I'm trying to identify the impact of government policy to market shares liquidity
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I'm trying to identify the impact of government policy on market shares liquidityComment
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Akhzan:
1) -testparm- (that returns -chi2- instead of -F-) was conceived to test the joint statistical signbiifcance of categorical variables (e.g., -i.year-);
2) see the hopefully helpful following toy-example:
Code:. use "https://www.stata-press.com/data/r17/grunfeld.dta" . xtset company year Panel variable: company (strongly balanced) Time variable: year, 1935 to 1954 Delta: 1 year . xtgls invest i.year i.company mvalue kstock Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: homoskedastic Correlation: no autocorrelation Estimated covariances = 1 Number of obs = 200 Estimated autocorrelations = 0 Number of groups = 10 Estimated coefficients = 31 Time periods = 20 Wald chi2(30) = 3940.22 Log likelihood = -1056.132 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ invest | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- year | 1936 | -19.19741 21.76377 -0.88 0.378 -61.85361 23.4588 1937 | -40.69001 22.70098 -1.79 0.073 -85.18311 3.803096 1938 | -39.2264 21.35937 -1.84 0.066 -81.09001 2.6372 1939 | -69.47029 21.74558 -3.19 0.001 -112.0908 -26.84973 1940 | -44.23507 21.88689 -2.02 0.043 -87.13258 -1.337565 1941 | -18.80446 21.78044 -0.86 0.388 -61.49335 23.88442 1942 | -21.13979 21.4933 -0.98 0.325 -63.26589 20.9863 1943 | -42.97762 21.65071 -1.99 0.047 -85.41223 -.5430099 1944 | -43.09876 21.70341 -1.99 0.047 -85.63666 -.5608657 1945 | -55.68303 21.96578 -2.53 0.011 -98.73516 -12.6309 1946 | -31.16928 22.16835 -1.41 0.160 -74.61845 12.27988 1947 | -39.39223 21.86288 -1.80 0.072 -82.2427 3.458228 1948 | -43.71651 22.03384 -1.98 0.047 -86.90204 -.5309879 1949 | -73.4951 22.22988 -3.31 0.001 -117.0649 -29.92534 1950 | -75.89611 22.37935 -3.39 0.001 -119.7588 -32.03338 1951 | -62.4809 22.85619 -2.73 0.006 -107.2782 -17.6836 1952 | -64.63233 23.30225 -2.77 0.006 -110.3039 -18.96077 1953 | -67.71796 24.46194 -2.77 0.006 -115.6625 -19.77343 1954 | -93.52622 24.9186 -3.75 0.000 -142.3658 -44.68666 | company | 2 | 207.0542 32.33215 6.40 0.000 143.6844 270.4241 3 | -135.2308 32.82507 -4.12 0.000 -199.5668 -70.89486 4 | 95.3538 46.62574 2.05 0.041 3.969043 186.7386 5 | -5.438636 53.16006 -0.10 0.919 -109.6304 98.75316 6 | 102.8886 49.79873 2.07 0.039 5.284886 200.4923 7 | 51.46657 53.4806 0.96 0.336 -53.35347 156.2866 8 | 67.49048 46.85445 1.44 0.150 -24.34256 159.3235 9 | 30.21752 51.22281 0.59 0.555 -70.17734 130.6124 10 | 126.8371 53.79886 2.36 0.018 21.39324 232.2809 | mvalue | .1177158 .0126407 9.31 0.000 .0929405 .1424912 kstock | .3579163 .0208842 17.14 0.000 .316984 .3988485 _cons | -86.90019 51.52024 -1.69 0.092 -187.878 14.07762 ------------------------------------------------------------------------------ . testparm i.year ( 1) 1936.year = 0 ( 2) 1937.year = 0 ( 3) 1938.year = 0 ( 4) 1939.year = 0 ( 5) 1940.year = 0 ( 6) 1941.year = 0 ( 7) 1942.year = 0 ( 8) 1943.year = 0 ( 9) 1944.year = 0 (10) 1945.year = 0 (11) 1946.year = 0 (12) 1947.year = 0 (13) 1948.year = 0 (14) 1949.year = 0 (15) 1950.year = 0 (16) 1951.year = 0 (17) 1952.year = 0 (18) 1953.year = 0 (19) 1954.year = 0 chi2( 19) = 31.55 Prob > chi2 = 0.0351 . testparm i.company ( 1) 2.company = 0 ( 2) 3.company = 0 ( 3) 4.company = 0 ( 4) 5.company = 0 ( 5) 6.company = 0 ( 6) 7.company = 0 ( 7) 8.company = 0 ( 8) 9.company = 0 ( 9) 10.company = 0 chi2( 9) = 557.71 Prob > chi2 = 0.0000 .Kind regards,
Carlo
(Stata 19.0)Comment
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I think the case is different with mineComment
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Kindly need your help..
