Also available in Stata and Python versions
Load libraries
library(wooldridge)
library(haven)
library(stargazer)
library(plm)
library(censReg)
library(truncreg)
library(sampleSelection)Annual Hours Equation for Married Women
sOLS <- lm(hours ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz)
sTobit <- censReg(hours ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz)
stargazer(sOLS, sTobit, title="Table 17.1 OLS and Tobit Estimation of Annual Hours Worked", no.space=TRUE, type="text")##
## Table 17.1 OLS and Tobit Estimation of Annual Hours Worked
## =======================================================
## Dependent variable:
## -----------------------------------
## hours
## OLS censored
## regression
## (1) (2)
## -------------------------------------------------------
## nwifeinc -3.447 -8.814**
## (2.544) (4.459)
## educ 28.761** 80.646***
## (12.955) (21.583)
## exper 65.673*** 131.564***
## (9.963) (17.279)
## expersq -0.700** -1.864***
## (0.325) (0.538)
## age -30.512*** -54.405***
## (4.364) (7.419)
## kidslt6 -442.090*** -894.022***
## (58.847) (111.878)
## kidsge6 -32.779 -16.218
## (23.176) (38.641)
## logSigma 7.023***
## (0.037)
## Constant 1,330.482*** 965.305**
## (270.785) (446.436)
## -------------------------------------------------------
## Observations 753 753
## R2 0.266
## Adjusted R2 0.259
## Log Likelihood -3,819.095
## Akaike Inf. Crit. 7,656.189
## Bayesian Inf. Crit. 7,697.806
## Residual Std. Error 750.179 (df = 745)
## F Statistic 38.495*** (df = 7; 745)
## =======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01summary(margEff(sTobit))## Marg. Eff. Std. Error t value Pr(>|t|)
## nwifeinc -5.32644 2.69073 -1.9796 0.0481217 *
## educ 48.73409 12.96341 3.7594 0.0001837 ***
## exper 79.50423 10.30497 7.7151 3.886e-14 ***
## expersq -1.12651 0.32326 -3.4848 0.0005213 ***
## age -32.87692 4.45770 -7.3753 4.383e-13 ***
## kidslt6 -540.25683 66.62393 -8.1091 2.220e-15 ***
## kidsge6 -9.80053 23.36134 -0.4195 0.6749580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1scale_factor <- 5.19/8.81
scale_factor## [1] 0.5891033Testing Exogeneity of Other Income in the Hours Equation
v2 <- resid(IV <- lm(nwifeinc ~ huseduc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz))
summary(censReg(hours ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6 + v2, data = mroz))##
## Call:
## censReg(formula = hours ~ nwifeinc + educ + exper + expersq +
## age + kidslt6 + kidsge6 + v2, data = mroz)
##
## Observations:
## Total Left-censored Uncensored Right-censored
## 753 325 428 0
##
## Coefficients:
## Estimate Std. error t value Pr(> t)
## (Intercept) 722.10317 475.68932 1.518 0.129011
## nwifeinc -31.48215 16.03761 -1.963 0.049644 *
## educ 116.78139 32.75981 3.565 0.000364 ***
## exper 124.34877 17.87504 6.957 3.49e-12 ***
## expersq -1.89720 0.53716 -3.532 0.000413 ***
## age -46.89244 8.95768 -5.235 1.65e-07 ***
## kidslt6 -867.91310 112.90250 -7.687 1.50e-14 ***
## kidsge6 -6.32605 39.16565 -0.162 0.871684
## v2 24.41832 16.58454 1.472 0.140925
## logSigma 7.02094 0.03705 189.485 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Newton-Raphson maximisation, 7 iterations
## Return code 1: gradient close to zero
## Log-likelihood: -3818.012 on 10 DfAnnual Hours Equation for Married Women
summary(Participation <- glm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz, family=binomial(link="probit")))##
## Call:
## glm(formula = inlf ~ nwifeinc + educ + exper + expersq + age +
## kidslt6 + kidsge6, family = binomial(link = "probit"), data = mroz)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2156 -0.9151 0.4315 0.8653 2.4553
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2700736 0.5080782 0.532 0.59503
## nwifeinc -0.0120236 0.0049392 -2.434 0.01492 *
## educ 0.1309040 0.0253987 5.154 2.55e-07 ***
## exper 0.1233472 0.0187587 6.575 4.85e-11 ***
## expersq -0.0018871 0.0005999 -3.145 0.00166 **
## age -0.0528524 0.0084624 -6.246 4.22e-10 ***
## kidslt6 -0.8683247 0.1183773 -7.335 2.21e-13 ***
## kidsge6 0.0360056 0.0440303 0.818 0.41350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1029.7 on 752 degrees of freedom
## Residual deviance: 802.6 on 745 degrees of freedom
## AIC: 818.6
##
## Number of Fisher Scoring iterations: 4summary(NormalH <- truncreg(hours ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz, point=0, direction = "left"))##
## Call:
## truncreg(formula = hours ~ nwifeinc + educ + exper + expersq +
## age + kidslt6 + kidsge6, data = mroz, point = 0, direction = "left")
##
## BFGS maximization method
## 57 iterations, 0h:0m:0s
## g'(-H)^-1g = 60.4
##
##
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 1318.8208 884.1668 1.4916 0.13580
