Estimates the second of a sorting model
second_stage(s1.results, data, endog = NULL, instr = NULL)
s1.results | Indicates the (maxLik) object estimation results of the first stage of the sorting model |
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data | Dataset to be used |
endog | Indicates the endogenous variable(s) to be instrumented (from the dataset in parentheses) |
instr | indicates the intrument(s) for the endogenous variable |
An estimation object
This function estimates an OLS or an instrument variable. if an instrument variable procedure is needed both the instrument and the endogenous variables should be given
data <- municipality s1.results <- first_stage(code_name = "mun_code", X_names = c("lnprice","kindergardens_1km","p_mig_west","nature","monuments","cafes_1km"), Z_names = c("income","double_earner_hh","hh_kids","age", "migskill"), data = data, print_detail = 1)#> -------------- #> successive function values within tolerance limit #> 13 iterations #> estimate: -0.5165651 -2.657561 -2.420705 -3.268584 -2.808338 -3.759156 -1.802347 -1.973176 -3.081623 -2.945921 -5.047281 -2.632009 -1.924907 0.6414166 -1.673695 -3.226506 -2.754643 -2.31913 -1.806289 -1.485187 -2.556821 -0.459179 0.9716554 -4.94152 -4.294334 -2.106439 -3.62121 -3.741176 -1.913509 -2.368222 -2.91929 -2.524537 -0.5003825 -3.567914 -0.8614097 -2.499268 -2.568059 -1.241397 -2.247542 -3.512908 -3.546684 -4.108796 -2.516137 -3.583525 -3.79585 -1.205857 -3.642069 -2.655842 -1.543215 -2.794182 -2.722002 -4.293146 -0.1461192 -2.854304 -1.448037 -1.868635 -3.200991 -3.074543 -3.289641 -1.748643 0.01061333 -3.872092 -0.488398 -3.627272 -2.521707 0.1164701 1.303374 -3.657476 -2.059095 -2.352496 -2.945384 -0.1982495 -2.391538 -2.953143 -0.9833586 -2.438837 -2.602777 -0.1934537 -3.127884 -3.477981 -2.757427 -4.04897 -2.859454 -3.27384 -2.495738 -3.258308 -2.576631 -2.390038 -3.196859 -3.2754 -2.5489 -2.376909 0.6323401 -0.03066489 -1.127135 -3.129769 -2.854264 -3.331671 -1.65677 -3.131075 -3.076578 -3.08638 -2.464041 1.437356 -3.573143 -2.271934 -2.472361 -0.4902505 -1.658275 -0.0325562 -4.357199 -1.980575 -3.171135 -4.330092 -3.184853 -3.898743 -4.063602 -3.877196 -2.259392 -3.046433 -3.253111 0.08185438 -1.639986 -1.855899 -3.182199 -2.536376 -0.04241286 0.1200429 -2.707349 -1.702393 -2.324123 -1.815255 -0.07592242 0.1701088 0.3831021 0.05513799 0.2442218 0.03865385 -0.02355409 -0.004849489 0.004649212 -0.07557817 -0.001901987 0.1010169 0.01132105 -0.01898369 0.00581637 0.0003628142 0.1184042 -0.1841824 -0.14097 -0.6152256 0.02022628 0.08026863 -0.01454854 -0.04967246 -0.0296229 -0.002572698 0.03316756 0.0001362216 -0.006167964 0.007078167 -0.0004072235 -0.02627413 #> Function value: -38558.81#> Warning: package ‘bindrcpp’ was built under R version 3.4.1#> [1] "iteration 1" #> [1] "sqrt dif max value: " #> [1] "0.10633" #> [1] "0.06328" #> [1] "0.032851" #> [1] "0.016162" #> [1] "0.0078435" #> [1] "0.0037948" #> [1] "0.0018362" #> [1] "0.00088968" #> [1] "0.00043183" #> [1] "iteration 2" #> [1] "sqrt dif max value: " #> [1] "0.89183" #> [1] "0.82855" #> [1] "0.55575" #> [1] "0.23394" #> [1] "0.086936" #> [1] "0.03435" #> [1] "0.014352" #> [1] "0.0061767" #> [1] "0.0026959" #> [1] "0.0011842" #> [1] "0.00052171" #> [1] "0.00023016" #> [1] "iteration 3" #> [1] "sqrt dif max value: " #> [1] "0.010344" #> [1] "0.0044851" #> [1] "0.0019635" #> [1] "0.00086341" #> [1] "0.00038045" #> 34 307 308 310 312 313 317 321 #> 11.58490 11.61966 11.79417 11.74903 11.66109 11.45457 11.64168 11.60150 #> 327 331 335 339 340 342 344 345 #> 11.69401 11.56534 11.57296 11.58292 11.62929 11.70122 11.63971 11.47382 #> 351 352 353 355 356 358 362 363 #> 11.58350 11.62280 11.61099 11.78080 11.67316 11.66515 11.81651 11.56122 #> 365 370 375 376 377 381 383 384 #> 11.66667 11.61580 11.57166 11.76982 11.98420 11.67313 11.62676 11.77748 #> 385 392 393 394 396 397 402 406 #> 11.51884 11.63168 11.66174 11.56373 11.67680 11.79186 11.69038 11.70083 #> 415 417 424 425 431 437 439 450 #> 11.62559 11.91127 11.74776 11.80202 11.56987 11.72106 11.59927 11.55220 #> 451 453 457 473 478 479 482 484 #> 11.62931 11.68327 11.70689 11.69832 11.62290 11.55747 11.57190 11.60269 #> 489 491 499 501 502 503 504 505 #> 11.57400 11.54253 11.51093 11.69379 11.65937 11.68212 11.56409 11.71965 #> 511 512 513 518 523 530 531 534 #> 11.61047 11.63044 11.55295 11.52689 11.48328 11.64071 11.51508 11.57529 #> 537 542 545 546 547 553 556 559 #> 11.47837 11.59005 11.61410 11.62338 11.65303 11.63343 11.48511 11.57208 #> 568 569 571 575 576 579 580 584 #> 11.60980 11.52972 11.58630 11.80018 11.62270 11.78437 11.54024 11.53447 #> 585 588 589 590 597 599 603 606 #> 11.54243 11.54787 11.53372 11.53999 11.64052 11.46218 11.75103 11.57034 #> 608 610 611 612 613 614 617 620 #> 11.58482 11.46340 11.51924 11.54066 11.63552 11.61058 11.50503 11.61671 #> 622 623 626 627 629 632 637 638 #> 11.42520 11.54274 11.76277 11.50069 11.98522 11.55269 11.58484 11.62854 #> 642 643 644 689 693 694 707 736 #> 11.55934 11.54519 11.55896 11.54902 11.54963 11.56944 11.56971 11.63860 #> 852 880 1525 1581 1621 1672 1696 1783 #> 11.66199 11.58532 11.57425 11.77370 11.54873 11.57897 11.71350 11.51685 #> 1842 1884 1892 1901 1904 1916 1926 #> 11.47692 11.54705 11.58623 11.53475 11.70212 11.68590 11.50918 #> #> Call: #> ivreg(formula = formula_iv, data = data_alt, weights = 1/se.weights) #> #> Residuals: #> Min 1Q Median 3Q Max #> -25.39717 -3.63648 0.07974 4.27760 19.50532 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 322.6482 93.9647 3.434 0.000803 *** #> lnprice -26.9416 7.7619 -3.471 0.000708 *** #> kindergardens_1km 0.3002 0.3559 0.843 0.400537 #> p_mig_west 0.5586 0.1965 2.843 0.005203 ** #> nature 7.6055 3.5850 2.121 0.035810 * #> monuments 0.9410 0.3745 2.513 0.013215 * #> cafes_1km -0.4035 0.1633 -2.470 0.014822 * #> --- #> Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 #> #> Residual standard error: 7.624 on 128 degrees of freedom #> Multiple R-Squared: -4.699, Adjusted R-squared: -4.966 #> Wald test: 3.742 on 6 and 128 DF, p-value: 0.001831 #> #> [1] "Correlation with endogenous variable == 0.6669"s2.results <- second_stage(s1.results, data) s2.results <- second_stage(s1.results, data, "lnprice", phat$sorting_inst)