1 2R version 4.1.1 (2021-08-10) -- "Kick Things" 3Copyright (C) 2021 The R Foundation for Statistical Computing 4Platform: x86_64-w64-mingw32/x64 (64-bit) 5 6R is free software and comes with ABSOLUTELY NO WARRANTY. 7You are welcome to redistribute it under certain conditions. 8Type 'license()' or 'licence()' for distribution details. 9 10R is a collaborative project with many contributors. 11Type 'contributors()' for more information and 12'citation()' on how to cite R or R packages in publications. 13 14Type 'demo()' for some demos, 'help()' for on-line help, or 15'help.start()' for an HTML browser interface to help. 16Type 'q()' to quit R. 17 18> ## Replicate some IV regression results 19> ## Replicate Baltagi (2013), Econometric Analysis of Panel Data, 5th edition, ch. 7.2 (p. 133) 20> ## (same as Baltagi (2006), Estimating an econometric model of crime using panel data from North Carolina, 21> ## Journal of Applied Econometrics 21(4), pp. 543-547. 22> ## 23> ## NB: Crime data set: results can diverge slightly form the values printed in Baltagi 24> ## if logarithm computation is performed on the original variable. For the paper, 25> ## a data set with pre-computed logarithms (variables l*) was used and those 26> ## logarithmic values diverge from what R's log() function gives. 27> ## -> see the two FE2SLS example which is computed in both ways 28> 29> 30> library(plm) 31> data("Crime", package = "plm") 32> 33> # replicates Table 7.1, column "Between" 34> form <- log(crmrte) ~ log(prbarr) + log(prbconv) + log(prbpris) + log(avgsen) + log(polpc) + log(density) + log(wcon) + log(wtuc) + log(wtrd) + log(wfir) + log(wser) + log(wmfg) + log(wfed) + log(wsta) + log(wloc) + log(pctymle) + log(pctmin) + region + smsa 35> be <- plm(form, data = Crime, model = "between") 36> summary(be) 37Oneway (individual) effect Between Model 38 39Call: 40plm(formula = form, data = Crime, model = "between") 41 42Balanced Panel: n = 90, T = 7, N = 630 43Observations used in estimation: 90 44 45Residuals: 46 Min. 1st Qu. Median 3rd Qu. Max. 47-0.510397 -0.098495 -0.021638 0.131446 0.598675 48 49Coefficients: 50 Estimate Std. Error t-value Pr(>|t|) 51(Intercept) -2.096704 2.821910 -0.7430 0.459999 52log(prbarr) -0.647509 0.087766 -7.3777 2.738e-10 *** 53log(prbconv) -0.528202 0.066741 -7.9143 2.868e-11 *** 54log(prbpris) 0.296505 0.230668 1.2854 0.202943 55log(avgsen) -0.235885 0.173534 -1.3593 0.178477 56log(polpc) 0.364217 0.060091 6.0611 6.370e-08 *** 57log(density) 0.168390 0.077380 2.1761 0.032971 * 58log(wcon) 0.195005 0.210406 0.9268 0.357259 59log(wtuc) -0.195747 0.170486 -1.1482 0.254864 60log(wtrd) 0.128619 0.278350 0.4621 0.645479 61log(wfir) 0.113239 0.220473 0.5136 0.609159 62log(wser) -0.105834 0.162825 -0.6500 0.517861 63log(wmfg) -0.024885 0.133876 -0.1859 0.853082 64log(wfed) 0.156213 0.287071 0.5442 0.588083 65log(wsta) -0.283780 0.256342 -1.1070 0.272123 66log(wloc) 0.010325 0.463487 0.0223 0.982292 67log(pctymle) -0.095049 0.157683 -0.6028 0.548626 68log(pctmin) 0.148195 0.048543 3.0529 0.003218 ** 69regionwest -0.229630 0.108468 -2.1170 0.037865 * 70regioncentral -0.163672 0.064453 -2.5394 0.013362 * 71smsayes -0.034592 0.132374 -0.2613 0.794624 72--- 73Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 74 75Total Sum of Squares: 26.913 76Residual Sum of Squares: 3.2171 77R-Squared: 0.88046 78Adj. R-Squared: 0.84582 79F-statistic: 25.4115 on 20 and 69 DF, p-value: < 2.22e-16 80> 81> # replicates Table 7.1, column "Fixed Effects" 82> fe <- plm(form, data = Crime, model = "within", effect = "twoways") 83> summary(fe) 84Twoways effects Within Model 85 86Call: 87plm(formula = form, data = Crime, effect = "twoways", model = "within") 88 89Balanced Panel: n = 90, T = 7, N = 630 90 91Residuals: 92 Min. 1st Qu. Median 3rd Qu. Max. 93-0.5581590 -0.0650155 -0.0018256 0.0698165 0.5247036 94 95Coefficients: 96 Estimate Std. Error t-value Pr(>|t|) 97log(prbarr) -0.3548257 0.0322048 -11.0178 < 2.2e-16 *** 98log(prbconv) -0.2815673 0.0211376 -13.3207 < 2.2e-16 *** 99log(prbpris) -0.1731044 0.0323027 -5.3588 1.263e-07 *** 100log(avgsen) -0.0024524 0.0261190 -0.0939 0.925232 101log(polpc) 0.4131576 0.0266231 15.5188 < 2.2e-16 *** 102log(density) 0.4143782 0.2825417 1.4666 0.143089 103log(wcon) -0.0377894 0.0390757 -0.9671 0.333954 104log(wtuc) 0.0455237 0.0190116 2.3945 0.016996 * 105log(wtrd) -0.0205048 0.0404790 -0.5066 0.612682 106log(wfir) -0.0038988 0.0282572 -0.1380 0.890312 107log(wser) 0.0088773 0.0191314 0.4640 0.642833 108log(wmfg) -0.3598306 0.1118355 -3.2175 0.001374 ** 109log(wfed) -0.3093206 0.1761644 -1.7559 0.079703 . 110log(wsta) 0.0528862 0.1135307 0.4658 0.641532 111log(wloc) 0.1815859 0.1176542 1.5434 0.123348 112log(pctymle) 0.6267986 0.3636065 1.7238 0.085334 . 113--- 114Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 115 116Total Sum of Squares: 16.123 117Residual Sum of Squares: 9.6545 118R-Squared: 0.40121 119Adj. R-Squared: 0.2729 120F-statistic: 21.6923 on 16 and 518 DF, p-value: < 2.22e-16 121> 122> # replicates Table 7.1, column "FE2SLS" 123> form_iv <- log(crmrte) ~ log(prbarr) + log(prbconv) + log(prbpris) + log(avgsen) + log(polpc) + log(density) + log(wcon) + log(wtuc) + log(wtrd) + log(wfir) + log(wser) + log(wmfg) + log(wfed) + log(wsta) + log(wloc) + log(pctymle) + log(pctmin) + region + smsa | . -log(prbarr) - log(polpc) + log(taxpc) + log(mix) 124> form_iv2 <- lcrmrte ~ lprbarr + lprbconv + lprbpris + lavgsen + lpolpc + ldensity + lwcon + lwtuc + lwtrd + lwfir + lwser + lwmfg + lwfed + lwsta + lwloc + lpctymle + lpctmin + region + smsa | . -lprbarr - lpolpc + ltaxpc + lmix 125> fe_iv <- plm(form_iv, data = Crime, model = "within", effect = "twoways", inst.method = "baltagi") 126> fe_iv2 <- plm(form_iv2, data = Crime, model = "within", effect = "twoways", inst.method = "baltagi") 127> summary(fe_iv) # logs computed by R 128Twoways effects Within Model 129Instrumental variable estimation 130 131Call: 132plm(formula = form_iv, data = Crime, effect = "twoways", model = "within", 133 inst.method = "baltagi") 134 135Balanced Panel: n = 90, T = 7, N = 630 136 137Residuals: 138 Min. 1st Qu. Median 3rd Qu. Max. 139-0.7207996 -0.0682050 -0.0041004 0.0759313 0.5661408 140 141Coefficients: 142 Estimate Std. Error z-value Pr(>|z|) 143log(prbarr) -0.5753943 0.8019932 -0.7175 0.4731 144log(prbconv) -0.4230764 0.5018196 -0.8431 0.3992 145log(prbpris) -0.2502194 0.2793986 -0.8956 0.3705 146log(avgsen) 0.0090948 0.0489808 0.1857 0.8527 147log(polpc) 0.6574104 0.8466656 0.7765 0.4375 148log(density) 0.1395236 1.0210334 0.1366 0.8913 149log(wcon) -0.0287310 0.0535109 -0.5369 0.5913 150log(wtuc) 0.0391296 0.0308542 1.2682 0.2047 151log(wtrd) -0.0177599 0.0453090 -0.3920 0.6951 152log(wfir) -0.0093412 0.0365471 -0.2556 0.7983 153log(wser) 0.0185815 0.0388087 0.4788 0.6321 154log(wmfg) -0.2431858 0.4194999 -0.5797 0.5621 155log(wfed) -0.4512812 0.5270259 -0.8563 0.3918 156log(wsta) -0.0187117 0.2807606 -0.0666 0.9469 157log(wloc) 0.2631882 0.3122909 0.8428 0.3994 158log(pctymle) 0.3512984 1.0107677 0.3476 0.7282 159 160Total Sum of Squares: 