Please show your Stata commands and Stata output.
Surround them with code delimiters to make them readable.
Surround them with code delimiters to make them readable.
-
Login or Register
- Log in with
- You are not logged in. You can browse but not post. Login or Register by clicking 'Login or Register' at the top-right of this page. For more information on Statalist, see the FAQ.
-
-
Karaca: Approaches #2 and #3 are always guaranteed to be the same: they're the same estimator. As Eric says, you need to show your Stata output for us to say more. I'm pretty sure that, when you do, the estimate of sigma(u) will be zero.Comment
-
Hi Carlo,
here is what I got after xttest0.
xttest0
Breusch and Pagan Lagrangian multiplier test for random effects
AERY[Reg,t] = Xb + u[Reg] + e[Reg,t]
Estimated results:
| Var sd = sqrt(Var)
---------+-----------------------------
AERY | 448.951 21.18846
e | .8437053 .9185343
u | 0 0
Test: Var(u) = 0
chibar2(01) = 0.00
Prob > chibar2 = 1.0000
Comment
-
Hi Eric and Jeff.
These are my codes and results.
Codes;
. clear all
. import excel "C:\Users\Asus\Desktop\Sheet666666Ultimate.xls x", sheet("Sheet1")
> firstrow
(9 vars, 66 obs)
. encode Region, gen(Reg)
. ssc install asdoc
checking asdoc consistency and verifying not already installed...
all files already exist and are up to date.
. asdoc regress AERY MktRF SMB HML RMW CMA Mom, replace
. tabulate Reg, generate(D)
. asdoc regress AERY MktRF SMB HML RMW CMA Mom D2 D3 D4 D5 D6, replace
. xtset Reg Time
. asdoc xtreg AERY MktRF SMB HML RMW CMA Mom, fe, replace
. asdoc xtreg AERY MktRF SMB HML RMW CMA Mom, re, replace
----------------------------------------------------------------------------------------------------------------
Results;
1-)
asdoc regress AERY MktRF SMB HML RMW CMA Mom, replace
Source | SS df MS Number of obs = 66
-------------+---------------------------------- F(6, 59) = 5416.37
Model | 29128.9313 6 4854.82188 Prob > F = 0.0000
Residual | 52.8831237 59 .89632413 R-squared = 0.9982
-------------+---------------------------------- Adj R-squared = 0.9980
Total | 29181.8144 65 448.950991 Root MSE = .94674
------------------------------------------------------------------------------
AERY | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
MktRF | 1.001499 .0081823 122.40 0.000 .9851261 1.017872
SMB | .4192452 .0171385 24.46 0.000 .3849511 .4535394
HML | .0470112 .016713 2.81 0.007 .0135686 .0804539
RMW | .0168759 .0247946 0.68 0.499 -.0327379 .0664897
CMA | .0038372 .0201636 0.19 0.850 -.0365101 .0441845
Mom | .0128932 .006925 1.86 0.068 -.0009637 .0267501
_cons | -.1946927 .1818611 -1.07 0.289 -.5585959 .1692106
------------------------------------------------------------------------------
(note: file Myfile.doc not found)
Click to Open File: Myfile.doc
2-)
. asdoc regress AERY MktRF SMB HML RMW CMA Mom D2 D3 D4 D5 D6, replace
Source | SS df MS Number of obs = 66
-------------+---------------------------------- F(11, 54) = 3139.43
Model | 29136.2543 11 2648.75039 Prob > F = 0.0000
Residual | 45.5600888 54 .843705347 R-squared = 0.9984
-------------+---------------------------------- Adj R-squared = 0.9981
Total | 29181.8144 65 448.950991 Root MSE = .91853
------------------------------------------------------------------------------
AERY | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
MktRF | 1.00382 .0084108 119.35 0.000 .9869576 1.020683
SMB | .4114549 .0188748 21.80 0.000 .3736132 .4492967
HML | .0545304 .0191644 2.85 0.006 .0161082 .0929526
RMW | .014417 .0262901 0.55 0.586 -.0382916 .0671256
CMA | .0029491 .0210127 0.14 0.889 -.039179 .0450771
Mom | .0134288 .0075123 1.79 0.079 -.0016324 .0284901
D2 | -.5252351 .3938701 -1.33 0.188 -1.314897 .264427
D3 | .3736749 .4112653 0.91 0.368 -.4508623 1.198212
D4 | .3567764 .4165184 0.86 0.395 -.4782927 1.191846
D5 | .3522308 .4256779 0.83 0.412 -.5012021 1.205664
D6 | -.2605524 .4363413 -0.60 0.553 -1.135364 .6142594
_cons | -.2455959 .3246247 -0.76 0.453 -.8964293 .4052375
------------------------------------------------------------------------------
(note: file Myfile.doc not found)