can I have step by step procedure to running my data in stata please? mine is panel data set, T>N, predictors were denoted dummies.
after -xtset- command, I thought I should determine which estimation model best for my data (common effect, fixed effect, or random effect), so I need to perform hausman test
since I'm dealing with T>N dataset, so I have to use -xtregar- or -xtgls- right?
xtregar y x, fe
xtregar y x, re
estimates store fe
estimates store fe
hausman fe re
is that right? because the result of hausman test was kind of strange:
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fe re Difference Std. err.
-------------+----------------------------------------------------------------
psbb | .0180501 .0180501 0 0
psbbt | .1706298 .1706298 0 0
ppkm | .2054497 .2054497 0 0
ppkmm | .042229 .042229 0 0
vaccine | .0501037 .0501037 0 0
c1 | 2.299816 2.299816 0 0
c2 | .2397661 .2397661 0 0
c3 | 2.595502 2.595502 0 0
c4 | 1.249913 1.249913 0 0
c5_ | 9.49e-06 9.49e-06 0 0
------------------------------------------------------------------------------
b = Consistent under H0 and Ha; obtained from xtregar.
B = Inconsistent under Ha, efficient under H0; obtained from xtregar.
Test of H0: Difference in coefficients not systematic
chi2(0) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 0.00
Prob > chi2 = .
(V_b-V_B is not positive definite)
I've tried using hausman fe re, sigmamore but the result is just the same
when I used stata 17, its work by using sigmamore..
help me please
Best regards,
Akhzan
Comment
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Oh I'm really sorry.. the sequence of my command was wrong.. I think that's why the output is kind of strange
I just realized that I have to -est store fe- right after running the -xtreg , fe- command (what I did before: -xtreg,fe- -xtreg,re- -est store fe- -est store re- )
but because my data is T>N so I have to use -xtregar- right?
now: -xtregar,fe- -est store fe- -xtregar,re- -est store re- -hasuman fe re-
the result of hausman test: used random effect
so can I use the output from -xtregar,re- instead of -xtgls- ?