16.123 161Residual Sum of Squares: 11.535 162R-Squared: 0.39131 163Adj. R-Squared: 0.26087 164Chisq: 56.2016 on 16 DF, p-value: 2.2539e-06 165> summary(fe_iv2) # logs as in data set by Baltagi -> results match exactly 166Twoways effects Within Model 167Instrumental variable estimation 168 169Call: 170plm(formula = form_iv2, data = Crime, effect = "twoways", model = "within", 171 inst.method = "baltagi") 172 173Balanced Panel: n = 90, T = 7, N = 630 174 175Residuals: 176 Min. 1st Qu. Median 3rd Qu. Max. 177-0.7209110 -0.0682207 -0.0041115 0.0759381 0.5661659 178 179Coefficients: 180 Estimate Std. Error z-value Pr(>|z|) 181lprbarr -0.5755058 0.8021842 -0.7174 0.4731 182lprbconv -0.4231446 0.5019375 -0.8430 0.3992 183lprbpris -0.2502550 0.2794602 -0.8955 0.3705 184lavgsen 0.0090987 0.0489879 0.1857 0.8527 185lpolpc 0.6575270 0.8468673 0.7764 0.4375 186ldensity 0.1394120 1.0212391 0.1365 0.8914 187lwcon -0.0287308 0.0535145 -0.5369 0.5914 188lwtuc 0.0391292 0.0308568 1.2681 0.2048 189lwtrd -0.0177536 0.0453142 -0.3918 0.6952 190lwfir -0.0093443 0.0365519 -0.2556 0.7982 191lwser 0.0185854 0.0388155 0.4788 0.6321 192lwmfg -0.2431684 0.4195485 -0.5796 0.5622 193lwfed -0.4513372 0.5271232 -0.8562 0.3919 194lwsta -0.0187458 0.2808182 -0.0668 0.9468 195lwloc 0.2632585 0.3123945 0.8427 0.3994 196lpctymle 0.3511166 1.0110334 0.3473 0.7284 197 198Total Sum of Squares: 16.123 199Residual Sum of Squares: 11.537 200R-Squared: 0.3913 201Adj. R-Squared: 0.26087 202Chisq: 56.1934 on 16 DF, p-value: 2.2609e-06 203> 204> # ## felm example 205> # library(lfe) 206> # form_felm <- log(crmrte) ~ log(prbconv) + log(prbpris) + log(avgsen) + log(density) + log(wcon) + log(wtuc) + log(wtrd) + log(wfir) + log(wser) + log(wmfg) + log(wfed) + log(wsta) + log(wloc) + log(pctymle) | 207> # county + year | 208> # (log(prbarr) + log(polpc) ~ log(prbpris) + log(avgsen) + log(density) + log(wcon) + log(wtuc) + log(wtrd) + log(wfir) + log(wser) + log(wmfg) + log(wfed) + log(wsta) + log(wloc) + log(pctymle) + log(taxpc) + log(mix)) 209> # summary(felm(form_felm, data = Crime)) 210> 211> # replicates Table 7.1, column "BE2SLS" 212> be_iv <- plm(form_iv, data = Crime, model = "between") 213> summary(be_iv) 214Oneway (individual) effect Between Model 215Instrumental variable estimation 216 (Balestra-Varadharajan-Krishnakumar's transformation) 217 218Call: 219plm(formula = form_iv, data = Crime, model = "between") 220 221Balanced Panel: n = 90, T = 7, N = 630 222Observations used in estimation: 90 223 224Residuals: 225 Min. 1st Qu. Median 3rd Qu. Max. 226-0.5499406 -0.1041014 0.0029817 0.0986084 0.6020580 227 228Coefficients: 229 Estimate Std. Error z-value Pr(>|z|) 230(Intercept) -1.977222 4.000782 -0.4942 0.621159 231log(prbarr) -0.502946 0.240623 -2.0902 0.036601 * 232log(prbconv) -0.524770 0.099948 -5.2504 1.517e-07 *** 233log(prbpris) 0.187177 0.318292 0.5881 0.556487 234log(avgsen) -0.227225 0.178509 -1.2729 0.203052 235log(polpc) 0.408439 0.192998 2.1163 0.034321 * 236log(density) 0.225624 0.102474 2.2018 0.027681 * 237log(wcon) 0.314005 0.259103 1.2119 0.225553 238log(wtuc) -0.198943 0.197119 -1.0093 0.312854 239log(wtrd) 0.053559 0.296005 0.1809 0.856415 240log(wfir) 0.041707 0.305622 0.1365 0.891453 241log(wser) -0.135428 0.173646 -0.7799 0.435446 242log(wmfg) -0.042002 0.156266 -0.2688 0.788097 243log(wfed) 0.148024 0.325648 0.4546 0.649431 244log(wsta) -0.203080 0.298153 -0.6811 0.495792 245log(wloc) 0.044440 0.494358 0.0899 0.928372 246log(pctymle) -0.094720 0.191805 -0.4938 0.621423 247log(pctmin) 0.168902 0.052700 3.2049 0.001351 ** 248regionwest -0.204816 0.113836 -1.7992 0.071982 . 249regioncentral -0.172932 0.066706 -2.5924 0.009530 ** 250smsayes -0.080500 0.144232 -0.5581 0.576758 251--- 252Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 253 254Total Sum of Squares: 26.913 255Residual Sum of Squares: 3.396 256R-Squared: 0.87385 257Adj. R-Squared: 0.83729 258Chisq: 413.647 on 20 DF, p-value: < 2.22e-16 259> 260> # not in table 261> fd_iv <- plm(form_iv, data = Crime, model = "fd", effect = "individual") 262> summary(fd_iv) 263Oneway (individual) effect First-Difference Model 264Instrumental variable estimation 265 (Balestra-Varadharajan-Krishnakumar's transformation) 266 267Call: 268plm(formula = form_iv, data = Crime, effect = "individual", model = "fd") 269 270Balanced Panel: n = 90, T = 7, N = 630 271Observations used in estimation: 540 272 273Residuals: 274 Min. 1st Qu. Median 3rd Qu. Max. 275-1.0255104 -0.0799101 0.0053594 0.0800347 0.9355716 276 277Coefficients: 278 Estimate Std. Error z-value Pr(>|z|) 279(Intercept) 0.0069945 0.1200615 0.0583 0.9535 280log(prbarr) -0.3657462 1.5603992 -0.2344 0.8147 281log(prbconv) -0.2303887 0.9292399 -0.2479 0.8042 282log(prbpris) -0.1751528 0.4564269 -0.3837 0.7012 283log(avgsen) -0.0109961 0.1067622 -0.1030 0.9180 284log(polpc) 0.2537245 2.1624236 0.1173 0.9066 285log(density) -0.1466853 2.4584727 -0.0597 0.9524 286log(wcon) -0.0368105 0.0605415 -0.6080 0.5432 287log(wtuc) 0.0122918 0.0411881 0.2984 0.7654 288log(wtrd) -0.0388990 0.0501528 -0.7756 0.4380 289log(wfir) 0.0013050 0.0373671 0.0349 0.9721 290log(wser) 0.0164254 0.0152130 1.0797 0.2803 291log(wmfg) -0.2568435 0.3419870 -0.7510 0.4526 292log(wfed) -0.1409253 0.5886135 -0.2394 0.8108 293log(wsta) 0.1249133 0.0970830 1.2867 0.1982 294log(wloc) 0.0553071 0.6247338 0.0885 0.9295 295log(pctymle) -0.0054946 1.3734037 -0.0040 0.9968 296 297Total Sum of Squares: 22.197 298Residual Sum of Squares: 14.057 299R-Squared: 0.37134 300Adj. R-Squared: 0.3521 301Chisq: 46.0102 on 16 DF, p-value: 9.7009e-05 302> 303> # replicates Table 7.1, column "EC2SLS" 304> ## need to include time dummies! 305> form_re_iv <- log(crmrte) ~ log(prbarr) + log(prbconv) + log(prbpris) + log(avgsen) + log(polpc) + log(density) + log(wcon) + log(wtuc) + log(wtrd) + log(wfir) + log(wser) + log(wmfg) + log(wfed) + log(wsta) + log(wloc) + log(pctymle) + log(pctmin) + region + smsa + factor(year) | . -log(prbarr) - log(polpc) + log(taxpc) + log(mix) 306> form_re_iv2 <- lcrmrte ~ lprbarr + lprbconv + lprbpris + lavgsen + lpolpc + ldensity + lwcon + lwtuc + lwtrd + lwfir + lwser + lwmfg + lwfed + lwsta + lwloc + lpctymle + lpctmin + region + smsa + factor(year) | . -lprbarr - lpolpc + ltaxpc + lmix 307> re_iv <- plm(form_re_iv, data = Crime, model = "random", inst.method = "baltagi") 308> re_iv2 <- plm(form_re_iv2, data = Crime, model = "random", inst.method = "baltagi") 309> summary(re_iv) 310Oneway (individual) effect Random Effect Model 311 (Swamy-Arora's transformation) 312Instrumental variable estimation 313 (Baltagi's transformation) 314 315Call: 316plm(formula = form_re_iv, data = Crime, model = "random", inst.method = "baltagi") 317 318Balanced Panel: n = 90, T = 7, N = 630 319 320Effects: 321 var std.dev share 322idiosyncratic 0.02227 0.14923 0.326 323individual 0.04604 0.21456 0.674 324theta: 0.7458 325 326Residuals: 327 Min. 1st Qu. Median 3rd Qu. Max. 328-4.997164 -0.465637 0.027153 0.512779 3.917220 329 330Coefficients: 331 Estimate Std. Error z-value Pr(>|z|) 332(Intercept) -0.9536145 