Click to Open File: Myfile.doc
3-)
. asdoc xtreg AERY MktRF SMB HML RMW CMA Mom, fe, replace
Fixed-effects (within) regression Number of obs = 66
Group variable: Reg Number of groups = 6
R-sq: Obs per group:
within = 0.9984 min = 11
between = 0.9675 avg = 11.0
overall = 0.9982 max = 11
F(6,54) = 5720.94
corr(u_i, Xb) = -0.0450 Prob > F = 0.0000
------------------------------------------------------------------------------
AERY | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
MktRF | 1.00382 .0084108 119.35 0.000 .9869576 1.020683
SMB | .4114549 .0188748 21.80 0.000 .3736132 .4492967
HML | .0545304 .0191644 2.85 0.006 .0161082 .0929526
RMW | .014417 .0262901 0.55 0.586 -.0382916 .0671256
CMA | .0029491 .0210127 0.14 0.889 -.039179 .0450771
Mom | .0134288 .0075123 1.79 0.079 -.0016324 .0284901
_cons | -.1961135 .1790362 -1.10 0.278 -.5550595 .1628325
-------------+----------------------------------------------------------------
sigma_u | .37948838
sigma_e | .91853435
rho | .14580235 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(5, 54) = 1.74 Prob > F = 0.1421
(note: file Myfile.doc not found)
Click to Open File: Myfile.doc
4-)
. asdoc xtreg AERY MktRF SMB HML RMW CMA Mom, re, replace
Random-effects GLS regression Number of obs = 66
Group variable: Reg Number of groups = 6
R-sq: Obs per group:
within = 0.9984 min = 11
between = 0.9709 avg = 11.0
overall = 0.9982 max = 11
Wald chi2(6) = 32498.21
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
AERY | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
MktRF | 1.001499 .0081823 122.40 0.000 .9854618 1.017536
SMB | .4192452 .0171385 24.46 0.000 .3856543 .4528361
HML | .0470112 .016713 2.81 0.005 .0142544 .0797681
RMW | .0168759 .0247946 0.68 0.496 -.0317205 .0654723
CMA | .0038372 .0201636 0.19 0.849 -.0356827 .0433572
Mom | .0128932 .006925 1.86 0.063 -.0006795 .0264659
_cons | -.1946927 .1818611 -1.07 0.284 -.5511339 .1617486
-------------+----------------------------------------------------------------
sigma_u | 0
sigma_e | .91853435
rho | 0 (fraction of variance due to u_i)
------------------------------------------------------------------------------
(note: file Myfile.doc not found)
Click to Open File: Myfile.doc
Comment
-
Karaca:
hencer, you do not have a panel-wise effect.
Switch to pooled OLS.Kind regards,
Carlo
(Stata 19.0)Comment
-
Thank you Carlo.
But when I look at my data set, it looks like i do have cross-sectional and time series variation. it just does not make sense for me. Would you please little bit extend your explanation?Comment
-
I disagree with Carlo, for reasons I will expound upon tomorrow. Briefly, getting a negative estimate for sigma_u does not mean there’s no heterogeneity. It means that the composite error has negative serial correlation.Comment
-
Hi Jeff. Is having same coefficients for both Pooled OLS and Random effects models a problem or it is a nature of data set? if it is a problem, is there anything that I can do to fix it?Comment
-
Karaca: Several comments. First, as a general rule, whenever the sigma_u (Stata notation) is zero, POLS and RE are identical. It's the same estimator in that case. This is not a deep point. Please look up a book on panel data to confirm this. An estimate of zero occurs when there is negative serial correlation in the composite error term. In Stata's notation, this is v(i,t) = u(i) + e(i,t). The RE estimator goes way back, and it was always assumed that e(i,t) had no serial correlation. In that case, the pairwise covariance between v(i,t) and v(i,s) for t and s different is sigma_u_square. That's what the xttest0 is based on. But this is an outdated way of thinking. There is absolutely no reason to expect e(i,t) to not have serial correlation. And when that serial correlation is strongly negative, that can cause v(i,t) to have negative serial correlation. That means sigma_u will be estimated to be zero, and xttest0 will return a p-value equal to one. But that does not mean c(i) is not present. The Breusch-Pagan test is based on a very restrictive set of assumptions.