but the value of coef and P > |z| is different to the previously suggestion you gave: -xtgls i.firm i.time, panels(hetero)
Kindly need your help
Thank you
Best regards,
Akhzan
Comment
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here is the output from stata:
Code:. xtregar turn psbb psbbt ppkm ppkmm ppkmd vaccine c1 c2 c3 c4 c5_, re note: ppkmd omitted because of collinearity. RE GLS regression with AR(1) disturbances Number of obs = 12,274 Group variable: shares_ Number of groups = 38 R-squared: Obs per group: Within = 0.1819 min = 323 Between = 0.3049 avg = 323.0 Overall = 0.1609 max = 323 Wald chi2(11) = 1977.13 corr(u_i, Xb) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ turn | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- psbb | .0180501 .05001 0.36 0.718 -.0799677 .1160678 psbbt | .1706298 .0449321 3.80 0.000 .0825645 .2586952 ppkm | .2054497 .0300301 6.84 0.000 .1465918 .2643075 ppkmm | .042229 .0218882 1.93 0.054 -.000671 .085129 ppkmd | 0 (omitted) vaccine | .0501037 .0402013 1.25 0.213 -.0286895 .1288968 c1 | 2.299816 .0588494 39.08 0.000 2.184474 2.415159 c2 | .2397661 .0410236 5.84 0.000 .1593614 .3201709 c3 | 2.595502 .0637002 40.75 0.000 2.470651 2.720352 c4 | 1.249913 .1782048 7.01 0.000 .900638 1.599188 c5_ | 9.49e-06 4.30e-06 2.21 0.027 1.07e-06 .0000179 _cons | .0451632 .059782 0.76 0.450 -.0720075 .1623338 -------------+---------------------------------------------------------------- rho_ar | .59212034 (estimated autocorrelation coefficient) sigma_u | .23459185 sigma_e | .28530424 rho_fov | .40337602 (fraction of variance due to u_i) theta | .83704245 ------------------------------------------------------------------------------Comment
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and below is the output of your previous suggestion: -xtgls i.firm i.time, panels(hetero)
Code:. xtgls turn psbb psbbt ppkm ppkmm ppkmd vaccine c1 c2 c3 c4 c5_ i.shares_ i.index, panels( > hetero) note: ppkmd omitted because of collinearity. note: 177.index omitted because of collinearity. note: 179.index omitted because of collinearity. note: 198.index omitted because of collinearity. note: 294.index omitted because of collinearity. note: 323.index omitted because of collinearity. Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: heteroskedastic Correlation: no autocorrelation Estimated covariances = 38 Number of obs = 12,274 Estimated autocorrelations = 0 Number of groups = 38 Estimated coefficients = 365 Time periods = 323 Wald chi2(364) = 9245.41 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ turn | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- psbb | -.2479266 .0594537 -4.17 0.000 -.3644537 -.1313995 psbbt | -.026166 .0592532 -0.44 0.659 -.1423001 .089968 ppkm | .0214051 .0418949 0.51 0.609 -.0607074 .1035177 ppkmm | -.0101935 .0418732 -0.24 0.808 -.0922634 .0718764 ppkmd | 0 (omitted) vaccine | -.1386944 .0418823 -3.31 0.001 -.2207823 -.0566065 c1 | .7095683 .0819241 8.66 0.000 .5489999 .8701366 c2 | .4140268 .0778993 5.31 0.000 .2613469 .5667066 c3 | 3.519221 .107323 32.79 0.000 3.308872 3.72957 c4 | 3.276047 .1968756 16.64 0.000 2.890178 3.661916 c5_ | 3.99e-06 1.62e-06 2.47 0.014 8.19e-07 7.17e-06 | shares_ | ADRO | .1567459 .0161576 9.70 0.000 .1250775 .1884142 AKRA | .3456302 .0207402 16.66 0.000 .3049801 .3862802 ANTM | 1.012777 .0671048 15.09 0.000 .8812538 1.1443 ASII | .0214949 .0151069 1.42 0.155 -.0081141 .0511039 BBCA | -.0099883 .015475 -0.65 0.519 -.0403187 .0203422 BBNI | .138481 .01464 9.46 0.000 .1097872 .1671749 BBRI | .0198876 .0154591 1.29 0.198 -.0104117 .050187 BBTN | .5195307 .0317877 16.34 0.000 .4572279 .5818336 BMRI | .0077437 .0150455 0.51 0.607 -.0217449 .0372323 BSDE | .0653923 .017743 3.69 0.000 .0306166 .100168 CPIN | -.0736912 .0158706 -4.64 0.000 -.1047971 -.0425853 ERAA | .5936801 .0293711 20.21 0.000 .5361138 .6512463 EXCL | .1669658 .0195904 8.52 0.000 .1285692 .2053623 GGRM | -.0015971 .0153077 -0.10 0.917 -.0315997 .0284055 HMSP | -.0428279 .0149413 -2.87 0.004 -.0721122 -.0135435 ICBP | .0125827 .0149878 0.84 0.401 -.0167928 .0419583 INCO | .0824822 .0159414 5.17 0.000 .0512376 .1137268 INDF | .0415381 .0145644 2.85 0.004 .0129924 .0700837 INKP | -.0123269 .0171191 -0.72 0.471 -.0458797 .0212259 INTP | -.0282624 .0157945 -1.79 0.074 -.0592191 .0026943 ITMG | .2074761 .0165942 12.50 0.000 .174952 .2400002 JPFA | .1200102 .0171412 7.00 0.000 .0864141 .1536062 JSMR | .0193347 .0158455 1.22 0.222 -.0117219 .0503914 KLBF | -.0007882 .0154051 -0.05 0.959 -.0309818 .0294053 MNCN | .3866137 .0227472 17.00 0.000 .3420299 .4311974 PGAS | .5244284 .0269931 19.43 0.000 .471523 .5773338 PTBA | .2784275 .0197414 14.10 0.000 .239735 .3171199 PTPP | .6950868 .0370324 18.77 0.000 .6225047 .7676689 PWON | .07405 .0176472 4.20 0.000 .0394622 .1086379 SMGR | -.0403054 .0163978 -2.46 0.014 -.0724446 -.0081662 TBIG | .0778167 .0194161 4.01 0.000 .0397619 .1158716 TKIM | .1496146 .0237221 6.31 0.000 .1031202 .196109 TLKM | .0368024 .0145671 2.53 0.012 .0082514 .0653534 TOWR | .1452118 .0222461 6.53 0.000 .1016101 .1888134 UNTR | -.0072614 .015832 -0.46 0.646 -.0382916 .0237687 UNVR | -.0277639 .0145281 -1.91 0.056 -.0562384 .0007107 WIKA | .2838796 .0193459 14.67 0.000 .2459623 .321797 | index | 2 | -.0164333 .041973 -0.39 0.695 -.0986989 .0658323 3 | .0633641 .0420069 1.51 0.131 -.0189678 .1456961 4 | .0228136 .0423114 0.54 0.590 -.0601152 .1057424 5 | .0029267 .0422616 0.07 0.945 -.0799046 .0857579 6 | .0438443 .0419733 1.04 0.296 -.0384218 .1261104 7 | .0296989 .0419618 0.71 0.479 -.0525448 .1119425 8 | .0472212 .0419696 1.13 0.261 -.0350377 .1294801 9 | -2.656188 .1568928 -16.93 0.000 -2.963692 -2.348684 10 | -.1643386 .0984125 -1.67 0.095 -.3572236 .0285465 11 | -.5695717 .0552253 -10.31 0.000 -.6778112 -.4613322 12 | -.5849775 .0548904 -10.66 0.000 -.6925608 -.4773942 13 | -.5823649 .0550013 -10.59 0.000 -.6901655 -.4745644 14 | -.595255 .0547715 -10.87 0.000 -.7026052 -.4879049 15 | .0110335 .0421765 0.26 0.794 -.0716309 .0936979 