Meghna:
-xtset-ting data with -timevar- is mandatory if you plan to use, say, lags and leads. If time series commands are not part of your research strategy, you can safely skip -xtset-ting with -timevar-, as you can see from the following toy-example:
-xtset-ting data with -timevar- is mandatory if you plan to use, say, lags and leads. If time series commands are not part of your research strategy, you can safely skip -xtset-ting with -timevar-, as you can see from the following toy-example:
Code:
. use "http://www.stata-press.com/data/r15/nlswork.dta" (National Longitudinal Survey. Young Women 14-26 years of age in 1968) . xtset idcode panel variable: idcode (unbalanced) . xtreg ln_wage age i.year Random-effects GLS regression Number of obs = 28,510 Group variable: idcode Number of groups = 4,710 R-sq: Obs per group: within = 0.1060 min = 1 between = 0.0918 avg = 6.1 overall = 0.0807 max = 15 Wald chi2(15) = 3253.70 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ln_wage | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0137208 .0018898 7.26 0.000 .0100169 .0174247 | year | 69 | .0744312 .012506 5.95 0.000 .0499199 .0989425 70 | .0453659 .0120494 3.77 0.000 .0217496 .0689822 71 | .0819949 .0125373 6.54 0.000 .0574222 .1065676 72 | .0827461 .0136074 6.08 0.000 .056076 .1094162 73 | .0840751 .0143598 5.85 0.000 .0559304 .1122198 75 | .0707387 .0167492 4.22 0.000 .0379108 .1035665 77 | .1032639 .0197156 5.24 0.000 .064622 .1419059 78 | .1279039 .0214888 5.95 0.000 .0857866 .1700211 80 | .108871 .0247933 4.39 0.000 .060277 .157465 82 | .098831 .0280824 3.52 0.000 .0437906 .1538714 83 | .1127655 .0298539 3.78 0.000 .0542529 .1712781 85 | .1380611 .0333412 4.14 0.000 .0727135 .2034087 87 | .1264818 .0369222 3.43 0.001 .0541156 .198848 88 | .1640382 .0393563 4.17 0.000 .0869012 .2411752 | _cons | 1.162473 .03784 30.72 0.000 1.088308 1.236638 -------------+---------------------------------------------------------------- sigma_u | .36664367 sigma_e | .30300411 rho | .59418375 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . xtset idcode year panel variable: idcode (unbalanced) time variable: year, 68 to 88, but with gaps delta: 1 unit . xtreg ln_wage age i.year Random-effects GLS regression Number of obs = 28,510 Group variable: idcode Number of groups = 4,710 R-sq: Obs per group: within = 0.1060 min = 1 between = 0.0918 avg = 6.1 overall = 0.0807 max = 15 Wald chi2(15) = 3253.70 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ ln_wage | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0137208 .0018898 7.26 0.000 .0100169 .0174247 | year | 69 | .0744312 .012506 5.95 0.000 .0499199 .0989425 70 | .0453659 .0120494 3.77 0.000 .0217496 .0689822 71 | .0819949 .0125373 6.54 0.000 .0574222 .1065676 72 | .0827461 .0136074 6.08 0.000 .056076 .1094162 73 | .0840751 .0143598 5.85 0.000 .0559304 .1122198 75 | .0707387 .0167492 4.22 0.000 .0379108 .1035665 77 | .1032639 .0197156 5.24 0.000 .064622 .1419059 78 | .1279039 .0214888 5.95 0.000 .0857866 .1700211 80 | .108871 .0247933 4.39 0.000 .060277 .157465 82 | .098831 .0280824 3.52 0.000 .0437906 .1538714 83 | .1127655 .0298539 3.78 0.000 .0542529 .1712781 85 | .1380611 .0333412 4.14 0.000 .0727135 .2034087 87 | .1264818 .0369222 3.43 0.001 .0541156 .198848 88 | .1640382 .0393563 4.17 0.000 .0869012 .2411752 | _cons | 1.162473 .03784 30.72 0.000 1.088308 1.236638 -------------+---------------------------------------------------------------- sigma_u | .36664367 sigma_e | .30300411 rho | .59418375 (fraction of variance due to u_i) ------------------------------------------------------------------------------ .
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