So, the general point is that POLS = RE when sigma_u = 0.
A specific point is: You only have N = 6, which means you shouldn't be trying to use RE, anyway. You need to look at an econometrics book on panel data. The RE estimator is justified with large N asymptotics, and N = 6 does not cut it.
With N = 6 and T = 11 you shouldn't be doing anything beyond basic regression. Including fixed effects for the 6 cross section units changes almost nothing. You could also put in year fixed effects, but you don't have a very large N, so these are imprecisely estimated. I'd report POLS and show that adding the five dummies changes very little from a practical perspective.Comment
-
Thank you Jeff for clear explanation.
I am working from Gujarati and Wooldridge books but could not see anything related "N" and "T".
I will take a look againComment
-
You should look at my MIT Press book.Comment
-
I will look at it. Thank you againComment
-
Hello everyone,
I have a problem similar to the one described by Jeff and Stefano. I'm trying to estimate the effect of different forms of infrastructure on GDP growth for a panel sample of 24 cities over 21 years. The model is specified in per capita terms and includes, as explanatory variables, the interaction between the infrastructure of each type and the city.
The results of the RE and Pool OLS models are exactly the same. I'm uncertain whether this is a data-related problem or what kind of correction I should apply.
Pool OLS
Code:regress lnY ln_K ln_khum $xlist $infr b13.id#c.( $infr ) , vce(cluster id)
Random EffectsCode:Linear regression Number of obs = 504 F(2, 23) = . Prob > F = . R-squared = 0.9971 Root MSE = .078 (Std. Err. adjusted for 24 clusters in id) -------------------------------------------------------------------------------- | Robust lnY | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ln_K | .0392785 .031809 1.23 0.229 -.0265235 .1050806 ln_khum | .0609317 .039548 1.54 0.137 -.0208796 .1427431 ln_lineas | .0382541 .0365162 1.05 0.306 -.0372854 .1137936 ln_poten | .3309151 .0256899 12.88 0.000 .2777714 .3840588 ln_km | -.02535 .0245589 -1.03 0.313 -.076154 .0254539 ln_salud | .0660992 .0018572 35.59 0.000 .0622574 .069941 ln_edu | .4252657 .0995293 4.27 0.000 .2193736 .6311578 | id#c.ln_lineas | Amazonas | .065404 .0442473 1.48 0.153 -.0261285 .1569365 Apurímac | .0946574 .0243155 3.89 0.001 .044357 .1449579 Arequipa | .1536096 .0417419 3.68 0.001 .06726 .2399593 Ayacucho | .0669445 .037277 1.80 0.086 -.0101688 .1440578 Cajamarca | -.0573588 .0232117 -2.47 0.021 -.1053758 -.0093419 Cusco | .1397357 .0432859 3.23 0.004 .0501919 .2292794 Huancavelica | -.0613288 .0132292 -4.64 0.000 -.0886956 -.0339621 Huánuco | .0118944 .0528762 0.22 0.824 -.0974885 .1212772 Ica | .1273962 .0487523 2.61 0.016 .0265444 .2282481 Junín | .0615422 .0411515 1.50 0.148 -.0235862 .1466706 La Libertad | .1186316 .0398124 2.98 0.007 .0362733 .2009898 Lambayeque | .0322334 .0442022 