WOOLDRIDGE CROSS-SECTION & PANEL DATA ECONOMETRICS– EXAMPLES
CHAPTER 10 – Panel Data Models
------------------------------------------------------------------------------- name: SN log: myReplications\iiexample10 log type: smcl opened on: 11 Jun 2019, 14:11:48 . ********************************************** . * Solomon Negash - Examples . * Wooldridge (2016). Economic Analysis of Cross-Section and Panel Data. 2nd ed. . * STATA Program, version 15.1. . * Chapter 10 - Basic Linear Unobserved Effects Panel Data Models . * Computer Exercises (Problems) . ******************** SETUP ********************* . // Example 10.1 NA . // Example 10.2 NA . // Example 10.3 NA . // Example 10.4 (RE Estimation of the Effects of Job Training Grants): . bcuse jtrain, clear nodesc . xtset fcode year panel variable: fcode (strongly balanced) time variable: year, 1987 to 1989 delta: 1 unit . xtreg lscrap d88 d89 union grant grant_1, re Random-effects GLS regression Number of obs = 162 Group variable: fcode Number of groups = 54 R-sq: Obs per group: within = 0.2006 min = 3 between = 0.0206 avg = 3.0 overall = 0.0361 max = 3 Wald chi2(5) = 26.99 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0001 ------------------------------------------------------------------------------ lscrap | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- d88 | -.0934519 .1091559 -0.86 0.392 -.3073936 .1204898 d89 | -.2698336 .1316496 -2.05 0.040 -.527862 -.0118052 union | .5478021 .410625 1.33 0.182 -.2570081 1.352612 grant | -.2146961 .1477838 -1.45 0.146 -.504347 .0749548 grant_1 | -.3770697 .2053516 -1.84 0.066 -.7795514 .025412 _cons | .4148333 .2434322 1.70 0.088 -.0622851 .8919517 -------------+---------------------------------------------------------------- sigma_u | 1.3900287 sigma_e | .4977442 rho | .88634984 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . test grant grant_1 ( 1) grant = 0 ( 2) grant_1 = 0 chi2( 2) = 3.66 Prob > chi2 = 0.1601 . // Example 10.5 (FE Estimation of the Effects of Job Training Grants): . xtreg lscrap d88 d89 union grant grant_1, fe note: union omitted because of collinearity Fixed-effects (within) regression Number of obs = 162 Group variable: fcode Number of groups = 54 R-sq: Obs per group: within = 0.2010 min = 3 between = 0.0079 avg = 3.0 overall = 0.0068 max = 3 F(4,104) = 6.54 corr(u_i, Xb) = -0.0714 Prob > F = 0.0001 ------------------------------------------------------------------------------ lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- d88 | -.0802157 .1094751 -0.73 0.465 -.2973089 .1368776 d89 | -.2472028 .1332183 -1.86 0.066 -.5113797 .016974 union | 0 (omitted) grant | -.2523149 .150629 -1.68 0.097 -.5510178 .046388 grant_1 | -.4215895 .2102 -2.01 0.047 -.8384239 -.0047551 _cons | .597434 .0677344 8.82 0.000 .4631142 .7317539 -------------+---------------------------------------------------------------- sigma_u | 1.438982 sigma_e | .4977442 rho | .89313867 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(53, 104) = 24.66 Prob > F = 0.0000 . test grant grant_1 ( 1) grant = 0 ( 2) grant_1 = 0 F( 2, 104) = 2.23 Prob > F = 0.1127 . // Example 10.5 (Continued) . xtreg lscrap d88 d89 union grant grant_1, fe r note: union omitted because of collinearity Fixed-effects (within) regression Number of obs = 162 Group variable: fcode Number of groups = 54 R-sq: Obs per group: within = 0.2010 min = 3 between = 0.0079 avg = 3.0 overall = 0.0068 max = 3 F(4,53) = 7.07 corr(u_i, Xb) = -0.0714 Prob > F = 0.0001 (Std. Err. adjusted for 54 clusters in fcode) ------------------------------------------------------------------------------ | Robust lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- d88 | -.0802157 .0978408 -0.82 0.416 -.2764594 .1160281 d89 | -.2472028 .1967819 -1.26 0.215 -.6418974 .1474917 union | 0 (omitted) grant | -.2523149 .1434399 -1.76 0.084 -.5400188 .035389 grant_1 | -.4215895 .2824604 -1.49 