16 | .0724809 .0420019 1.73 0.084 -.0098413 .1548031 17 | .0729441 .0418997 1.74 0.082 -.0091777 .155066 18 | .09392 .0419133 2.24 0.025 .0117714 .1760687 19 | .0760558 .0419323 1.81 0.070 -.0061301 .1582416 20 | .1235203 .0420207 2.94 0.003 .0411613 .2058794 21 | .1401788 .0420485 3.33 0.001 .0577653 .2225923 22 | .1050693 .0420561 2.50 0.012 .0226409 .1874976 23 | .1136693 .0419927 2.71 0.007 .0313651 .1959734 24 | .1105857 .0420062 2.63 0.008 .0282551 .1929163 25 | .098762 .0420021 2.35 0.019 .0164395 .1810846 26 | -.1472308 .0419516 -3.51 0.000 -.2294545 -.0650072 27 | -.157334 .0419635 -3.75 0.000 -.239581 -.075087 28 | -.1041594 .041906 -2.49 0.013 -.1862937 -.0220251 29 | .0591943 .0419252 1.41 0.158 -.0229776 .1413662 30 | .2734219 .041944 6.52 0.000 .1912131 .3556307 31 | -.0537195 .0420002 -1.28 0.201 -.1360384 .0285994 32 | -.013009 .0420081 -0.31 0.757 -.0953433 .0693253 33 | .0386777 .0420278 0.92 0.357 -.0436953 .1210507 34 | -.063126 .0419885 -1.50 0.133 -.145422 .01917 35 | -.0431907 .0419465 -1.03 0.303 -.1254043 .039023 36 | -.0128416 .0419842 -0.31 0.760 -.095129 .0694458 37 | -.0236805 .0422163 -0.56 0.575 -.106423 .0590619 38 | -.001103 .0421604 -0.03 0.979 -.0837359 .0815299 39 | -.0214524 .0419831 -0.51 0.609 -.1037377 .0608329 40 | -.0806652 .0419789 -1.92 0.055 -.1629424 .001612 41 | -.1842535 .0420627 -4.38 0.000 -.2666948 -.1018121 42 | -.0964181 .0420157 -2.29 0.022 -.1787674 -.0140688 43 | -.0974299 .0420053 -2.32 0.020 -.1797588 -.0151009 44 | -.0625694 .0419785 -1.49 0.136 -.1448458 .0197071 45 | -.1058322 .0419389 -2.52 0.012 -.188031 -.0236334 46 | -.1148346 .0419739 -2.74 0.006 -.1971019 -.0325674 47 | -.0721072 .0419132 -1.72 0.085 -.1542557 .0100412 48 | -.0957255 .0419697 -2.28 0.023 -.1779846 -.0134665 49 | -.0897628 .0419552 -2.14 0.032 -.1719934 -.0075321 50 | -.0937705 .0418992 -2.24 0.025 -.1758915 -.0116495 51 | -.0335735 .0419112 -0.80 0.423 -.115718 .0485711 52 | -.0715093 .0419032 -1.71 0.088 -.1536381 .0106194 53 | -.0275196 .0418832 -0.66 0.511 -.1096091 .05457 54 | -.062584 .0418893 -1.49 0.135 -.1446856 .0195176 55 | -.0767096 .0418908 -1.83 0.067 -.1588139 .0053948 56 | -.0465572 .0418962 -1.11 0.266 -.1286722 .0355578 57 | -.0578834 .0418828 -1.38 0.167 -.1399722 .0242053 58 | -.0069819 .0419019 -0.17 0.868 -.0891082 .0751444 59 | -.0790195 .0419516 -1.88 0.060 -.1612432 .0032042 60 | -.0875552 .0419091 -2.09 0.037 -.1696956 -.0054149 61 | -.0641905 .0418816 -1.53 0.125 -.1462769 .0178959 62 | -.0528815 .0419001 -1.26 0.207 -.1350041 .0292411 63 | -.0732292 .0418884 -1.75 0.080 -.155329 .0088706 64 | -.0427773 .0419005 -1.02 0.307 -.1249007 .039346 65 | -.0551976 .0419481 -1.32 0.188 -.1374144 .0270193 66 | -.0304658 .0419181 -0.73 0.467 -.1126238 .0516922 67 | -.0334437 .0418881 -0.80 0.425 -.1155428 .0486555 68 | -.0110376 .0418861 -0.26 0.792 -.0931327 .0710576 69 | -.0706151 .0420228 -1.68 0.093 -.1529783 .011748 70 | -.1089259 .0419647 -2.60 0.009 -.1911752 -.0266765 71 | -.0710949 .0418905 -1.70 0.090 -.1531988 .0110089 72 | -.0755867 .041902 -1.80 0.071 -.157713 .0065396 73 | -.0560151 .0418882 -1.34 0.181 -.1381146 .0260843 74 | -.0352306 .0421378 -0.84 0.403 -.1178191 .0473579 75 | -.0757311 .042017 -1.80 0.071 -.1580828 .0066206 76 | -.0864983 .0418674 -2.07 0.039 -.1685568 -.0044397 77 | -.0901936 .0418708 -2.15 0.031 -.1722589 -.0081283 78 | -.0791766 .0419196 -1.89 0.059 -.1613374 .0029843 79 | -.1001405 .0419225 -2.39 0.017 -.182307 -.0179739 80 | -.0511012 .0418736 -1.22 0.222 -.1331719 .0309695 81 | -.0152267 .0418722 -0.36 0.716 -.0972948 .0668413 82 | -.0537655 .0418808 -1.28 0.199 -.1358503 .0283192 83 | -.0667456 .0418947 -1.59 0.111 -.1488577 .0153666 84 | -.02529 .0418799 -0.60 0.546 -.1073731 .0567932 85 | -.0372755 .0419051 -0.89 0.374 -.1194081 .044857 86 | -.0693989 .0419156 -1.66 0.098 -.151552 .0127542 87 | -.0612697 .0418854 -1.46 0.144 -.1433636 .0208241 88 | -.0544867 .0419005 -1.30 0.193 -.1366102 .0276367 89 | -.0737331 .0419092 -1.76 0.079 -.1558736 .0084075 90 | -.0467486 .041924 -1.12 0.265 -.1289181 .0354208 91 | .0436884 .0420435 1.04 0.299 -.0387154 .1260921 92 | -.0866189 .0419462 -2.07 0.039 -.168832 -.0044059 93 | -.0684753 .0418809 -1.64 0.102 -.1505603 .0136098 94 | -.0452769 .0419311 -1.08 0.280 -.1274604 .0369066 95 | -.0683159 .041947 -1.63 0.103 -.1505306 .0138987 96 | -.1022735 .0419097 -2.44 0.015 -.184415 -.0201321 97 | -.1051805 .0419028 -2.51 0.012 -.1873084 -.0230526 98 | -.0725066 .0420605 -1.72 0.085 -.1549437 .0099305 99 | -.0764249 .0428824 -1.78 0.075 -.1604728 .007623 100 | -.0786326 .0423296 -1.86 0.063 -.161597 .0043318 101 | -.2144803 .0419781 -5.11 0.000 -.2967559 -.1322048 102 | -.1736878 .042075 -4.13 0.000 -.2561533 -.0912223 103 | -.1702422 .0421189 -4.04 0.000 -.2527937 -.0876908 104 | -.1693562 .042099 -4.02 0.000 -.2518687 -.0868438 105 | -.1058084 .0419614 -2.52 0.012 -.1880513 -.0235655 106 | -.1256957 .0419555 -3.00 0.003 -.207927 -.0434643 107 | -.0978526 .0420026 -2.33 0.020 -.1801762 -.015529 108 | -.1035955 .0419531 -2.47 0.014 -.1858221 -.021369 109 | -.109248 .0420112 -2.60 0.009 -.1915885 -.0269074 110 | -.1577856 .0419571 -3.76 0.000 -.2400201 -.0755512 111 | -.1560831 .041927 -3.72 0.000 -.2382585 -.0739078 112 | -.1051881 .0419476 -2.51 0.012 -.187404 -.0229723 113 | -.1233976 .0419237 -2.94 0.003 -.2055666 -.0412287 114 | -.1904016 .0418887 -4.55 0.000 -.272502 -.1083012 115 | -.1407843 .0419375 -3.36 0.001 -.2229802 -.0585883 116 | -.147007 .0419286 -3.51 0.000 -.2291855 -.0648285 117 | -.112981 .0418741 -2.70 0.007 -.1950528 -.0309092 118 | -.1244386 .0418828 -2.97 0.003 -.2065273 -.0423498 119 | -.1237437 .0418804 -2.95 0.003 -.2058277 -.0416596 120 | -.108163 .0418937 -2.58 0.010 -.1902731 -.0260529 121 | -.0844072 .0418977 -2.01 0.044 -.1665252 -.0022891 122 | -.0725625 .0418953 -1.73 0.083 -.1546758 .0095509 123 | -.057659 .041892 -1.38 0.169 -.1397657 .0244478 124 | -.078867 .0419859 -1.88 0.060 -.1611578 .0034239 125 | -.0909237 .0419566 -2.17 0.030 -.1731571 -.0086904 126 | -.1044352 .0418834 -2.49 0.013 -.1865251 -.0223454 127 | -.0778422 .0419274 -1.86 0.063 -.1600183 .0043339 128 | -.0729473 .0419187 -1.74 0.082 -.1551065 .0092118 129 | -.0554659 .0419057 -1.32 0.186 -.1375996 .0266679 130 | -.0934868 .0419063 -2.23 0.026 -.1756216 -.0113519 131 | -.0879338 .0418973 -2.10 0.036 -.170051 -.0058165 132 | -.0467324 .0419296 -1.11 0.265 -.1289129 .0354481 133 | -.0353897 .0419467 -0.84 0.399 -.1176037 .0468243 134 | -.0949368 .0419025 -2.27 0.023 -.1770643 -.0128093 135 | -.0857403 .0419461 -2.04 0.041 -.1679532 -.0035274 136 | -.1305437 .0419692 -3.11 0.002 -.2128018 -.0482855 137 | -.0606797 .0418933 -1.45 0.147 -.1427891 .0214298 138 | -.0551393 .0418795 -1.32 0.188 -.1372217 .0269431 139 | -.0147265 .0418928 -0.35 0.725 -.096835 .0673819 140 | -.0032358 .0418892 -0.08 0.938 -.0853371 .0788654 141 | -.0942705 .0419328 -2.25 0.025 -.1764574 -.0120837 142 | -.0967276 .0419253 -2.31 0.021 -.1788997 -.0145555 143 | -.0566018 .0418788 -1.35 0.177 -.1386828 .0254792 144 | 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.0471059 .0419564 1.12 0.262 -.0351271 .1293389 314 | -.0151724 .0419225 -0.36 0.717 -.097339 .0669942 315 | -.0201353 .0418741 -0.48 0.631 -.1022071 .0619365 316 | -.0223106 .0418662 -0.53 0.594 -.1043667 .0597456 317 | .0043264 .041867 0.10 0.918 -.0777313 .0863842 318 | -.0161106 .0418681 -0.38 0.700 -.0981706 .0659495 319 | -.0033681 .0418827 -0.08 0.936 -.0854567 .0787205 320 | .003445 .0418784 0.08 0.934 -.0786351 .0855252 321 | -.0021673 .0418831 -0.05 0.959 -.0842567 .0799221 322 | -.0130658 .0418758 -0.31 0.755 -.0951408 .0690092 323 | 0 (omitted) | _cons | .0835242 .0529788 1.58 0.115 -.0203123 .1873606 ------------------------------------------------------------------------------
Comment
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better display (predictors variables only)
Code:Cross-sectional time-series FGLS regression Coefficients: generalized least squares Panels: heteroskedastic Correlation: no autocorrelation Estimated covariances = 38 Number of obs = 12,274 Estimated autocorrelations = 0 Number of groups = 38 Estimated coefficients = 365 Time periods = 323 Wald chi2(364) = 9245.41 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ turn | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- psbb | -.2479266 .0594537 -4.17 0.000 -.3644537 -.1313995 psbbt | -.026166 .0592532 -0.44 0.659 -.1423001 .089968 ppkm | .0214051 .0418949 0.51 0.609 -.0607074 .1035177 ppkmm | -.0101935 .0418732 -0.24 0.808 -.0922634 .0718764 ppkmd | 0 (omitted) vaccine | -.1386944 .0418823 -3.31 0.001 -.2207823 -.0566065 c1 | .7095683 .0819241 8.66 0.000 .5489999 .8701366 c2 | .4140268 .0778993 5.31 0.000 .2613469 .5667066 c3 | 3.519221 .107323 32.79 0.000 3.308872 3.72957 c4 | 3.276047 .1968756 16.64 0.000 2.890178 3.661916 c5_ | 3.99e-06 1.62e-06 2.47 0.014 8.19e-07 7.17e-06Comment
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for my dataset, which command is the proper one?
