Mixed Results LM-Test and Hausman-Test

Hello everyone,

I am running some regression on my dataset and I do get mixed results which is very confusing for me.

Running the LM-Test to see if I use RE or OLS I get prob > chibar2 = 1.000 which indicates that I should take pooled regression

Code:

// Try as Panel Data RE and LM Test
xtset namex monthly_date, monthly
xtreg ri mktminusrf smb_5 hml rmw cma logdiff_fintech_funding, re
xttest0

After that I did the Hausman-Test to see if FE or RE should be considered my result is Prob>chi2 = 0.8903 which indicates that I should take RE-Model over FE model

Code:

xtreg ri mktminusrf smb_5 hml rmw cma logdiff_fintech_funding, fe
estimates store fe
xtreg ri mktminusrf smb_5 hml rmw cma logdiff_fintech_funding, re
hausman fe

Therefore, I should run my regressions only with pooled regression, right? If so, is it still possible to hold for fix effects like the id or time? Or in other words, does that make sense at all since we actually rejected the FE model?

Code:

reg ri mktminusrf smb_5 hml rmw cma logdiff_fintech_funding i.namex
reg ri mktminusrf smb_5 hml rmw cma logdiff_fintech_funding i.monthly_date

I ask because I am replicating a paper from which I have the original dataset and they hold for fix effects.

Here is a sample data

Code:

* Example generated by -dataex-. To install: ssc install dataex
clear
input double logdiff_fintech_funding float monthly_date double(mktminusrf smb_5 hml rmw cma rf ri) float namex
-2.428878818318338 601   3.4  1.53  2.74  -.55  1.43   0   9.74967061923584 1
 4.750675543873377 602  6.31  1.85  2.01   -.9  1.67 .01   7.19288115246098 1
-1.547824874555269 603     2  5.03  3.12   .49  1.69 .01 -.1219820828667389 1
-2.377775945715615 604 -7.89  -.08 -2.32  1.38  -.18 .01 -11.73197309417041 1
 2.033946504409595 605 -5.56 -2.59 -4.27  -.34 -1.48 .01 -8.650023366859694 1
 1.866966321771243 606  6.93   .13   .04   .32  2.03 .01  -2.30703713485449 1
-6.230064946559605 607 -4.77 -3.07 -1.51   .34 -2.13 .01 -11.26298770771683 1
 5.650530448089943 608  9.54  3.71 -2.94  -.01   .39 .01  5.226579911347658 1
  .453532778924937 609  3.88   .72 -2.23  1.46  -.16 .01 -12.63015385787189 1
-2.051943886966359 610    .6  3.54  -.58   -.1  1.76 .01 -4.368399944212481 1
 2.453635974122106 611  6.82  1.03  3.47 -3.44  3.44 .01  21.90832010280622 1
-2.555251514231911 612  1.99 -2.38   .68 -1.07    .8 .01  2.913674964491289 1
-.3940574407002404 613  3.49  1.76  1.73 -1.76   .72 .01  4.068267880099642 1
 .9019179029163964 614   .45  2.66 -1.16  1.21  -.03 .01 -6.657778025904411 1
-3.366182863817053 615   2.9  -.41 -2.15   .96 -1.28   0 -7.876952978993803 1
 3.801343710610448 616 -1.27  -.69 -2.12  2.02 -1.46   0 -4.316214001119832 1
 .5375060083539553 617 -1.75   .09  -.26  2.16  -1.4   0  -6.63788863069695 1
-.9872492784416207 618 -2.36 -1.38 -1.18  2.41 -1.75   0 -11.40552472135674 1
 .6191782687064706 619 -5.99 -3.39 -1.58  2.79  -.23 .01 -15.76704135095508 1
  .263701731990106 620 -7.59  -3.9  -.98  1.71   .24   0 -25.09158623711403 1
-1.255992779152897 621 11.35  3.72  -.96 -1.42  -.86   0  11.60089037646026 1
-7.328765598425764 622  -.28  -.34  -.18  1.46  1.52   0 -20.20469666749166 1
 6.918695219020471 623   .74  -.36  1.57   .59  2.44   0  2.205842106223427 1
 1.664472961319299 624  5.05  2.35 -2.14 -1.05 -1.41   0  28.23735227962441 1
-4.488823618117669 625  4.42 -1.54   .01  -.17  -.03   0   11.9215991981736 1
 2.808398174936492 626  3.11   -.3  -.06   .25   .77   0  20.07462686567165 1
-.1262357447864098 627  -.85  -.66   -.2   .96   .72   0 -15.25585249637455 1
 .4387329863579144 628 -6.19   -.2   .08  1.98  2.37 .01 -9.258032073534451 1
 .6944331180936318 629  3.89   .99   .54 -1.48   .37   0  11.29353769900062 1
-.8876966737515692 630   .79 -2.74   .01   .68   .12   0 -10.26975348234923 1
 1.840011420252095 631  2.55   .61    .6  -.77  -.69 .01  8.845620743138433 1
-.6554042290035031 632  2.73   .69  1.56 -1.14  1.57 .01  10.62919416188624 1
-.2497564922306408 633 -1.76   -.8  4.16 -1.35  2.28 .01  5.538836480100336 1
-2.997485122381406 634   .78   .41 -1.12   .94   .93 .01  5.783980032465801 1
 2.968343045907073 635  1.18  1.91  3.26 -1.75   .88 .01  17.84005114627837 1
 .2701289866054237 636  5.57   .57  1.34 -1.88  1.47   0 -2.497596010654139 1
 .1509605075700726 637  1.29  -.35   .28  -.96   .49   0 -.7069419958456361 1
-1.062582825088892 638  4.03    .9  -.07   .13  1.21   0  8.459320722874628 1
 2.455217987516834 639  1.56 -2.32   .35   .04   .39   0  1.067330134854775 1
-.6101666516017357 640   2.8  2.27  1.33  -.71  -.83   0  10.96632961534452 1
-.8187397318572058 641  -1.2  1.33   -.4  -.47   .01   0  -5.78314123640568 1
 .1706158923222114 642  5.65  1.81   .71 -1.43   .53   0  13.53024190455739 1
-.7756070164821449 643 -2.71  -.03 -2.48   .85 -2.13   0 -3.287326969457456 1
 1.726891305749435 644  3.77  2.72 -1.57   -.1 -1.32   0  -2.19549166299936 1
 .3957065823815764 645  4.18 -1.57  1.36  2.83   .89   0  1.232019655177335 1
-1.548639224836185 646  3.12  1.47  -.38   .77   .12   0  13.24265022329774 1
-1.181443691265908 647  2.81  -.44   -.2  -.57   .07   0 -1.517048888224331 1
-.0883624242566112 648 -3.32   .56 -1.88  -4.5 -1.42   0  7.578728949732418 1
 1.654120853093416 649  4.65   .16  -.49  -.49   -.4   0 -1.313700354005207 1
 .7675383150229624 650   .43 -1.23   4.6  1.76  1.91   0  4.113931593373855 1