1.2839853 -0.7427 0.457664 333log(prbarr) -0.4129201 0.0974056 -4.2392 2.243e-05 *** 334log(prbconv) -0.3228859 0.0535539 -6.0292 1.648e-09 *** 335log(prbpris) -0.1863204 0.0419391 -4.4426 8.886e-06 *** 336log(avgsen) -0.0101739 0.0270229 -0.3765 0.706551 337log(polpc) 0.4347568 0.0896981 4.8469 1.254e-06 *** 338log(density) 0.4290337 0.0548511 7.8218 5.208e-15 *** 339log(wcon) -0.0074746 0.0395773 -0.1889 0.850202 340log(wtuc) 0.0454430 0.0197925 2.2960 0.021678 * 341log(wtrd) -0.0081453 0.0413823 -0.1968 0.843960 342log(wfir) -0.0036394 0.0289236 -0.1258 0.899867 343log(wser) 0.0056112 0.0201257 0.2788 0.780393 344log(wmfg) -0.2041324 0.0804418 -2.5376 0.011160 * 345log(wfed) -0.1635333 0.1594522 -1.0256 0.305083 346log(wsta) -0.0540400 0.1056774 -0.5114 0.609094 347log(wloc) 0.1630405 0.1196368 1.3628 0.172947 348log(pctymle) -0.1080968 0.1397015 -0.7738 0.439067 349log(pctmin) 0.1890388 0.0415013 4.5550 5.238e-06 *** 350regionwest -0.2268401 0.0995975 -2.2776 0.022752 * 351regioncentral -0.1940408 0.0598277 -3.2433 0.001181 ** 352smsayes -0.2251624 0.1156369 -1.9471 0.051517 . 353factor(year)82 0.0107457 0.0257968 0.4166 0.677006 354factor(year)83 -0.0837924 0.0307088 -2.7286 0.006360 ** 355factor(year)84 -0.1034973 0.0370886 -2.7905 0.005262 ** 356factor(year)85 -0.0956959 0.0494505 -1.9352 0.052968 . 357factor(year)86 -0.0688930 0.0595961 -1.1560 0.247681 358factor(year)87 -0.0314024 0.0705204 -0.4453 0.656106 359--- 360Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 361 362Total Sum of Squares: 30.168 363Residual Sum of Squares: 544.47 364R-Squared: 0.59845 365Adj. R-Squared: 0.58114 366Chisq: 575.685 on 26 DF, p-value: < 2.22e-16 367> summary(re_iv2) 368Oneway (individual) effect Random Effect Model 369 (Swamy-Arora's transformation) 370Instrumental variable estimation 371 (Baltagi's transformation) 372 373Call: 374plm(formula = form_re_iv2, data = Crime, model = "random", inst.method = "baltagi") 375 376Balanced Panel: n = 90, T = 7, N = 630 377 378Effects: 379 var std.dev share 380idiosyncratic 0.02227 0.14924 0.326 381individual 0.04604 0.21456 0.674 382theta: 0.7457 383 384Residuals: 385 Min. 1st Qu. Median 3rd Qu. Max. 386-4.996927 -0.465655 0.027205 0.512780 3.917085 387 388Coefficients: 389 Estimate Std. Error z-value Pr(>|z|) 390(Intercept) -0.9538032 1.2839664 -0.7429 0.457568 391lprbarr -0.4129261 0.0974020 -4.2394 2.241e-05 *** 392lprbconv -0.3228872 0.0535517 -6.0295 1.645e-09 *** 393lprbpris -0.1863195 0.0419382 -4.4427 8.883e-06 *** 394lavgsen -0.0101765 0.0270231 -0.3766 0.706481 395lpolpc 0.4347492 0.0896950 4.8470 1.254e-06 *** 396ldensity 0.4290282 0.0548483 7.8221 5.196e-15 *** 397lwcon -0.0074751 0.0395775 -0.1889 0.850194 398lwtuc 0.0454450 0.0197926 2.2961 0.021673 * 399lwtrd -0.0081412 0.0413828 -0.1967 0.844040 400lwfir -0.0036395 0.0289238 -0.1258 0.899865 401lwser 0.0056098 0.0201259 0.2787 0.780447 402lwmfg -0.2041398 0.0804393 -2.5378 0.011155 * 403lwfed -0.1635108 0.1594496 -1.0255 0.305142 404lwsta -0.0540503 0.1056769 -0.5115 0.609024 405lwloc 0.1630523 0.1196380 1.3629 0.172920 406lpctymle -0.1081057 0.1396949 -0.7739 0.439007 407lpctmin 0.1890370 0.0414988 4.5552 5.233e-06 *** 408regionwest -0.2268433 0.0995913 -2.2777 0.022742 * 409regioncentral -0.1940428 0.0598241 -3.2436 0.001180 ** 410smsayes -0.2251539 0.1156302 -1.9472 0.051512 . 411factor(year)82 0.0107452 0.0257969 0.4165 0.677023 412factor(year)83 -0.0837944 0.0307088 -2.7287 0.006359 ** 413factor(year)84 -0.1034997 0.0370885 -2.7906 0.005261 ** 414factor(year)85 -0.0957017 0.0494502 -1.9353 0.052952 . 415factor(year)86 -0.0688982 0.0595956 -1.1561 0.247642 416factor(year)87 -0.0314071 0.0705197 -0.4454 0.656055 417--- 418Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 419 420Total Sum of Squares: 30.169 421Residual Sum of Squares: 544.4 422R-Squared: 0.59847 423Adj. R-Squared: 0.58115 424Chisq: 575.735 on 26 DF, p-value: < 2.22e-16 425> 426> # replicates Baltagi (2013), p. 137/Baltagi (2021), p. 165 ("G2SLS"), table 7.3 (not in Table 7.1) 427> re_iv_bvk <- plm(form_re_iv, data = Crime, model = "random", inst.method = "bvk") 428> re_iv_bvk2 <- plm(form_re_iv2, data = Crime, model = "random", inst.method = "bvk") 429> summary(re_iv_bvk) 430Oneway (individual) effect Random Effect Model 431 (Swamy-Arora's transformation) 432Instrumental variable estimation 433 (Balestra-Varadharajan-Krishnakumar's transformation) 434 435Call: 436plm(formula = form_re_iv, data = Crime, model = "random", inst.method = "bvk") 437 438Balanced Panel: n = 90, T = 7, N = 630 439 440Effects: 441 var std.dev share 442idiosyncratic 0.02227 0.14923 0.326 443individual 0.04604 0.21456 0.674 444theta: 0.7458 445 446Residuals: 447 Min. 1st Qu. Median 3rd Qu. Max. 448-0.7485123 -0.0710015 0.0040742 0.0784401 0.4756493 449 450Coefficients: 451 Estimate Std. Error z-value Pr(>|z|) 452(Intercept) -0.4538241 1.7029840 -0.2665 0.789864 453log(prbarr) -0.4141200 0.2210540 -1.8734 0.061015 . 454log(prbconv) -0.3432383 0.1324679 -2.5911 0.009567 ** 455log(prbpris) -0.1900437 0.0733420 -2.5912 0.009564 ** 456log(avgsen) -0.0064374 0.0289406 -0.2224 0.823977 457log(polpc) 0.5049285 0.2277811 2.2167 0.026642 * 458log(density) 0.4343519 0.0711528 6.1045 1.031e-09 *** 459log(wcon) -0.0042963 0.0414225 -0.1037 0.917392 460log(wtuc) 0.0444572 0.0215449 2.0635 0.039068 * 461log(wtrd) -0.0085626 0.0419822 -0.2040 0.838387 462log(wfir) -0.0040302 0.0294565 -0.1368 0.891175 463log(wser) 0.0105604 0.0215822 0.4893 0.624620 464log(wmfg) -0.2017917 0.0839423 -2.4039 0.016220 * 465log(wfed) -0.2134634 0.2151074 -0.9924 0.321023 466log(wsta) -0.0601083 0.1203146 -0.4996 0.617362 467log(wloc) 0.1835137 0.1396721 1.3139 0.188884 468log(pctymle) -0.1458448 0.2268137 -0.6430 0.520214 469log(pctmin) 0.1948760 0.0459409 4.2419 2.217e-05 *** 470regionwest -0.2281780 0.1010317 -2.2585 0.023916 * 471regioncentral -0.1987675 0.0607510 -3.2718 0.001068 ** 472smsayes -0.2595423 0.1499780 -1.7305 0.083535 . 473factor(year)82 0.0132140 0.0299923 0.4406 0.659518 474factor(year)83 -0.0847676 0.0320008 -2.6489 0.008075 ** 475factor(year)84 -0.1062004 0.0387893 -2.7379 0.006184 ** 476factor(year)85 -0.0977398 0.0511685 -1.9102 0.056113 . 477factor(year)86 -0.0719390 0.0605821 -1.1875 0.235045 478factor(year)87 -0.0396520 0.0758537 -0.5227 0.601153 479--- 480Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 481 482Total Sum of Squares: 30.168 483Residual Sum of Squares: 12.418 484R-Squared: 0.59228 485Adj. R-Squared: 0.5747 486Chisq: 542.435 on 26 DF, p-value: < 2.22e-16 487> summary(re_iv_bvk2) 488Oneway (individual) effect Random Effect Model 489 (Swamy-Arora's transformation) 490Instrumental variable estimation 491 (Balestra-Varadharajan-Krishnakumar's transformation) 492 493Call: 494plm(formula = form_re_iv2, data = Crime, model = "random", inst.method = "bvk") 495 496Balanced Panel: n = 90, T = 7, N = 630 497 498Effects: 499 var std.dev share 500idiosyncratic 0.02227 0.14924 0.326 501individual 0.04604 0.21456 0.674 502theta: 0.7457 503 504Residuals: 505 Min. 1st Qu. Median 3rd Qu. Max. 506-0.7485357 -0.0709883 0.0040648 0.0784455 0.4756273 507 508Coefficients: 509 Estimate Std. Error z-value Pr(>|z|) 510(Intercept) -0.4538501 1.7029831 -0.2665 0.789852 511lprbarr -0.4141383 0.2210496 -1.8735 0.060998 . 