kindly need your help
Thank you very much
Best Regards,
AkhzanComment
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Oh I'm sorry.. I just realized.Originally posted by akhzan fasti View Post
So, the -testparm- (that returns -chi2- instead of -F-) was conceived to test the joint statistical significance of categorical variables (e.g., -i.year-)
so the command -i.firm- and -i.time- are used for categorical variables, right?
may I know the purpose of this command, please?
my dataset consisted of: 38 shares and 323 trading days
I'm trying to analyze the impact of government policies (X) on these shares (Y).
during 323 trading days, there were 6 policies were issued by the government.
these government policies was denoted dummies. 1 when issued and 0 when stopped/expired/replaced by the new one.
for my case, do I need the -i.firm- and -i.time- in my command?
Really need your help..
Thank you
Best Regards,
AkhzanComment
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here is the illustration of my dataset:
Attached FilesComment
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Oh I see..
I need i.time to inform stata that the time value is dummy
so, for my case, it should be i.x1,ix2, i.x3, i.x4 and so on right?
but all of my predictors were omitted
Code:. xtregar turn psbb psbbt ppkm ppkmm ppkmd vaccine c1 c2 c3 c4 c5_ i.psbb i.psbbt i.ppkm i.ppkmm i.ppkmd i.vaccine, re note: ppkmd omitted because of collinearity. note: 1.psbb omitted because of collinearity. note: 1.psbbt omitted because of collinearity. note: 1.ppkm omitted because of collinearity. note: 1.ppkmm omitted because of collinearity. note: 1.ppkmd omitted because of collinearity. note: 1.vaccine omitted because of collinearity. RE GLS regression with AR(1) disturbances Number of obs = 12,274 Group variable: shares_ Number of groups = 38 R-squared: Obs per group: Within = 0.1819 min = 323 Between = 0.3049 avg = 323.0 Overall = 0.1609 max = 323 Wald chi2(11) = 1977.13 corr(u_i, Xb) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ turn | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- psbb | .0180501 .05001 0.36 0.718 -.0799677 .1160678 psbbt | .1706298 .0449321 3.80 0.000 .0825645 .2586952 ppkm | .2054497 .0300301 6.84 0.000 .1465918 .2643075 ppkmm | .042229 .0218882 1.93 0.054 -.000671 .085129 ppkmd | 0 (omitted) vaccine | .0501037 .0402013 1.25 0.213 -.0286895 .1288968 c1 | 2.299816 .0588494 39.08 0.000 2.184474 2.415159 c2 | .2397661 .0410236 5.84 0.000 .1593614 .3201709 c3 | 2.595502 .0637002 40.75 0.000 2.470651 2.720352 c4 | 1.249913 .1782048 7.01 0.000 .900638 1.599188 c5_ | 9.49e-06 4.30e-06 2.21 0.027 1.07e-06 .0000179 1.psbb | 0 (omitted) 1.psbbt | 0 (omitted) 1.ppkm | 0 (omitted) 1.ppkmm | 0 (omitted) 1.ppkmd | 0 (omitted) 1.vaccine | 0 (omitted) _cons | .0451632 .059782 0.76 0.450 -.0720075 .1623338 -------------+---------------------------------------------------------------- rho_ar | .59212034 (estimated autocorrelation coefficient) sigma_u | .23459185 sigma_e | .28530424 rho_fov | .40337602 (fraction of variance due to u_i) theta | .83704245 ------------------------------------------------------------------------------Comment
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Oh I think I understand..
i.firm and i.time were performed to tell stata that this is panel data right? because we used -xtgls- not -xtregar-
if I used -xtregar- there is no need to add i.firm and i.time
Am I right?
or i.firm and i.time are used to tell stata that the value is dummy?
kindly need your guidance, please..
Thank You
Best Regards,
AkhzanComment
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