0.141 -.9881333 .1449543 _cons | .597434 .0638746 9.35 0.000 .4693177 .7255503 -------------+---------------------------------------------------------------- sigma_u | 1.438982 sigma_e | .4977442 rho | .89313867 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . // Example 10.6 (FD Estimation of the Effects of Job Training Grants) . reg d.lscrap d89 d.grant d.grant_1 Source | SS df MS Number of obs = 108 -------------+---------------------------------- F(3, 104) = 1.31 Model | 1.31104171 3 .437013902 Prob > F = 0.2739 Residual | 34.5904836 104 .332600804 R-squared = 0.0365 -------------+---------------------------------- Adj R-squared = 0.0087 Total | 35.9015253 107 .335528274 Root MSE = .57672 ------------------------------------------------------------------------------ D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- d89 | -.0962082 .1254469 -0.77 0.445 -.3449741 .1525577 | grant | D1. | -.2227811 .1307423 -1.70 0.091 -.482048 .0364859 | grant_1 | D1. | -.3512459 .2350848 -1.49 0.138 -.817428 .1149361 | _cons | -.0906072 .0909695 -1.00 0.322 -.2710031 .0897888 ------------------------------------------------------------------------------ . test d.grant d.grant_1 ( 1) D.grant = 0 ( 2) D.grant_1 = 0 F( 2, 104) = 1.53 Prob > F = 0.2215 . reg d.lscrap d89 d.grant d.grant_1, robust Linear regression Number of obs = 108 F(3, 104) = 1.95 Prob > F = 0.1254 R-squared = 0.0365 Root MSE = .57672 ------------------------------------------------------------------------------ | Robust D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- d89 | -.0962082 .1284682 -0.75 0.456 -.3509654 .1585491 | grant | D1. | -.2227811 .1238652 -1.80 0.075 -.4684103 .0228482 | grant_1 | D1. | -.3512459 .2477216 -1.42 0.159 -.8424871 .1399952 | _cons | -.0906072 .0846918 -1.07 0.287 -.2585543 .0773399 ------------------------------------------------------------------------------ . //Example 10.6 (continued) . reg d.lscrap d89 d.grant d.grant_1 Source | SS df MS Number of obs = 108 -------------+---------------------------------- F(3, 104) = 1.31 Model | 1.31104171 3 .437013902 Prob > F = 0.2739 Residual | 34.5904836 104 .332600804 R-squared = 0.0365 -------------+---------------------------------- Adj R-squared = 0.0087 Total | 35.9015253 107 .335528274 Root MSE = .57672 ------------------------------------------------------------------------------ D.lscrap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- d89 | -.0962082 .1254469 -0.77 0.445 -.3449741 .1525577 | grant | D1. | -.2227811 .1307423 -1.70 0.091 -.482048 .0364859 | grant_1 | D1. | -.3512459 .2350848 -1.49 0.138 -.817428 .1149361 | _cons | -.0906072 .0909695 -1.00 0.322 -.2710031 .0897888 ------------------------------------------------------------------------------ . predict u, r (363 missing values generated) . g u_1 = u[_n-1] (363 missing values generated) . reg u u_1 Source | SS df MS Number of obs = 54 -------------+---------------------------------- F(1, 52) = 3.10 Model | .971329776 1 .971329776 Prob > F = 0.0844 Residual | 16.3125141 52 .313702194 R-squared = 0.0562 -------------+---------------------------------- Adj R-squared = 0.0380 Total | 17.2838438 53 .326110261 Root MSE = .56009 ------------------------------------------------------------------------------ u | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- u_1 | .2369065 .1346333 1.76 0.084 -.0332549 .5070679 _cons | 3.33e-09 .0762188 0.00 1.000 -.1529441 .1529441 ------------------------------------------------------------------------------ . //Example 10.7 (Job Training Grants) . *See Example 10.4 above ------------------------------------------------------------------------------ . log close name: SN log: myReplications\iiexample10 log type: smcl closed on: 11 Jun 2019, 14:11:50 -------------------------------------------------------------------------------
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