512lprbconv -0.3432506 0.1324648 -2.5913 0.009563 ** 513lprbpris -0.1900467 0.0733392 -2.5913 0.009560 ** 514lavgsen -0.0064389 0.0289407 -0.2225 0.823935 515lpolpc 0.5049461 0.2277778 2.2168 0.026634 * 516ldensity 0.4343449 0.0711496 6.1047 1.030e-09 *** 517lwcon -0.0042958 0.0414226 -0.1037 0.917403 518lwtuc 0.0444589 0.0215448 2.0636 0.039060 * 519lwtrd -0.0085579 0.0419829 -0.2038 0.838476 520lwfir -0.0040305 0.0294569 -0.1368 0.891166 521lwser 0.0105602 0.0215823 0.4893 0.624630 522lwmfg -0.2018020 0.0839373 -2.4042 0.016208 * 523lwfed -0.2134579 0.2151046 -0.9923 0.321029 524lwsta -0.0601232 0.1203149 -0.4997 0.617275 525lwloc 0.1835363 0.1396775 1.3140 0.188846 526lpctymle -0.1458703 0.2268086 -0.6431 0.520131 527lpctmin 0.1948763 0.0459385 4.2421 2.214e-05 *** 528regionwest -0.2281821 0.1010260 -2.2586 0.023905 * 529regioncentral -0.1987703 0.0607475 -3.2721 0.001068 ** 530smsayes -0.2595451 0.1499718 -1.7306 0.083518 . 531factor(year)82 0.0132147 0.0299924 0.4406 0.659500 532factor(year)83 -0.0847693 0.0320010 -2.6490 0.008074 ** 533factor(year)84 -0.1062027 0.0387893 -2.7379 0.006183 ** 534factor(year)85 -0.0977457 0.0511681 -1.9103 0.056097 . 535factor(year)86 -0.0719451 0.0605819 -1.1876 0.235004 536factor(year)87 -0.0396595 0.0758531 -0.5228 0.601081 537--- 538Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 539 540Total Sum of Squares: 30.169 541Residual Sum of Squares: 12.419 542R-Squared: 0.5923 543Adj. R-Squared: 0.57472 544Chisq: 542.478 on 26 DF, p-value: < 2.22e-16 545> cor(plm:::fitted_exp.plm(re_iv_bvk2), re_iv_bvk2$model[ , 1])^2 # overall R^2 as per Stata 546[1] 0.7724889 547> 548> 549> 550> ## Hausman-Taylor estimator: 551> ## replicates Baltagi (2005, 2013), table 7.4; Baltagi (2021), table 7.5 552> # (chisq values in Baltagi (2021) are not those of the models but of Hausman test 553> # between the models! plm's summary replicates chisq values of the models as 554> # given by Stata and printed in Baltagi (2021), tables 7.6, 7.7) 555> # 556> # Table 7.5 claims to replicate Baltagi/Khanti-Akom (1990), table II, but values 557> # for all models but within are largely different (even the GLS case!), making 558> # the book reproducible but not the paper (likely the paper is in error!). 559> data("Wages", package = "plm") 560> pWages <- pdata.frame(Wages, index = 595) 561> 562> form_wage <- lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + 563+ bluecol + ind + union + sex + black + ed 564> 565> form_wage_iv <- lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + 566+ bluecol + ind + union + sex + black + ed | 567+ bluecol + south + smsa + ind + sex + black | 568+ wks + married + union + exp + I(exp ^ 2) 569> 570> gls <- plm(form_wage, data = pWages, model = "random") 571> summary(gls) 572Oneway (individual) effect Random Effect Model 573 (Swamy-Arora's transformation) 574 575Call: 576plm(formula = form_wage, data = pWages, model = "random") 577 578Balanced Panel: n = 595, T = 7, N = 4165 579 580Effects: 581 var std.dev share 582idiosyncratic 0.02310 0.15199 0.251 583individual 0.06899 0.26266 0.749 584theta: 0.7863 585 586Residuals: 587 Min. 1st Qu. Median 3rd Qu. Max. 588-2.0612918 -0.1146344 0.0073351 0.1227697 2.0972144 589 590Coefficients: 591 Estimate Std. Error z-value Pr(>|z|) 592(Intercept) 4.2637e+00 9.7716e-02 43.6332 < 2.2e-16 *** 593wks 1.0347e-03 7.7337e-04 1.3379 0.1809396 594southyes -1.6618e-02 2.6527e-02 -0.6265 0.5310184 595smsayes -1.3823e-02 1.9993e-02 -0.6914 0.4893108 596marriedyes -7.4628e-02 2.3005e-02 -3.2440 0.0011788 ** 597exp 8.2054e-02 2.8478e-03 28.8138 < 2.2e-16 *** 598I(exp^2) -8.0845e-04 6.2823e-05 -12.8686 < 2.2e-16 *** 599bluecolyes -5.0066e-02 1.6647e-02 -3.0076 0.0026336 ** 600ind 3.7441e-03 1.7262e-02 0.2169 0.8282830 601unionyes 6.3223e-02 1.7070e-02 3.7038 0.0002124 *** 602sexfemale -3.3921e-01 5.1303e-02 -6.6119 3.795e-11 *** 603blackyes -2.1028e-01 5.7989e-02 -3.6262 0.0002876 *** 604ed 9.9659e-02 5.7475e-03 17.3395 < 2.2e-16 *** 605--- 606Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 607 608Total Sum of Squares: 270.16 609Residual Sum of Squares: 164.79 610R-Squared: 0.39002 611Adj. R-Squared: 0.38825 612Chisq: 2654.74 on 12 DF, p-value: < 2.22e-16 613> 614> within <- plm(form_wage, data = pWages, model = "within") 615> summary(within) 616Oneway (individual) effect Within Model 617 618Call: 619plm(formula = form_wage, data = pWages, model = "within") 620 621Balanced Panel: n = 595, T = 7, N = 4165 622 623Residuals: 624 Min. 1st Qu. Median 3rd Qu. Max. 625-1.8122282 -0.0519417 0.0038855 0.0614706 1.9434306 626 627Coefficients: 628 Estimate Std. Error t-value Pr(>|t|) 629wks 8.3595e-04 5.9967e-04 1.3940 0.16340 630southyes -1.8612e-03 3.4299e-02 -0.0543 0.95673 631smsayes -4.2469e-02 1.9428e-02 -2.1859 0.02889 * 632marriedyes -2.9726e-02 1.8984e-02 -1.5659 0.11747 633exp 1.1321e-01 2.4710e-03 45.8141 < 2.2e-16 *** 634I(exp^2) -4.1835e-04 5.4595e-05 -7.6629 2.329e-14 *** 635bluecolyes -2.1476e-02 1.3784e-02 -1.5581 0.11930 636ind 1.9210e-02 1.5446e-02 1.2437 0.21370 637unionyes 3.2785e-02 1.4923e-02 2.1970 0.02809 * 638--- 639Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 640 641Total Sum of Squares: 240.65 642Residual Sum of Squares: 82.267 643R-Squared: 0.65815 644Adj. R-Squared: 0.60026 645F-statistic: 761.751 on 9 and 3561 DF, p-value: < 2.22e-16 646> 647> ht <- plm(form_wage_iv, 648+ data = pWages, 649+ random.method = "ht", model = "random", inst.method = "baltagi") 650> summary(ht) 651Oneway (individual) effect Random Effect Model 652 (Hausman-Taylor's transformation) 653Instrumental variable estimation 654 (Baltagi's transformation) 655 656Call: 657plm(formula = form_wage_iv, data = pWages, model = "random", 658 random.method = "ht", inst.method = "baltagi") 659 660Balanced Panel: n = 595, T = 7, N = 4165 661 662Effects: 663 var std.dev share 664idiosyncratic 0.02304 0.15180 0.025 665individual 0.88699 0.94180 0.975 666theta: 0.9392 667 668Residuals: 669 Min. 1st Qu. Median 3rd Qu. Max. 670-12.643736 -0.466002 0.043285 0.524739 13.340263 671 672Coefficients: 673 Estimate Std. Error z-value Pr(>|z|) 674(Intercept) 2.9127e+00 2.8365e-01 10.2687 < 2.2e-16 *** 675wks 8.3740e-04 5.9973e-04 1.3963 0.16263 676southyes 7.4398e-03 3.1955e-02 0.2328 0.81590 677smsayes -4.1833e-02 1.8958e-02 -2.2066 0.02734 * 678marriedyes -2.9851e-02 1.8980e-02 -1.5728 0.11578 679exp 1.1313e-01 2.4710e-03 45.7851 < 2.2e-16 *** 680I(exp^2) -4.1886e-04 5.4598e-05 -7.6718 1.696e-14 *** 681bluecolyes -2.0705e-02 1.3781e-02 -1.5024 0.13299 682ind 1.3604e-02 1.5237e-02 0.8928 0.37196 683unionyes 3.2771e-02 1.4908e-02 2.1982 0.02794 * 684sexfemale -1.3092e-01 1.2666e-01 -1.0337 0.30129 685blackyes -2.8575e-01 1.5570e-01 -1.8352 0.06647 . 