP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
log_PopulationDensity |   .0369799   .0276281     1.34   0.182    -.0173587    .0913185
       log_Population |   .0719217   .0530923     1.35   0.176    -.0324995    .1763429
    Unemployment_rate |  -.1208169   .0040402   -29.90   0.000    -.1287631   -.1128707
      real_consCost_p |  -.0557926   .0008512   -65.54   0.000    -.0574667   -.0541184
       logReal_income |  -.0498034    .059056    -0.84   0.400    -.1659539    .0663471
        real_interest |   .0355213   .0011817    30.06   0.000     .0331972    .0378454
           1.low_devA |          0  (omitted)
                _cons |   11.85315   .9092113    13.04   0.000     10.06493    13.64138
----------------------+----------------------------------------------------------------
              sigma_u |  .25363028
              sigma_e |  .05180709
                  rho |  .95994803   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------




end

[Adam Klaas]

What do you hope to learn from 9 observations.Furthermore, it's hopeless to run a regression model with just as many, if not more, parameters to estimate as observations.

I would take a big step back and examine the quality of your data, any transformations you use, and what is the reason for the many missing observations.

So you mean to drop some explanatory variables in my regression model and just focus on the most important explanatory variables? The reason of so many missing data is because the website I have the data from doesnt have a complete data sets for some variables some years does not have values.

[Carlo Lazzaro]

Adam:
-i.low_devA- was omitted as, being a time-invariant predictor, the -fe- estimator wiped it out.
That said, I would recommend to add -i.timevar- as a predictor in the right-hand side of your regression equation.
You can then test the joint staistical significance on -i.timevar- via -testparm-.

[Adam Klaas]

Adam:
-i.low_devA- was omitted as, being a time-invariant predictor, the -fe- estimator wiped it out.
That said, I would recommend to add -i.timevar- as a predictor in the right-hand side of your regression equation.
You can then test the joint staistical significance on -i.timevar- via -testparm-.

Now I am getting this when I use the Year as timevariable:

. xtreg log_realHP log_PopulationDensity log_Population logunemployment real_consCost log
> Real_income real_interest i.high_dev i.Year,fe vce(robust)
note: 2018.Year omitted because of collinearity
note: 2019.Year omitted because of collinearity

Fixed-effects (within) regression               Number of obs     =      3,020
Group variable: GM_code                         Number of groups  =        350

R-sq:                                           Obs per group:
     within  = 0.8950                                         min =          1
     between = 0.0493                                         avg =        8.6
     overall = 0.0021                                         max =          9

                                                F(13,349)         =    1091.08
corr(u_i, Xb)  = -0.5984                        Prob > F          =     0.0000

                                       (Std. Err. adjusted for 350 clusters in GM_code)
---------------------------------------------------------------------------------------
                      |               Robust
           log_realHP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
log_PopulationDensity |   .0408971   .0300572     1.36   0.175    -.0182189     .100013
       log_Population |   .0885554   .0559159     1.58   0.114    -.0214192      .19853
      logunemployment |   .0626742   .0115337     5.43   0.000     .0399898    .0853585
        real_consCost |   .7189247   .0103547    69.43   0.000     .6985592    .7392901
       logReal_income |   .2008085   .0887648     2.26   0.024     .0262272    .3753898
        real_interest |   .5124789   .0076199    67.26   0.000     .4974922    .5274656
           1.high_dev |  -.0151478   .0041305    -3.67   0.000    -.0232717    -.007024
                      |
                 Year |
                2012  |   1.015005   .0154797    65.57   0.000       .98456     1.04545
                2013  |   2.625527   .0389241    67.45   0.000     2.548972    2.702082
                2014  |   3.206205   .0472807    67.81   0.000     3.113214    3.299196
                2015  |   2.712921   .0409138    66.31   0.000     2.632452     2.79339
                2016  |   1.726945   .0260159    66.38   0.000     1.675777    1.778112
                2017  |    .692979   .0106379    65.14   0.000     .6720565    .7139014
                2018  |          0  (omitted)
                2019  |          0  (omitted)
                      |
                _cons |  -68.49108   1.869881   -36.63   0.000    -72.16874   -64.81343
----------------------+----------------------------------------------------------------
              sigma_u |  .29871983
              sigma_e |  .02811635
                  rho |  .99121869   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------

[Carlo Lazzaro]

Adam:
please report the outcome of

. testparm i.Year

[Adam Klaas]

Adam:
please report the outcome of

. testparm i.Year

. testparm i.Year
no such variables;
the specified varlist does not identify any testable coefficients

I am getting this as outcome