 .7337002959205572 651  -.19 -4.21  1.62  2.85  1.09   0 -11.97717543096541 1
-1.300090055657543 652  2.06 -1.83  -.38   .45 -1.09   0                  0 1
 3.226876440418037 653  2.61  3.04   -.6  -1.9  -1.9   0  1.585206473468275 1
-1.639863325627885 654 -2.04 -4.16   .04  1.48   .44   0 -.7802349025506456 1
-1.073779734353744 655  4.23    .3  -.76  -.91  -.65   0   5.50781854776281 1
-.7707598557843998 656 -1.97  -3.8 -1.68  1.28  -.62   0  6.277437421279833 1
 .1447530739194685 657  2.52  3.79 -1.81  -.78  -.18   0  .6449163219633881 1
 .1178356353986567 658  2.55 -2.27 -3.37  1.69   .15   0 -.6990888408729704 1
 2.635474229565091 659  -.06  2.85  1.56 -1.52   .81   0  5.281517853545095 1
-3.078890081227631 660 -3.11  -.91 -3.06  1.09 -1.67   0 -15.31597537371373 1
 .4817352432663817 661  6.13   .35 -2.16   .06 -1.62   0   4.35686522643044 1
 .2211978151041967 662 -1.12  3.07  -.73   .16  -.54   0 -2.340530842046646 1
 1.991202305673274 663   .59 -2.99  2.13   .41  -.49   0  3.509094249494756 1
-1.329517653461937 664  1.36   .85  -1.9 -1.54  -.68   0  3.578029941365856 1
   .01391606423699 665 -1.53  2.88 -1.04  1.03 -1.51   0  3.454429422331463 1
 .3699056815049282 666  1.54  -4.5 -4.49   .31  -2.6   0  5.053176298564061 1
-.1181539396678257 667 -6.04   .38  2.88   .75  1.14   0 -8.613293462008967 1
 .1582819462867526 668 -3.07 -2.81   .73  1.66   -.5   0 -4.344877233271656 1
 1.965128759761292 669  7.75 -2.05  -.32  1.19   .45   0  7.701828585644973 1
-2.397516434496359 670   .56  3.35 -1.23 -2.11    -1   0  3.873806760104661 1
-2.359402434240175 671 -2.17    -3 -2.07   .45   .17 .01 -3.165594842970808 1
 1.646352401779502 672 -5.77 -3.56  3.13  2.27     3 .01 -15.99323717672722 1
-.8583285517720807 673  -.07   .87  -.03  2.44  2.09 .02 -11.47723012169207 1
 1.201286271998179 674  6.96  1.01   1.3   .58   .07 .02  8.366773160473809 1
-2.428878818318338 601   3.4  1.53  2.74  -.55  1.43   0  2.368137782561895 2
 4.750675543873377 602  6.31  1.85  2.01   -.9  1.67 .01  13.51961794602173 2
-1.547824874555269 603     2  5.03  3.12   .49  1.69 .01  3.077372645878357 2
-2.377775945715615 604 -7.89  -.08 -2.32  1.38  -.18 .01 -9.035157232704403 2
 2.033946504409595 605 -5.56 -2.59 -4.27  -.34 -1.48 .01 -13.00623063881619 2
 1.866966321771243 606  6.93   .13   .04   .32  2.03 .01 -5.065110542705463 2
-6.230064946559605 607 -4.77 -3.07 -1.51   .34 -2.13 .01 -10.92393114332057 2
 5.650530448089943 608  9.54  3.71 -2.94  -.01   .39 .01  8.850566965344177 2
  .453532778924937 609  3.88   .72 -2.23  1.46  -.16 .01 -2.169458881930408 2
-2.051943886966359 610    .6  3.54  -.58   -.1  1.76 .01 -.9071284330072684 2
 2.453635974122106 611  6.82  1.03  3.47 -3.44  3.44 .01  13.30895218196535 2
-2.555251514231911 612  1.99 -2.38   .68 -1.07    .8 .01   5.69565179666697 2
-.3940574407002404 613  3.49  1.76  1.73 -1.76   .72 .01 -.1547070478347035 2
 .9019179029163964 614   .45  2.66 -1.16  1.21  -.03 .01 -.5535264021315793 2
-3.366182863817053 615   2.9  -.41 -2.15   .96 -1.28   0 -1.311378461472698 2
 3.801343710610448 616 -1.27  -.69 -2.12  2.02 -1.46   0  2.303116065498028 2
 .5375060083539553 617 -1.75   .09  -.26  2.16  -1.4   0 -2.541673135824082 2
-.9872492784416207 618 -2.36 -1.38 -1.18  2.41 -1.75   0 -3.726017676045373 2
 .6191782687064706 619 -5.99 -3.39 -1.58  2.79  -.23 .01 -13.21072808779173 2
  .263701731990106 620 -7.59  -3.9  -.98  1.71   .24   0 -4.307173712418937 2
-1.255992779152897 621 11.35  3.72  -.96 -1.42  -.86   0  10.17386579733768 2
-7.328765598425764 622  -.28  -.34  -.18  1.46  1.52   0 -.7286401183577812 2
 6.918695219020471 623   .74  -.36  1.57   .59  2.44   0   8.63191710445154 2
 1.664472961319299 624  5.05  2.35 -2.14 -1.05 -1.41   0  8.660940215237343 2
-4.488823618117669 625  4.42 -1.54   .01  -.17  -.03   0  7.576411173654582 2
 2.808398174936492 626  3.11   -.3  -.06   .25   .77   0  7.316341633576536 2
end
format %tm monthly_date

Thanks



Edit: I did also the command testparm and the results for i.xname was 0.832 and i.monthly_date = 0 which indicates that time fixed effects is needed in the regression

Share on FacebookPlus on Google+