686ed 1.3794e-01 2.1248e-02 6.4919 8.474e-11 *** 687--- 688Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 689 690Total Sum of Squares: 243.04 691Residual Sum of Squares: 4163.6 692R-Squared: 0.60945 693Adj. R-Squared: 0.60833 694Chisq: 6891.87 on 12 DF, p-value: < 2.22e-16 695> 696> am <- plm(form_wage_iv, 697+ data = pWages, 698+ random.method = "ht", model = "random", inst.method = "am") 699> summary(am) 700Oneway (individual) effect Random Effect Model 701 (Hausman-Taylor's transformation) 702Instrumental variable estimation 703 (Amemiya-MaCurdy's transformation) 704 705Call: 706plm(formula = form_wage_iv, data = pWages, model = "random", 707 random.method = "ht", inst.method = "am") 708 709Balanced Panel: n = 595, T = 7, N = 4165 710 711Effects: 712 var std.dev share 713idiosyncratic 0.02304 0.15180 0.025 714individual 0.88699 0.94180 0.975 715theta: 0.9392 716 717Residuals: 718 Min. 1st Qu. Median 3rd Qu. Max. 719-12.643192 -0.464811 0.043216 0.523598 13.338789 720 721Coefficients: 722 Estimate Std. Error z-value Pr(>|z|) 723(Intercept) 2.9273e+00 2.7513e-01 10.6399 < 2.2e-16 *** 724wks 8.3806e-04 5.9945e-04 1.3980 0.16210 725southyes 7.2818e-03 3.1936e-02 0.2280 0.81964 726smsayes -4.1951e-02 1.8947e-02 -2.2141 0.02682 * 727marriedyes -3.0089e-02 1.8967e-02 -1.5864 0.11266 728exp 1.1297e-01 2.4688e-03 45.7584 < 2.2e-16 *** 729I(exp^2) -4.2140e-04 5.4554e-05 -7.7244 1.124e-14 *** 730bluecolyes -2.0850e-02 1.3765e-02 -1.5147 0.12986 731ind 1.3629e-02 1.5229e-02 0.8949 0.37082 732unionyes 3.2475e-02 1.4894e-02 2.1804 0.02922 * 733sexfemale -1.3201e-01 1.2660e-01 -1.0427 0.29709 734blackyes -2.8590e-01 1.5549e-01 -1.8388 0.06595 . 735ed 1.3720e-01 2.0570e-02 6.6703 2.553e-11 *** 736--- 737Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 738 739Total Sum of Squares: 243.04 740Residual Sum of Squares: 4160.3 741R-Squared: 0.60948 742Adj. R-Squared: 0.60835 743Chisq: 6879.2 on 12 DF, p-value: < 2.22e-16 744> 745> bms <- plm(form_wage_iv, 746+ data = pWages, 747+ random.method = "ht", model = "random", inst.method = "bms") 748> summary(bms) 749Oneway (individual) effect Random Effect Model 750 (Hausman-Taylor's transformation) 751Instrumental variable estimation 752 (Breusch-Mizon-Schmidt's transformation) 753 754Call: 755plm(formula = form_wage_iv, data = pWages, model = "random", 756 random.method = "ht", inst.method = "bms") 757 758Balanced Panel: n = 595, T = 7, N = 4165 759 760Effects: 761 var std.dev share 762idiosyncratic 0.02304 0.15180 0.025 763individual 0.88699 0.94180 0.975 764theta: 0.9392 765 766Residuals: 767 Min. 1st Qu. Median 3rd Qu. Max. 768-12.790365 -0.448022 0.042648 0.506978 13.292638 769 770Coefficients: 771 Estimate Std. Error z-value Pr(>|z|) 772(Intercept) 1.9794e+00 2.6724e-01 7.4071 1.291e-13 *** 773wks 7.9537e-04 5.9850e-04 1.3289 0.183869 774southyes 1.4668e-02 3.1883e-02 0.4601 0.645478 775smsayes -5.2042e-02 1.8911e-02 -2.7520 0.005923 ** 776marriedyes -3.9262e-02 1.8925e-02 -2.0747 0.038017 * 777exp 1.0867e-01 2.4557e-03 44.2513 < 2.2e-16 *** 778I(exp^2) -4.9060e-04 5.4352e-05 -9.0265 < 2.2e-16 *** 779bluecolyes -1.5389e-02 1.3737e-02 -1.1203 0.262596 780ind 1.9024e-02 1.5202e-02 1.2514 0.210795 781unionyes 3.7855e-02 1.4864e-02 2.5467 0.010873 * 782sexfemale -1.8027e-01 1.2639e-01 -1.4263 0.153769 783blackyes -1.5636e-01 1.5506e-01 -1.0084 0.313276 784ed 2.2066e-01 1.9850e-02 11.1162 < 2.2e-16 *** 785--- 786Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 787 788Total Sum of Squares: 243.04 789Residual Sum of Squares: 4147.6 790R-Squared: 0.60686 791Adj. R-Squared: 0.60572 792Chisq: 6467.37 on 12 DF, p-value: < 2.22e-16 793> 794> # texreg::screenreg(list(ht, am, bms)) 795> 796> phtest(within, ht) # 5.2577 -> match Baltagi (2021), p. 175 for statistic but 797 798 Hausman Test 799 800data: form_wage 801chisq = 5.2577, df = 9, p-value = 0.8113 802alternative hypothesis: one model is inconsistent 803 804> # df are different (9 vs. 3), Baltagi explains why df = 3. 805> 806> phtest(ht, am) # 14.66 -> close to Baltagi's 17.74 (df = 12 vs. 13) 807 808 Hausman Test 809 810data: form_wage_iv 811chisq = 14.666, df = 12, p-value = 0.2602 812alternative hypothesis: one model is inconsistent 813 814> 815> 816> 817> 818> ### IV estimators ## 819> form_wage_iv2 <- lwage ~ wks + married + exp + I(exp ^ 2) + bluecol | 820+ wks + exp + bluecol | 821+ wks + married + exp + I(exp ^ 2) 822> 823> ## balanced one-way individual 824> IVbvk <- plm(form_wage_iv2, 825+ data = pWages, 826+ model = "random", inst.method = "bvk") 827> summary(IVbvk) 828Oneway (individual) effect Random Effect Model 829 (Swamy-Arora's transformation) 830Instrumental variable estimation 831 (Balestra-Varadharajan-Krishnakumar's transformation) 832 833Call: 834plm(formula = form_wage_iv2, data = pWages, model = "random", 835 inst.method = "bvk") 836 837Balanced Panel: n = 595, T = 7, N = 4165 838 839Effects: 840 var std.dev share 841idiosyncratic 0.02315 0.15215 0.189 842individual 0.09928 0.31509 0.811 843theta: 0.8205 844 845Residuals: 846 Min. 1st Qu. Median 3rd Qu. Max. 847-1.9670577 -0.1180355 0.0091769 0.1192050 2.0936461 848 849Coefficients: 850 Estimate Std. Error z-value Pr(>|z|) 851(Intercept) 5.3917e+00 5.2313e-02 103.0662 < 2.2e-16 *** 852wks 1.1115e-03 7.7209e-04 1.4397 0.1500 853marriedyes -1.3964e-02 2.1789e-02 -0.6409 0.5216 854exp 8.6050e-02 2.9196e-03 29.4729 < 2.2e-16 *** 855I(exp^2) -7.9169e-04 6.4523e-05 -12.2700 < 2.2e-16 *** 856bluecolyes -1.1180e-01 1.5963e-02 -7.0034 2.498e-12 *** 857--- 858Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 859 860Total Sum of Squares: 261.48 861Residual Sum of Squares: 163.12 862R-Squared: 0.37618 863Adj. R-Squared: 0.37543 864Chisq: 2507.95 on 5 DF, p-value: < 2.22e-16 865> 866> IVbalt <- plm(form_wage_iv2, 867+ data = pWages, 868+ model = "random", inst.method = "baltagi") 869> summary(IVbalt) 870Oneway (individual) effect Random Effect Model 871 (Swamy-Arora's transformation) 872Instrumental variable estimation 873 (Baltagi's transformation) 874 875Call: 876plm(formula = form_wage_iv2, data = pWages, model = "random", 877 inst.method = "baltagi") 878 879Balanced Panel: n = 595, T = 7, N = 4165 880 881Effects: 882 var std.dev share 883idiosyncratic 0.02315 0.15215 0.189 884individual 0.09928 0.31509 0.811 885theta: 0.8205 886 887Residuals: 888 Min. 1st Qu. Median 3rd Qu. Max. 889-12.909745 -0.785178 0.047664 0.784261 13.789006 890 891Coefficients: 892 Estimate Std. Error z-value Pr(>|z|) 893(Intercept) 5.4619e+00 5.3852e-02 101.4244 < 2.2e-16 *** 894wks 1.1434e-03 7.7454e-04 1.4763 0.1399 895marriedyes -1.1791e-01 2.4588e-02 -4.7954 1.623e-06 *** 896exp 8.7309e-02 3.1570e-03 27.6556 < 2.2e-16 *** 897I(exp^2) -8.1720e-04 7.0594e-05 -11.5761 < 2.2e-16 *** 898bluecolyes -1.0980e-01 1.6010e-02 -6.8583 6.968e-12 *** 899--- 900Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 901 902Total Sum of Squares: 261.48 903Residual Sum of Squares: 7085.3 904R-Squared: 0.3728 905Adj. R-Squared: 0.37204 906Chisq: 2502.88 on 5 DF, p-value: < 2.22e-16 907> 908> IVam <- plm(form_wage_iv2, 909+ data = pWages, 910+ model = "random", inst.method = "am") 911> summary(IVam) 912Oneway (individual) effect Random Effect Model 913 (Swamy-Arora's