0.73 0.473 -.0592057 .1236726 Loreto | -.0145669 .0405126 -0.36 0.722 -.0983736 .0692398 Madre de Dios | .0476139 .0486835 0.98 0.338 -.0530955 .1483233 Moquegua | -.0383305 .036807 -1.04 0.309 -.1144716 .0378107 Pasco | -.0778414 .0303786 -2.56 0.017 -.1406844 -.0149984 Piura | .1136678 .0538 2.11 0.046 .0023739 .2249617 Puno | .0162516 .0349408 0.47 0.646 -.0560289 .088532 San Martín | .0757022 .0364599 2.08 0.049 .0002792 .1511253 Tacna | .0603895 .042311 1.43 0.167 -.0271375 .1479165 Tumbes | .0996731 .0568279 1.75 0.093 -.0178843 .2172305 Ucayali | .0441944 .0386813 1.14 0.265 -.0358241 .1242128 Áncash | .0028638 .0375553 0.08 0.940 -.0748253 .0805529 | id#c.ln_poten | Amazonas | -.1832356 .0544154 -3.37 0.003 -.2958025 -.0706688 Apurímac | -2.168464 .0621639 -34.88 0.000 -2.297059 -2.039868 Arequipa | -.0914937 .0327226 -2.80 0.010 -.1591856 -.0238018 Ayacucho | -.2981789 .0463234 -6.44 0.000 -.3940061 -.2023518 Cajamarca | -.7506572 .0192499 -39.00 0.000 -.7904788 -.7108357 Cusco | -.3565641 .0288598 -12.36 0.000 -.4162651 -.2968631 Huancavelica | -.269121 .0617575 -4.36 0.000 -.3968762 -.1413659 Huánuco | -.2810383 .0450281 -6.24 0.000 -.3741861 -.1878905 Ica | -.1507203 .0362095 -4.16 0.000 -.2256254 -.0758151 Junín | .4341032 .0194841 22.28 0.000 .3937972 .4744092 La Libertad | -.3411277 .04063 -8.40 0.000 -.4251774 -.2570781 Lambayeque | -.2703864 .0425167 -6.36 0.000 -.3583389 -.1824339 Loreto | -.2914384 .0549464 -5.30 0.000 -.4051037 -.1777731 Madre de Dios | -.3801749 .0494981 -7.68 0.000 -.4825696 -.2777801 Moquegua | -.4167558 .0374018 -11.14 0.000 -.4941275 -.3393842 Pasco | -.2483541 .0637836 -3.89 0.001 -.3803004 -.1164077 Piura | -.1107411 .0507632 -2.18 0.040 -.2157528 -.0057294 Puno | -.2495404 .0329792 -7.57 0.000 -.317763 -.1813178 San Martín | -.4251851 .0549771 -7.73 0.000 -.5389138 -.3114564 Tacna | -.1556492 .0873299 -1.78 0.088 -.3363049 .0250065 Tumbes | -.9941327 .1344387 -7.39 0.000 -1.27224 -.716025 Ucayali | -.2154607 .0855361 -2.52 0.019 -.3924055 -.0385158 Áncash | -.0080315 .0189589 -0.42 0.676 -.047251 .0311879 | id#c.ln_km | Amazonas | .182208 .0396818 4.59 0.000 .1001199 .2642962 Apurímac | 1.151643 .0461113 24.98 0.000 1.056255 1.247032 Arequipa | .2434194 .0238159 10.22 0.000 .1941525 .2926864 Ayacucho | .2697952 .0278456 9.69 0.000 .2121921 .3273982 Cajamarca | -.1364695 .0189734 -7.19 0.000 -.1757189 -.0972202 Cusco | .3099537 .0380797 8.14 0.000 .2311798 .3887276 Huancavelica | .2031439 .0940441 2.16 0.041 .0085989 .397689 Huánuco | .1726641 .0322439 5.35 0.000 .1059626 .2393656 Ica | .2686949 .0477181 5.63 0.000 .1699824 .3674074 Junín | .3583153 .0198413 18.06 0.000 .3172704 .3993602 La Libertad | .1886441 .0397651 4.74 0.000 .1063837 .2709045 Lambayeque | .1962144 .0427921 4.59 0.000 .1076923 .2847365 Loreto | .0294425 .0234186 1.26 0.221 -.0190026 .0778875 Madre de Dios | .0202542 .0276675 0.73 0.472 -.0369803 .0774887 Moquegua | -.3406279 .0110701 -30.77 