Hello everybody
What is exactly the difference between those two commands?
Thank you in advance
What is exactly the difference between those two commands?
Thank you in advance
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reg Y X= OLS regression using cross sectional data
xtreg Y X, fe=fixed effects within estimation using panel data (cross sectional data observed over a period of time, so the data structure has added time dimension) -
Thank you for the answer. But how can I add the column of years (period of time), because after regressing it shows Group variable: firmcode , but in fact I need to add the variable Year and not the variable firmcodeComment
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Command: xtset firmcode Year (if your variable names are like this, frim 1 firm 2 firm 3 ... firm N, collected over Year 1, Year 2, ... Year N).
Alternatively: click on Statistics--Longitudinal/Panel data--Setup and utilities--Declare dataset to be panel data
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No, that will not do it.xtset firmcode Year
-xtset- will cause -xtreg, fe- (or -xtreg, re-) to recognize the firmcode variable as the fixed (or random) effect level. But it does not cause -xtreg, fe- to incorporate Year. To do that, you must explicitly include Year in the regression:
The only thing that include Year in the -xtset- command does is enable Stata to identify the time variable for purposes of calculating leads, lags, and differences, and to structure autoregressive correlation. If you're not using any of those things, there is no need to mention Year in -xtset-. But you do need to include i.Year in the -xtreg- variable list if you want Year to be included.Code:xtset firmcode Year xtreg dep_var indep_vars covariates i.Year, fe
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Dr Schechter thank you again !Comment
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Theofanis:
your question might be due to the fact that you can run fixed effect estimator using -regress- too (but -xtreg,fe- outperforms -regress- in this respect).
As you can see from the following toy-example, set aside the constant and panel-specific statistics, coefficients and standard errors are the same:
Code:. . use "https://www.stata-press.com/data/r17/nlswork.dta" (National Longitudinal Survey of Young Women, 14-24 years old in 1968) . regress ln_wage age i.idcode i.year if idcode<=3 Source | SS df MS Number of obs = 39 -------------+---------------------------------- F(17, 21) = 2.68 Model | 3.54194923 17 .208349955 Prob > F = 0.0171 Residual | 1.63378973 21 .077799511 R-squared = 0.6843 -------------+---------------------------------- Adj R-squared = 0.4288 Total | 5.17573896 38 .136203657 Root MSE = .27893 ------------------------------------------------------------------------------ ln_wage | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- age | .3010572 .3561559 0.85 0.407 -.4396095 1.041724 | idcode | 2 | -.3898423 .11632 -3.35 0.003 -.631743 -.1479415 3 | -2.247118 2.111457 -1.06 0.299 -6.638133 2.143897 | year | 69 | -.0920902 .5314565 -0.17 0.864 -1.197315 1.013134 70 | -.8648493 .779214 -1.11 0.280 -2.485314 .7556149 71 | -1.248506 1.09967 -1.14 0.269 -3.535396 1.038383 72 | -1.39387 1.443494 -0.97 0.345 -4.395779 1.60804 73 | -1.520276 1.79214 -0.85 0.406 -5.247236 2.206684 75 | -2.049717 2.495803 -0.82 0.421 -7.240024 3.14059 77 | -2.657565 3.203292 -0.83 0.416 -9.319175 4.004045 78 | -2.751196 3.557758 -0.77 0.448 -10.14996 4.647567 80 | -3.324016 4.267534 -0.78 0.445 -12.19884 5.550808 82 | -4.027975 4.983977 -0.81 0.428 -14.39272 6.336774 83 | -4.207353 5.333467 -0.79 0.439 -15.2989 6.884199 85 | -4.730657 6.044586 -0.78 0.443 -17.30106 7.839747 87 | -5.407995 6.755956 -0.80 0.432 -19.45777 8.641785 88 | -5.901929 7.348904 -0.80 0.431 -21.18481 9.380954 | _cons | -2.882579 5.734884 -0.50 0.620 -14.80892 9.043766 ------------------------------------------------------------------------------ . xtreg ln_wage age i.year if idcode<=3, fe Fixed-effects (within) regression Number of obs = 39 Group variable: idcode Number of groups = 3 R-squared: Obs per group: Within = 0.5596 min = 12 Between = 0.4744 avg = 13.0 Overall = 0.0413 max = 15 F(15,21) = 1.78 corr(u_i, Xb) = -0.9573 Prob > F = 0.1102 ------------------------------------------------------------------------------ ln_wage | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- age | .3010572 .3561559 0.85 0.407 -.4396095 1.041724 | year | 69 | -.0920902 .5314565 -0.17 0.864 -1.197315 1.013134 70 | -.8648493 .779214 -1.11 0.280 -2.485314 .7556149 71 | -1.248506 1.09967 -1.14 0.269 -3.535396 1.038383 72 | -1.39387 1.443494 -0.97 0.345 -4.395779 1.60804 73 | -1.520276 1.79214 -0.85 0.406 -5.247236 2.206684 75 | -2.049717 2.495803 -0.82 0.421 -7.240024 3.14059 77 | -2.657565 3.203292 -0.83 0.416 -9.319175 4.004045 78 | -2.751196 3.557758 -0.77 0.448 -10.14996 4.647567 80 | -3.324016 4.267534 -0.78 0.445 -12.19884 5.550808 82 | -4.027975 4.983977 -0.81 0.428 -14.39272 6.336774 83 | -4.207353 5.333467 -0.79 0.439 -15.2989 6.884199 85 | -4.730657 6.044586 -0.78 0.443 -17.30106 7.839747 87 | -5.407995 6.755956 -0.80 0.432 -19.45777 8.641785 88 | -5.901929 7.348904 -0.80 0.431 -21.18481 9.380954 | _cons | -3.866807 6.544144 -0.59 0.561 -17.4761 9.742485 -------------+---------------------------------------------------------------- sigma_u | 1.2007631 sigma_e | .27892564 rho | .9488037 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(2, 21) = 6.09 Prob > F = 0.0082 . .Kind regards,
Carlo
(Stata 19.0)Comment
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Thanks Carlo, I really appreciate your helpComment
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