transformation) 914Instrumental variable estimation 915 (Amemiya-MaCurdy's transformation) 916 917Call: 918plm(formula = form_wage_iv2, data = pWages, model = "random", 919 inst.method = "am") 920 921Balanced Panel: n = 595, T = 7, N = 4165 922 923Effects: 924 var std.dev share 925idiosyncratic 0.02315 0.15215 0.189 926individual 0.09928 0.31509 0.811 927theta: 0.8205 928 929Residuals: 930 Min. 1st Qu. Median 3rd Qu. Max. 931-12.910190 -0.782406 0.048668 0.783272 13.787161 932 933Coefficients: 934 Estimate Std. Error z-value Pr(>|z|) 935(Intercept) 5.4599e+00 5.3794e-02 101.4955 < 2.2e-16 *** 936wks 1.1451e-03 7.7434e-04 1.4788 0.1392 937marriedyes -1.1314e-01 2.4486e-02 -4.6206 3.826e-06 *** 938exp 8.7077e-02 3.1494e-03 27.6490 < 2.2e-16 *** 939I(exp^2) -8.1187e-04 7.0401e-05 -11.5320 < 2.2e-16 *** 940bluecolyes -1.0993e-01 1.6006e-02 -6.8681 6.504e-12 *** 941--- 942Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 943 944Total Sum of Squares: 261.48 945Residual Sum of Squares: 7081.8 946R-Squared: 0.3731 947Adj. R-Squared: 0.37235 948Chisq: 2501.31 on 5 DF, p-value: < 2.22e-16 949> 950> IVbms <- plm(form_wage_iv2, 951+ data = pWages, 952+ model = "random", inst.method = "bms") 953> summary(IVbms) 954Oneway (individual) effect Random Effect Model 955 (Swamy-Arora's transformation) 956Instrumental variable estimation 957 (Breusch-Mizon-Schmidt's transformation) 958 959Call: 960plm(formula = form_wage_iv2, data = pWages, model = "random", 961 inst.method = "bms") 962 963Balanced Panel: n = 595, T = 7, N = 4165 964 965Effects: 966 var std.dev share 967idiosyncratic 0.02315 0.15215 0.189 968individual 0.09928 0.31509 0.811 969theta: 0.8205 970 971Residuals: 972 Min. 1st Qu. Median 3rd Qu. Max. 973-12.910637 -0.781238 0.049022 0.783769 13.787656 974 975Coefficients: 976 Estimate Std. Error z-value Pr(>|z|) 977(Intercept) 5.4586e+00 5.3775e-02 101.5077 < 2.2e-16 *** 978wks 1.1419e-03 7.7433e-04 1.4747 0.1403 979marriedyes -1.1298e-01 2.4431e-02 -4.6244 3.757e-06 *** 980exp 8.7249e-02 3.1468e-03 27.7266 < 2.2e-16 *** 981I(exp^2) -8.1597e-04 7.0335e-05 -11.6012 < 2.2e-16 *** 982bluecolyes -1.0990e-01 1.6006e-02 -6.8660 6.600e-12 *** 983--- 984Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 985 986Total Sum of Squares: 261.48 987Residual Sum of Squares: 7081.8 988R-Squared: 0.37311 989Adj. R-Squared: 0.37235 990Chisq: 2503.01 on 5 DF, p-value: < 2.22e-16 991> 992> # texreg::screenreg(list("BVK" = IVbvk, "Baltagi" = IVbalt, "AM" = IVam, "BMS" = IVbms), 993> # digits = 5) 994> 995> ## unbalanced one-way individual 996> 997> pWages_ubal <- pWages[-c(2:7, 79:82, 500:505), ] 998> pdim(pWages_ubal) 999Unbalanced Panel: n = 595, T = 1-7, N = 4149 1000> IVbvk_ubal <- plm(form_wage_iv2, 1001+ data = pWages_ubal, 1002+ model = "random", inst.method = "bvk") 1003> summary(IVbvk_ubal) 1004Oneway (individual) effect Random Effect Model 1005 (Swamy-Arora's transformation) 1006Instrumental variable estimation 1007 (Balestra-Varadharajan-Krishnakumar's transformation) 1008 1009Call: 1010plm(formula = form_wage_iv2, data = pWages_ubal, model = "random", 1011 inst.method = "bvk") 1012 1013Unbalanced Panel: n = 595, T = 1-7, N = 4149 1014 1015Effects: 1016 var std.dev share 1017idiosyncratic 0.02323 0.15243 0.19 1018individual 0.09893 0.31453 0.81 1019theta: 1020 Min. 1st Qu. Median Mean 3rd Qu. Max. 1021 0.5639 0.8198 0.8198 0.8196 0.8198 0.8198 1022 1023Residuals: 1024 Min. 1st Qu. Median Mean 3rd Qu. Max. 1025-1.96701 -0.11847 0.00915 -0.00001 0.11963 2.09363 1026 1027Coefficients: 1028 Estimate Std. Error z-value Pr(>|z|) 1029(Intercept) 5.3954e+00 5.2425e-02 102.9162 < 2.2e-16 *** 1030wks 1.1191e-03 7.7476e-04 1.4445 0.1486 1031marriedyes -1.3685e-02 2.1830e-02 -0.6269 0.5308 1032exp 8.5760e-02 2.9316e-03 29.2539 < 2.2e-16 *** 1033I(exp^2) -7.8934e-04 6.4755e-05 -12.1898 < 2.2e-16 *** 1034bluecolyes -1.1174e-01 1.6014e-02 -6.9771 3.012e-12 *** 1035--- 1036Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1037 1038Total Sum of Squares: 265.29 1039Residual Sum of Squares: 163.35 1040R-Squared: 0.38425 1041Adj. R-Squared: 0.38351 1042Chisq: 2477.34 on 5 DF, p-value: < 2.22e-16 1043> 1044> IVbalt_ubal <- plm(form_wage_iv2, 1045+ data = pWages_ubal, 1046+ model = "random", inst.method = "baltagi") 1047> summary(IVbalt_ubal) 1048Oneway (individual) effect Random Effect Model 1049 (Swamy-Arora's transformation) 1050Instrumental variable estimation 1051 (Baltagi's transformation) 1052 1053Call: 1054plm(formula = form_wage_iv2, data = pWages_ubal, model = "random", 1055 inst.method = "baltagi") 1056 1057Unbalanced Panel: n = 595, T = 1-7, N = 4149 1058 1059Effects: 1060 var std.dev share 1061idiosyncratic 0.02323 0.15243 0.19 1062individual 0.09893 0.31453 0.81 1063theta: 1064 Min. 1st Qu. Median Mean 3rd Qu. Max. 1065 0.5639 0.8198 0.8198 0.8196 0.8198 0.8198 1066 1067Residuals: 1068 Min. 1st Qu. Median Mean 3rd Qu. Max. 1069-12.8856 -0.7915 0.0470 -0.0004 0.7861 13.7637 1070 1071Coefficients: 1072 Estimate Std. Error z-value Pr(>|z|) 1073(Intercept) 5.4676e+00 5.4011e-02 101.2297 < 2.2e-16 *** 1074wks 1.1427e-03 7.7735e-04 1.4700 0.1416 1075marriedyes -1.1874e-01 2.4650e-02 -4.8172 1.456e-06 *** 1076exp 8.6999e-02 3.1742e-03 27.4081 < 2.2e-16 *** 1077I(exp^2) -8.1476e-04 7.0926e-05 -11.4874 < 2.2e-16 *** 1078bluecolyes -1.0986e-01 1.6064e-02 -6.8389 7.982e-12 *** 1079--- 1080Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1081 1082Total Sum of Squares: 265.29 1083Residual Sum of Squares: 7070.4 1084R-Squared: 0.38083 1085Adj. R-Squared: 0.38008 1086Chisq: 2468.11 on 5 DF, p-value: < 2.22e-16 1087> 1088> IVam_ubal <- plm(form_wage_iv2, 1089+ data = pWages_ubal, 1090+ model = "random", inst.method = "am") 1091> summary(IVam_ubal) 1092Oneway (individual) effect Random Effect Model 1093 (Swamy-Arora's transformation) 1094Instrumental variable estimation 1095 (Amemiya-MaCurdy's transformation) 1096 1097Call: 1098plm(formula = form_wage_iv2, data = pWages_ubal, model = "random", 1099 inst.method = "am") 1100 1101Unbalanced Panel: n = 595, T = 1-7, N = 4149 1102 1103Effects: 1104 var std.dev share 1105idiosyncratic 0.02323 0.15243 0.19 1106individual 0.09893 0.31453 0.81 1107theta: 1108 Min. 1st Qu. Median Mean 3rd Qu. Max. 1109 0.5639 0.8198 0.8198 0.8196 0.8198 0.8198 1110 1111Residuals: 1112 Min. 1st Qu. Median Mean 3rd Qu. Max. 1113-12.8863 -0.7867 0.0495 -0.0001 0.7861 13.7627 1114 1115Coefficients: 1116 Estimate Std. Error z-value Pr(>|z|) 1117(Intercept) 5.4638e+00 5.3915e-02 101.3399 < 2.2e-16 *** 1118wks 1.1511e-03 7.7708e-04 1.4813 0.1385 1119marriedyes -1.1403e-01 2.4534e-02 -4.6479 3.354e-06 *** 1120exp 8.6894e-02 3.1630e-03 27.4723 < 2.2e-16 *** 1121I(exp^2) -8.1196e-04 7.0668e-05 -11.4898 < 2.2e-16 *** 1122bluecolyes -1.0982e-01 1.6058e-02 -6.8391 7.968e-12 *** 1123--- 1124Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1125 1126Total Sum of Squares: 265.29 1127Residual Sum of Squares: 7066.9 1128R-Squared: 0.38113 1129Adj. R-Squared: 0.38038 1130Chisq: 2471.83 on 5 DF, p-value: < 2.22e-16 1131> 1132> IVbms_ubal <- plm(form_wage_iv2, 1133+ data = pWages_ubal, 1134+ model = "random", inst.method = "bms") 1135> 1136> 1137> summary(IVbms_ubal) 1138Oneway (individual) effect Random Effect Model 1139 (Swamy-Arora's transformation) 1140Instrumental variable estimation 1141 (Breusch-Mizon-Schmidt's transformation) 1142 1143Call: 1144plm(formula = form_wage_iv2, data = pWages_ubal, model = "random", 1145 inst.method = "bms") 1146 1147Unbalanced Panel: n = 595, T = 1-7, N = 4149 1148 1149Effects: 1150 var std.dev share 1151idiosyncratic 0.02323 0.15243 0.19 1152individual 0.09893 0.31453 0.81 1153theta: 1154 Min. 1st Qu. Median Mean 3rd Qu. Max. 1155 0.5639 0.8198 0.8198 0.8196 0.8198 0.8198 1156 1157Residuals: 1158 Min. 1st Qu. Median Mean 3rd Qu. Max. 1159-12.8867 -0.7873 0.0499 -0.0001 0.7873 13.7632 1160 1161Coefficients: 1162 Estimate Std. Error z-value Pr(>|z|) 1163(Intercept) 5.46240311 0.05389528 101.3522 < 2.2e-16 *** 1164wks 0.00114787 0.00077707 1.4772 0.1396 1165marriedyes -0.11376538 0.02447722 -4.6478 3.355e-06 *** 1166exp 0.08706288 0.00316031 27.5488 < 2.2e-16 *** 1167I(exp^2) -0.00081599 0.00007060 -11.5579 < 2.2e-16 *** 1168bluecolyes -0.10979302 0.01605806 -6.8373 8.072e-12 *** 1169--- 1170Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1171 1172Total Sum of Squares: 265.29 1173Residual Sum of Squares: 7066.8 1174R-Squared: 0.38114 1175Adj. R-Squared: 0.3804 1176Chisq: 2473.49 on 5 DF, p-value: < 2.22e-16 1177> 1178> # texreg::screenreg(list("BVK ui" = IVbvk_ubal, "Baltagi ui" = IVbalt_ubal, "AM ui" = IVam_ubal, "BMS ui" = IVbms_ubal), 1179> # digits = 5) 1180> 1181> 1182> ## balanced one-way time 1183> # gives identical results for "am" and "bms" results are identical to "baltagi", 1184> # likely because function StarX is not symmetric in effect 1185> IVbvk_t <- plm(form_wage_iv2, 1186+ data = pWages, 1187+ model = "random", inst.method = "bvk", effect = "time") 1188> summary(IVbvk_t) 1189Oneway (time) effect Random Effect Model 1190 (Swamy-Arora's transformation) 1191Instrumental variable estimation 1192 (Balestra-Varadharajan-Krishnakumar's transformation) 1193 1194Call: 1195plm(formula = form_wage_iv2, data = pWages, effect = "time", 1196 model = "random", inst.method = "bvk") 1197 1198Balanced Panel: n = 595, T = 7, N = 4165 1199 1200Effects: 1201 var std.dev share 1202idiosyncratic 0.1271 0.3565 1 1203time 0.0000 0.0000 0 1204theta: 0 1205 1206Residuals: 1207 Min. 1st Qu. Median 3rd Qu. Max. 1208-1.9957868 -0.2573738 -0.0061258 0.2677200 2.1611993 1209 1210Coefficients: 1211 Estimate Std. Error z-value Pr(>|z|) 1212(Intercept) 6.0131e+00 6.0968e-02 98.629 < 2.2e-16 *** 1213wks 3.6313e-03 1.2032e-03 3.018 0.002545 ** 1214marriedyes 3.1837e-01 1.6118e-02 19.753 < 2.2e-16 *** 1215exp 3.6882e-02 2.4393e-03 15.120 < 2.2e-16 *** 1216I(exp^2) -6.4913e-04 5.3708e-05 -12.086 < 2.2e-16 *** 1217bluecolyes -3.2165e-01 1.2368e-02 -26.007 < 2.2e-16 *** 1218--- 1219Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1220 1221Total Sum of Squares: 886.9 1222Residual Sum of Squares: 654.8 1223R-Squared: 0.2617 1224Adj. R-Squared: 0.26081 1225Chisq: 1474.21 on 5 DF, p-value: < 2.22e-16 1226> 1227> IVbalt_t <- plm(form_wage_iv2, 1228+ data = pWages, 1229+ model = "random", inst.method = "baltagi", effect = "time") 1230> summary(IVbalt_t) 1231Oneway (time) effect Random Effect Model 1232 (Swamy-Arora's transformation) 1233Instrumental variable estimation 1234 (Baltagi's transformation) 1235 1236Call: 1237plm(formula = form_wage_iv2, data = pWages, effect = "time", 1238 model = "random", inst.method = "baltagi") 1239 1240Balanced Panel: n = 595, T = 7, N = 4165 1241 1242Effects: 1243 var std.dev share 1244idiosyncratic 0.1271 0.3565 1 1245time 0.0000 0.0000 0 1246theta: 0 1247 1248Residuals: 1249 Min. 1st Qu. Median 3rd Qu. Max. 1250-5.598694 -0.721869 -0.017043 0.751016 6.062259 1251 1252Coefficients: 1253 Estimate Std. Error z-value Pr(>|z|) 1254(Intercept) 6.01329823 0.06096780 98.6307 < 2.2e-16 *** 1255wks 0.00363128 0.00120324 3.0179 0.002545 ** 1256marriedyes 0.31849295 0.01611823 19.7598 < 2.2e-16 *** 1257exp 0.03685389 0.00243934 15.1081 < 2.2e-16 *** 1258I(exp^2) -0.00064850 0.00005371 -12.0740 < 2.2e-16 *** 1259bluecolyes -0.32165802 0.01236790 -26.0075 < 2.2e-16 *** 1260--- 1261Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1262 1263Total Sum of Squares: 886.9 1264Residual Sum of Squares: 5152.8 1265R-Squared: 0.2617 1266Adj. R-Squared: 0.26081 1267Chisq: 1474.19 on 5 DF, p-value: < 2.22e-16 1268> 1269> IVam_t <- plm(form_wage_iv2, 1270+ data = pWages, 1271+ model = "random", inst.method = "am", effect = "time") 1272> summary(IVam_t) 1273Oneway (time) effect Random Effect Model 1274 (Swamy-Arora's transformation) 1275Instrumental variable estimation 1276 (Amemiya-MaCurdy's transformation) 1277 1278Call: 1279plm(formula = form_wage_iv2, data = pWages, effect = "time", 1280 model = "random", inst.method = "am") 1281 1282Balanced Panel: n = 595, T = 7, N = 4165 1283 1284Effects: 1285 var std.dev share 1286idiosyncratic 0.1271 0.3565 1 1287time 0.0000 0.0000 0 1288theta: 0 1289 1290Residuals: 1291 Min. 1st Qu. Median 3rd Qu. Max. 1292-5.598694 -0.721869 -0.017043 0.751016 6.062259 1293 1294Coefficients: 1295 Estimate Std. Error z-value Pr(>|z|) 1296(Intercept) 6.01329823 0.06096780 98.6307 < 2.2e-16 *** 1297wks 0.00363128 0.00120324 3.0179 0.002545 ** 1298marriedyes 0.31849295 0.01611823 19.7598 < 2.2e-16 *** 1299exp 0.03685389 0.00243934 15.1081 < 2.2e-16 *** 1300I(exp^2) -0.00064850 0.00005371 -12.0740 < 2.2e-16 *** 1301bluecolyes -0.32165802 0.01236790 -26.0075 < 2.2e-16 *** 1302--- 1303Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1304 1305Total Sum of Squares: 886.9 1306Residual Sum of Squares: 5152.8 1307R-Squared: 0.2617 1308Adj. R-Squared: 0.26081 1309Chisq: 1474.19 on 5 DF, p-value: < 2.22e-16 1310> 1311> IVbms_t <- plm(form_wage_iv2, 1312+ data = pWages, 1313+ model = "random", inst.method = "bms", effect = "time") 1314> summary(IVbms_t) 1315Oneway (time) effect Random Effect Model 1316 (Swamy-Arora's transformation) 1317Instrumental variable estimation 1318 (Breusch-Mizon-Schmidt's transformation) 1319 1320Call: 1321plm(formula = form_wage_iv2, data = pWages, effect = "time", 1322 model = "random", inst.method = "bms") 1323 1324Balanced Panel: n = 595, T = 7, N = 4165 1325 1326Effects: 1327 var std.dev share 1328idiosyncratic 0.1271 0.3565 1 1329time 0.0000 0.0000 0 1330theta: 0 1331 1332Residuals: 1333 Min. 1st Qu. Median 3rd Qu. Max. 1334-5.598694 -0.721869 -0.017043 0.751016 6.062259 1335 1336Coefficients: 1337 Estimate Std. Error z-value Pr(>|z|) 1338(Intercept) 6.01329823 0.06096780 98.6307 < 2.2e-16 *** 1339wks 0.00363128 0.00120324 3.0179 0.002545 ** 1340marriedyes 0.31849295 0.01611823 19.7598 < 2.2e-16 *** 1341exp 0.03685389 0.00243934 15.1081 < 2.2e-16 *** 1342I(exp^2) -0.00064850 0.00005371 -12.0740 < 2.2e-16 *** 1343bluecolyes -0.32165802 0.01236790 -26.0075 < 2.2e-16 *** 1344--- 1345Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1346 1347Total Sum of Squares: 886.9 1348Residual Sum of Squares: 5152.8 1349R-Squared: 0.2617 