0.000 -.3635282 -.3177275 Pasco | -.3004189 .0127116 -23.63 0.000 -.3267149 -.2741228 Piura | .0469183 .0258608 1.81 0.083 -.0065788 .1004154 Puno | .0943043 .0274078 3.44 0.002 .0376068 .1510017 San Martín | .1716453 .0278115 6.17 0.000 .1141128 .2291779 Tacna | .0519716 .0200243 2.60 0.016 .0105482 .093395 Tumbes | .2141541 .0580996 3.69 0.001 .0939659 .3343423 Ucayali | .06505 .0201189 3.23 0.004 .0234308 .1066693 Áncash | -.1433421 .0222188 -6.45 0.000 -.1893052 -.0973789 | id#c.ln_salud | Amazonas | -.1354905 .0692011 -1.96 0.062 -.2786439 .0076629 Apurímac | .3345131 .0114564 29.20 0.000 .3108137 .3582125 Arequipa | -.0709841 .0048994 -14.49 0.000 -.0811192 -.060849 Ayacucho | .0892449 .0208987 4.27 0.000 .0460127 .1324771 Cajamarca | .0908407 .0235809 3.85 0.001 .0420598 .1396216 Cusco | -.3182929 .0214596 -14.83 0.000 -.3626855 -.2739002 Huancavelica | -.182198 .0715461 -2.55 0.018 -.3302024 -.0341935 Huánuco | -.1235633 .0291889 -4.23 0.000 -.1839452 -.0631815 Ica | -.0758639 .0180329 -4.21 0.000 -.1131677 -.03856 Junín | -.0447417 .0023256 -19.24 0.000 -.0495526 -.0399308 La Libertad | -.0805698 .0035989 -22.39 0.000 -.0880146 -.0731249 Lambayeque | -.0328874 .0141035 -2.33 0.029 -.0620626 -.0037122 Loreto | .224672 .0546491 4.11 0.000 .1116216 .3377223 Madre de Dios | .1483617 .0090117 16.46 0.000 .1297196 .1670037 Moquegua | -.0404466 .0015996 -25.29 0.000 -.0437557 -.0371376 Pasco | .3263746 .0407563 8.01 0.000 .2420638 .4106854 Piura | -.048961 .0073588 -6.65 0.000 -.0641838 -.0337383 Puno | -.0980472 .013065 -7.50 0.000 -.1250742 -.0710202 San Martín | -.3521583 .0294337 -11.96 0.000 -.4130466 -.2912701 Tacna | .0726015 .0458199 1.58 0.127 -.0221842 .1673871 Tumbes | .25387 .0332515 7.63 0.000 .1850841 .322656 Ucayali | -.036994 .0163216 -2.27 0.033 -.0707578 -.0032302 Áncash | -.1694466 .0194702 -8.70 0.000 -.2097237 -.1291694 | id#c.ln_edu | Amazonas | -.184267 .1122227 -1.64 0.114 -.4164174 .0478835 Apurímac | -1.053295 .0687137 -15.33 0.000 -1.19544 -.91115 Arequipa | -.2254419 .1099798 -2.05 0.052 -.4529525 .0020687 Ayacucho | -.3767622 .1046721 -3.60 0.002 -.5932928 -.1602315 Cajamarca | .059142 .0808467 0.73 0.472 -.1081023 .2263862 Cusco | -.1109195 .1143567 -0.97 0.342 -.3474844 .1256454 Huancavelica | -.1753583 .0811308 -2.16 0.041 -.3431903 -.0075264 Huánuco | -.0661685 .1333885 -0.50 0.625 -.3421036 .2097667 Ica | -.1840886 .1251192 -1.47 0.155 -.4429173 .0747401 Junín | -.4483017 .1008563 -4.44 0.000 -.6569388 -.2396646 La Libertad | -.2187196 .1093808 -2.00 0.057 -.4449909 .0075518 Lambayeque | -.1633516 .1259868 -1.30 0.208 -.4239752 .097272 Loreto | -.6967474 .1433866 -4.86 0.000 -.9933652 -.4001295 Madre de Dios | -.6144027 .1408009 -4.36 0.000 -.9056716 -.3231338 Moquegua | .3102817 .1202008 2.58 0.017 .0616274 .5589361 Pasco | -.2350489 .1042438 -2.25 0.034 -.4506936 -.0194041 Piura | -.2424484 .1389755 -1.74 0.094 -.5299412 .0450444 Puno | -.2188596 .0964796 -2.27 