1350Adj. R-Squared: 0.26081 1351Chisq: 1474.19 on 5 DF, p-value: < 2.22e-16 1352> 1353> # texreg::screenreg(list("BVK t" = IVbvk_t, "Baltagi t" = IVbalt_t, "AM t" = IVam_t, "BMS t" = IVbms_t), 1354> # digits = 5) 1355> 1356> ## unbalanced one-way time 1357> IVbvk_t_ubal <- plm(form_wage_iv2, 1358+ data = pWages_ubal, 1359+ model = "random", inst.method = "bvk", effect = "time") 1360> summary(IVbvk_t_ubal) 1361Oneway (time) effect Random Effect Model 1362 (Swamy-Arora's transformation) 1363Instrumental variable estimation 1364 (Balestra-Varadharajan-Krishnakumar's transformation) 1365 1366Call: 1367plm(formula = form_wage_iv2, data = pWages_ubal, effect = "time", 1368 model = "random", inst.method = "bvk") 1369 1370Unbalanced Panel: n = 595, T = 1-7, N = 4149 1371 1372Effects: 1373 var std.dev share 1374idiosyncratic 0.1267 0.3559 1 1375time 0.0000 0.0000 0 1376theta: 1377 Min. 1st Qu. Median Mean 3rd Qu. Max. 1378 0 0 0 0 0 0 1379 1380Residuals: 1381 Min. 1st Qu. Median 3rd Qu. Max. 1382-1.9960628 -0.2575742 -0.0060508 0.2675715 2.1579866 1383 1384Coefficients: 1385 Estimate Std. Error z-value Pr(>|z|) 1386(Intercept) 6.0335e+00 6.1226e-02 98.5444 <2e-16 *** 1387wks 3.3238e-03 1.2072e-03 2.7533 0.0059 ** 1388marriedyes 3.2084e-01 1.6130e-02 19.8905 <2e-16 *** 1389exp 3.6349e-02 2.4441e-03 14.8720 <2e-16 *** 1390I(exp^2) -6.3990e-04 5.3793e-05 -11.8954 <2e-16 *** 1391bluecolyes -3.2373e-01 1.2389e-02 -26.1299 <2e-16 *** 1392--- 1393Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1394 1395Total Sum of Squares: 883.99 1396Residual Sum of Squares: 652.02 1397R-Squared: 0.26241 1398Adj. R-Squared: 0.26152 1399Chisq: 1473.97 on 5 DF, p-value: < 2.22e-16 1400> 1401> IVbalt_t_ubal <- plm(form_wage_iv2, 1402+ data = pWages_ubal, 1403+ model = "random", inst.method = "baltagi", effect = "time") 1404> summary(IVbalt_t_ubal) 1405Oneway (time) effect Random Effect Model 1406 (Swamy-Arora's transformation) 1407Instrumental variable estimation 1408 (Baltagi's transformation) 1409 1410Call: 1411plm(formula = form_wage_iv2, data = pWages_ubal, effect = "time", 1412 model = "random", inst.method = "baltagi") 1413 1414Unbalanced Panel: n = 595, T = 1-7, N = 4149 1415 1416Effects: 1417 var std.dev share 1418idiosyncratic 0.1267 0.3559 1 1419time 0.0000 0.0000 0 1420theta: 1421 Min. 1st Qu. Median Mean 3rd Qu. Max. 1422 0 0 0 0 0 0 1423 1424Residuals: 1425 Min. 1st Qu. Median 3rd Qu. Max. 1426-5.608286 -0.723549 -0.016968 0.751478 6.062804 1427 1428Coefficients: 1429 Estimate Std. Error z-value Pr(>|z|) 1430(Intercept) 6.0336e+00 6.1226e-02 98.5463 < 2.2e-16 *** 1431wks 3.3236e-03 1.2072e-03 2.7532 0.005902 ** 1432marriedyes 3.2096e-01 1.6130e-02 19.8980 < 2.2e-16 *** 1433exp 3.6323e-02 2.4442e-03 14.8609 < 2.2e-16 *** 1434I(exp^2) -6.3932e-04 5.3795e-05 -11.8844 < 2.2e-16 *** 1435bluecolyes -3.2374e-01 1.2389e-02 -26.1307 < 2.2e-16 *** 1436--- 1437Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1438 1439Total Sum of Squares: 883.99 1440Residual Sum of Squares: 5147.1 1441R-Squared: 0.26241 1442Adj. R-Squared: 0.26152 1443Chisq: 1473.99 on 5 DF, p-value: < 2.22e-16 1444> 1445> IVam_t_ubal <- plm(form_wage_iv2, 1446+ data = pWages_ubal, 1447+ model = "random", inst.method = "am", effect = "time") 1448> summary(IVam_t_ubal) 1449Oneway (time) effect Random Effect Model 1450 (Swamy-Arora's transformation) 1451Instrumental variable estimation 1452 (Amemiya-MaCurdy's transformation) 1453 1454Call: 1455plm(formula = form_wage_iv2, data = pWages_ubal, effect = "time", 1456 model = "random", inst.method = "am") 1457 1458Unbalanced Panel: n = 595, T = 1-7, N = 4149 1459 1460Effects: 1461 var std.dev share 1462idiosyncratic 0.1267 0.3559 1 1463time 0.0000 0.0000 0 1464theta: 1465 Min. 1st Qu. Median Mean 3rd Qu. Max. 1466 0 0 0 0 0 0 1467 1468Residuals: 1469 Min. 1st Qu. Median 3rd Qu. Max. 1470-5.608287 -0.723547 -0.016967 0.751478 6.062799 1471 1472Coefficients: 1473 Estimate Std. Error z-value Pr(>|z|) 1474(Intercept) 6.0336e+00 6.1226e-02 98.5463 < 2.2e-16 *** 1475wks 3.3236e-03 1.2072e-03 2.7532 0.005902 ** 1476marriedyes 3.2096e-01 1.6130e-02 19.8980 < 2.2e-16 *** 1477exp 3.6323e-02 2.4442e-03 14.8607 < 2.2e-16 *** 1478I(exp^2) -6.3931e-04 5.3795e-05 -11.8842 < 2.2e-16 *** 1479bluecolyes -3.2374e-01 1.2389e-02 -26.1307 < 2.2e-16 *** 1480--- 1481Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1482 1483Total Sum of Squares: 883.99 1484Residual Sum of Squares: 5147.1 1485R-Squared: 0.26241 1486Adj. R-Squared: 0.26152 1487Chisq: 1473.99 on 5 DF, p-value: < 2.22e-16 1488> 1489> IVbms_t_ubal <- plm(form_wage_iv2, 1490+ data = pWages_ubal, 1491+ model = "random", inst.method = "bms", effect = "time") 1492> summary(IVbms_t_ubal) 1493Oneway (time) effect Random Effect Model 1494 (Swamy-Arora's transformation) 1495Instrumental variable estimation 1496 (Breusch-Mizon-Schmidt's transformation) 1497 1498Call: 1499plm(formula = form_wage_iv2, data = pWages_ubal, effect = "time", 1500 model = "random", inst.method = "bms") 1501 1502Unbalanced Panel: n = 595, T = 1-7, N = 4149 1503 1504Effects: 1505 var std.dev share 1506idiosyncratic 0.1267 0.3559 1 1507time 0.0000 0.0000 0 1508theta: 1509 Min. 1st Qu. Median Mean 3rd Qu. Max. 1510 0 0 0 0 0 0 1511 1512Residuals: 1513 Min. 1st Qu. Median 3rd Qu. Max. 1514-5.608281 -0.723591 -0.017027 0.751556 6.062881 1515 1516Coefficients: 1517 Estimate Std. Error z-value Pr(>|z|) 1518(Intercept) 6.0335e+00 6.1226e-02 98.5452 < 2.2e-16 *** 1519wks 3.3233e-03 1.2072e-03 2.7529 0.005907 ** 1520marriedyes 3.2099e-01 1.6130e-02 19.8997 < 2.2e-16 *** 1521exp 3.6332e-02 2.4442e-03 14.8648 < 2.2e-16 *** 1522I(exp^2) -6.3952e-04 5.3794e-05 -11.8884 < 2.2e-16 *** 1523bluecolyes -3.2374e-01 1.2389e-02 -26.1307 < 2.2e-16 *** 1524--- 1525Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 1526 1527Total Sum of Squares: 883.99 1528Residual Sum of Squares: 5147.1 1529R-Squared: 0.26241 1530Adj. R-Squared: 0.26152 1531Chisq: 1474.18 on 5 DF, p-value: < 2.22e-16 1532> 1533> # texreg::screenreg(list("BVK tu" = IVbvk_t_ubal, "Baltagi tu" = IVbalt_t_ubal, "AM tu" = IVam_t_ubal, "BMS tu" = IVbms_t_ubal), 1534> # digits = 5) 1535> 1536> 1537> ### twoway RE estimation: currently prevented (error informatively) 1538> # IVbvktw <- plm(form_wage_iv2, 1539> # data = pWages, 1540> # model = "random", inst.method = "bvk", effect = "twoways") 1541> # summary(IVbvktw) 1542> # 1543> # IVbalttw <- plm(form_wage_iv2, 1544> # data = pWages, 1545> # model = "random", inst.method = "baltagi", effect = "twoways") 1546> # summary(IVbalttw) 1547> # 1548> # IVamtw <- plm(form_wage_iv2, 1549> # data = pWages, 1550> # model = "random", inst.method = "am", effect = "twoways") 1551> # summary(IVamtw) 1552> # 1553> # IVbmstw <- plm(form_wage_iv2, 1554> # data = pWages, 1555> # model = "random", inst.method = "bms", effect = "twoways") 1556> # summary(IVbmstw) 1557> # 1558> # texreg::screenreg(list("BVK tw" = IVbvktw, "Baltagi tw" = IVbalttw, "AM tw" = IVamtw, "BMS tw" = IVbmstw), 1559> # digits = 5) 1560> 1561> 1562> 1563> proc.time() 1564 user system elapsed 1565 9.32 0.18 9.54 1566