0.033 -.4184429 -.0192763 San Martín | -.2437405 .1024754 -2.38 0.026 -.455727 -.031754 Tacna | -.2268031 .1335765 -1.70 0.103 -.5031272 .0495209 Tumbes | -.2946518 .1386928 -2.12 0.045 -.5815596 -.007744 Ucayali | -.1751879 .115516 -1.52 0.143 -.414151 .0637752 Áncash | -.060665 .0900927 -0.67 0.507 -.247036 .125706 | _cons | 8.011457 .3735159 21.45 0.000 7.23878 8.784133 --------------------------------------------------------------------------------
Code:xtreg lnY ln_K ln_khum $xlist $infr b13.id#c.( $infr ) , re theta vce(robust)
Code:Random-effects GLS regression Number of obs = 504 Group variable: id Number of groups = 24 R-sq: Obs per group: within = 0.9118 min = 21 between = 1.0000 avg = 21.0 overall = 0.9971 max = 21 Wald chi2(3) = . corr(u_i, X) = 0 (assumed) Prob > chi2 = . theta = 0 (Std. Err. adjusted for 24 clusters in id) -------------------------------------------------------------------------------- | Robust lnY | Coef. Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- ln_K | .0392785 .031809 1.23 0.217 -.0230661 .1016231 ln_khum | .0609317 .039548 1.54 0.123 -.0165809 .1384444 ln_lineas | .0382541 .0365162 1.05 0.295 -.0333163 .1098245 ln_poten | .3309151 .0256899 12.88 0.000 .2805638 .3812665 ln_km | -.02535 .0245589 -1.03 0.302 -.0734846 .0227845 ln_salud | .0660992 .0018572 35.59 0.000 .0624593 .0697392 ln_edu | .4252657 .0995293 4.27 0.000 .2301918 .6203396 | id#c.ln_lineas | Amazonas | .065404 .0442473 1.48 0.139 -.0213191 .1521271 Apurímac | .0946574 .0243155 3.89 0.000 .0469999 .1423149 Arequipa | .1536096 .0417419 3.68 0.000 .0717971 .2354222 Ayacucho | .0669445 .037277 1.80 0.073 -.006117 .140006 Cajamarca | -.0573588 .0232117 -2.47 0.013 -.1028528 -.0118648 Cusco | .1397357 .0432859 3.23 0.001 .0548968 .2245745 Huancavelica | -.0613288 .0132292 -4.64 0.000 -.0872577 -.0354 Huánuco | .0118944 .0528762 0.22 0.822 -.0917412 .1155299 Ica | .1273962 .0487523 2.61 0.009 .0318434 .222949 Junín | .0615422 .0411515 1.50 0.135 -.0191133 .1421977 La Libertad | .1186316 .0398124 2.98 0.003 .0406006 .1966625 Lambayeque | .0322334 .0442022 0.73 0.466 -.0544012 .1188681 Loreto | -.0145669 .0405126 -0.36 0.719 -.0939701 .0648364 Madre de Dios | .0476139 .0486835 0.98 0.328 -.047804 .1430317 Moquegua | -.0383305 .036807 -1.04 0.298 -.1104709 .03381 Pasco | -.0778414 .0303786 -2.56 0.010 -.1373824 -.0183004 Piura | .1136678 .0538 2.11 0.035 .0082217 .219114 Puno | .0162516 .0349408 0.47 0.642 -.0522311 .0847342 San Martín | .0757022 .0364599 2.08 0.038 .0042421 .1471623 Tacna | .0603895 .042311 1.43 0.154 -.0225386 .1433176 Tumbes | .0996731 .0568279 1.75 0.079 -.0117075 .2110536 Ucayali | .0441944 .0386813 1.14 0.253 -.0316197 .1200084 Áncash | .0028638 .0375553 0.08 0.939 -.0707433 .0764709 | id#c.ln_poten | Amazonas | -.1832356 .0544154 -3.37 0.001 -.2898879 -.0765834 Apurímac | -2.168464 .0621639 -34.88 0.000 -2.290303 -2.046624 Arequipa | -.0914937 .0327226 -2.80 0.005 -.1556288 -.0273585 Ayacucho | -.2981789 .0463234 -6.44 0.000 -.3889711 -.2073868 Cajamarca | -.7506572 .0192499 -39.00 0.000 -.7883864 -.7129281 Cusco | -.3565641 .0288598 -12.36 0.000 -.4131282 -.3 Huancavelica | -.269121 .0617575 -4.36 0.000 -.3901635 -.1480786 Huánuco | -.2810383 .0450281 -6.24 0.000 -.3692918 -.1927848 Ica | -.1507203 .0362095 -4.16 0.000 -.2216896 -.0797509 Junín | .4341032 .0194841 22.28 0.000 .395915 .4722914 La Libertad | -.3411277 .04063 -8.40 0.000 -.4207612 -.2614943 Lambayeque | -.2703864 .0425167 -6.36 0.000 -.3537176 -.1870552 Loreto | -.2914384 .0549464 -5.30 0.000 -.3991313 -.1837454 Madre de Dios | -.3801749 .0494981 -7.68 0.000 -.4771895 -.2831603 Moquegua | -.4167558 .0374018 -11.14 0.000 -.4900621 -.3434496 Pasco | -.2483541 .0637836 -3.89 0.000 -.3733675 -.1233406 Piura | -.1107411 .0507632 -2.18 0.029 -.2102351 -.011247 Puno | -.2495404 .0329792 -7.57 0.000 -.3141783 -.1849024 San Martín | -.4251851 .0549771 -7.73 0.000 -.5329382 -.3174321 Tacna | -.1556492 .0873299 -1.78 0.075 -.3268127 .0155143 Tumbes | -.9941327 .1344387 -7.39 0.000 -1.257628 -.7306376 Ucayali | -.2154607 .0855361 -2.52 0.012 -.3831082 -.0478131 Áncash | -.0080315 .0189589 -0.42 0.672 -.0451903 .0291272 | id#c.ln_km | Amazonas | .182208 .0396818 4.59 0.000 .104433 .259983 Apurímac | 1.151643 .0461113 24.98 0.000 1.061267 1.24202 Arequipa | .2434194 .0238159 10.22 0.000 .1967411 .2900977 Ayacucho | .2697952 .0278456 9.69 0.000 .2152187 .3243716 Cajamarca | -.1364695 .0189734 -7.19 0.000 -.1736566 -.0992824 Cusco | .3099537 .0380797 8.14 0.000 .2353188 .3845886 Huancavelica | .2031439 .0940441 2.16 0.031 .0188209 .387467 Huánuco | .1726641 .0322439 5.35 0.000 .1094673 .2358609 Ica | .2686949 .0477181 5.63 0.000 .1751691 .3622207 Junín | .3583153 .0198413 18.06 0.000 .3194271 .3972036 La Libertad | .1886441 .0397651 4.74 0.000 .1107059 .2665823 Lambayeque | .1962144 .0427921 4.59 0.000 .1123435 .2800853 Loreto | .0294425 .0234186 1.26 0.209 -.0164571 .075342 Madre de Dios | .0202542 .0276675 0.73 0.464 -.0339731 .0744814 Moquegua | -.3406279 .0110701 -30.77 0.000 -.3623249 -.3189308 Pasco | -.3004189 .0127116 -23.63 0.000 -.3253332 -.2755045 Piura | .0469183 .0258608 1.81 0.070 -.0037679 .0976045 Puno | .0943043 .0274078 3.44 0.001 .0405859 .1480227 San Martín | .1716453 .0278115 6.17 0.000 .1171357 .226155 Tacna | .0519716 .0200243 2.60 0.009 .0127247 .0912185 Tumbes | .2141541 .0580996 3.69 0.000 .100281 .3280273 Ucayali | .06505 .0201189 3.23 0.001 .0256176 .1044825 Áncash | -.1433421 .0222188 -6.45 0.000 -.1868902 -.0997939 | id#c.ln_salud | Amazonas | -.1354905 .0692011 -1.96 0.050 -.2711222 .0001412 Apurímac | .3345131 .0114564 29.20 0.000 .312059 .3569672 Arequipa | -.0709841 .0048994 -14.49 0.000 -.0805867 -.0613815 Ayacucho | .0892449 .0208987 4.27 0.000 .0482842 .1302056 Cajamarca | .0908407 .0235809 3.85 0.000 .0446229 .1370585 Cusco | -.3182929 .0214596 -14.83 0.000 -.360353 -.2762327 Huancavelica | -.182198 .0715461 -2.55 0.011 -.3224258 -.0419701 Huánuco | -.1235633 .0291889 -4.23 0.000 -.1807726 -.0663541 Ica | -.0758639 .0180329 -4.21 0.000 -.1112076 -.0405201 Junín | -.0447417 .0023256 -19.24 0.000 -.0492998 -.0401836 La Libertad | -.0805698 .0035989 -22.39 0.000 -.0876234 -.0735161 Lambayeque | -.0328874 .0141035 -2.33 0.020 -.0605297 -.0052451 Loreto | .224672 .0546491 4.11 0.000 .1175616 .3317823 Madre de Dios | .1483617 .0090117 16.46 0.000 .1306991 .1660242 Moquegua | -.0404466 .0015996 -25.29 0.000 -.0435818 -.0373114 Pasco | .3263746 .0407563 8.01 0.000 .2464937 .4062555 Piura | -.048961 .0073588 -6.65 0.000 -.063384 -.0345381 Puno | -.0980472 .013065 -7.50 0.000 -.1236541 -.0724402 San Martín | -.3521583 .0294337 -11.96 0.000 -.4098474 -.2944693 Tacna | .0726015 .0458199 1.58 0.113 -.0172039 .1624068 Tumbes | .25387 .0332515 7.63 0.000 .1886983 .3190418 Ucayali | -.036994 .0163216 -2.27 0.023 -.0689838 -.0050043 Áncash | -.1694466 .0194702 -8.70 0.000 -.2076075 -.1312857 | id#c.ln_edu | Amazonas | -.184267 .1122227 -1.64 0.101 -.4042195 .0356856 Apurímac | -1.053295 .0687137 -15.33 0.000 -1.187972 -.9186187 Arequipa | -.2254419 .1099798 -2.05 0.040 -.4409983 -.0098854 Ayacucho | -.3767622 .1046721 -3.60 0.000 -.5819156 -.1716087 Cajamarca | .059142 .0808467 0.73 0.464 -.0993147 .2175987 Cusco | -.1109195 .1143567 -0.97 0.332 -.3350545 .1132155 Huancavelica | -.1753583 .0811308 -2.16 0.031 -.3343719 -.0163448 Huánuco | -.0661685 .1333885 -0.50 0.620 -.3276052 .1952682 Ica | -.1840886 .1251192 -1.47 0.141 -.4293177 .0611404 Junín | -.4483017 .1008563 -4.44 0.000 -.6459764 -.250627 La Libertad | -.2187196 .1093808 -2.00 0.046 -.4331019 -.0043372 Lambayeque | -.1633516 .1259868 -1.30 0.195 -.4102812 .083578 Loreto | -.6967474 .1433866 -4.86 0.000 -.97778 -.4157147 Madre de Dios | -.6144027 .1408009 -4.36 0.000 -.8903674 -.338438 Moquegua | .3102817 .1202008 2.58 0.010 .0746924 .545871 Pasco | -.2350489 .1042438 -2.25 0.024 -.439363 -.0307348 Piura | -.2424484 .1389755 -1.74 0.081 -.5148354 .0299387 Puno | -.2188596 .0964796 -2.27 0.023 -.4079562 -.029763 San Martín | -.2437405 .1024754 -2.38 0.017 -.4445885 -.0428924 Tacna | -.2268031 .1335765 -1.70 0.090 -.4886082 .035002 Tumbes | -.2946518 .1386928 -2.12 0.034 -.5664846 -.022819 Ucayali | -.1751879 .115516 -1.52 0.129 -.4015951 .0512193 Áncash | -.060665 .0900927 -0.67 0.501 -.2372435 .1159135 | _cons | 8.011457 .3735159 21.45 0.000 7.279379 8.743534 ---------------+---------------------------------------------------------------- sigma_u | 0 sigma_e | .07397206 rho | 0 (fraction of variance due to u_i) --------------------------------------------------------------------------------Comment
-
Sam:
you actualy have a pooled OLS with no panel-wise effect (did you run -xttest0- after -xtreg,re-?).
That said, I'd go with a more parsimonius model and see if the sitation still remains the same.Kind regards,
Carlo
(Stata 19.0)Comment
Comment