## nwifeinc -25.8081 10.3958 -2.4825 0.01305 *
## educ 84.3128 41.2027 2.0463 0.04073 *
## exper 402.7480 55.7661 7.2221 5.118e-13 ***
## expersq -6.0773 1.2317 -4.9341 8.052e-07 ***
## age -148.9702 22.2083 -6.7079 1.975e-11 ***
## kidslt6 -590.1136 236.2784 -2.4975 0.01251 *
## kidsge6 -70.7249 74.4038 -0.9506 0.34183
## sigma 1203.1805 80.9263 14.8676 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-Likelihood: -5611.6 on 9 Df# summary(LogNormalH <- truncreg(log(hours) ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz, point=0, direction = "left"))
summary(Heckman <-selection(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, log(hours) ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data=mroz, method="2step"))## --------------------------------------------
## Tobit 2 model (sample selection model)
## 2-step Heckman / heckit estimation
## 753 observations (325 censored and 428 observed)
## 19 free parameters (df = 735)
## Probit selection equation:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.270077 0.508593 0.531 0.59556
## nwifeinc -0.012024 0.004840 -2.484 0.01320 *
## educ 0.130905 0.025254 5.183 2.82e-07 ***
## exper 0.123348 0.018716 6.590 8.37e-11 ***
## expersq -0.001887 0.000600 -3.145 0.00173 **
## age -0.052853 0.008477 -6.235 7.63e-10 ***
## kidslt6 -0.868328 0.118522 -7.326 6.24e-13 ***
## kidsge6 0.036005 0.043477 0.828 0.40786
## Outcome equation:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.683e+00 6.711e-01 12.939 <2e-16 ***
## nwifeinc 5.234e-03 6.572e-03 0.796 0.4261
## educ -1.118e-01 4.854e-02 -2.304 0.0215 *
## exper -1.182e-03 4.721e-02 -0.025 0.9800
## expersq -4.647e-05 9.210e-04 -0.050 0.9598
## age 6.047e-03 1.892e-02 0.320 0.7494
## kidslt6 -7.092e-02 3.199e-01 -0.222 0.8246
## kidsge6 -9.052e-02 4.548e-02 -1.990 0.0469 *
## Multiple R-Squared:0.1719, Adjusted R-Squared:0.1561
## Error terms:
## Estimate Std. Error t value Pr(>|t|)
## invMillsRatio -1.1224 0.6230 -1.801 0.072 .
## sigma 1.1739 NA NA NA
## rho -0.9561 NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## --------------------------------------------Panel Data Estimation of Annual Hours Equation for Women
#library(haven)
df <- read_dta("Wooldridge_2E/psid80_92.dta")
dfP <- pdata.frame(df, index = c("id", "year"))
Linear_FE <- plm(hours ~ nwifeinc + ch0_2 + ch3_5 + ch6_17 + marr + factor(year), data=dfP, model="within")
RE_Tobit <- censReg(hours ~ nwifeinc + ch0_2 + ch3_5 + ch6_17 + marr + factor(year), data=dfP, left = 0, method="BHHH")
CRE_Tobit <- censReg(hours ~ nwifeinc + ch0_2 + ch3_5 + ch6_17 + marr + factor(year) + nwifeincb + ch0_2b + ch3_5b + ch6_17b + marrb, data=dfP, left = 0, method="BHHH")
stargazer(Linear_FE, RE_Tobit, CRE_Tobit, title="Table 17.3 Panel Data Models for Annual Women's Labor Supply, 1980-1992", no.space=TRUE, type="text")##
## Table 17.3 Panel Data Models for Annual Women's Labor Supply, 1980-1992
## ========================================================================
## Dependent variable:
## ----------------------------------------------------
## hours
## panel censored
## linear regression
## (1) (2) (3)
## ------------------------------------------------------------------------
## nwifeinc -0.775*** -1.892*** -1.577***
## (0.199) (0.143) (0.227)
## ch0_2 -342.377*** -440.759*** -471.742***
## (17.023) (14.171) (15.516)
## ch3_5 -254.128*** -299.439*** -328.120***
## (14.670) (10.802) (11.502)
## ch6_17 -42.958*** -20.217*** -45.540***
## (8.152) (4.959) (6.174)
## marr -634.805*** -575.380*** -771.706***
## (116.420) (19.091) (68.596)
## factor(year)81 -4.820 -7.240 -5.718
## (23.787) (51.395) (50.648)
## factor(year)82 -14.888 -39.979 -36.302
## (23.795) (39.848) (39.593)
## factor(year)83 6.613 -9.888 -7.560
## (23.814) (42.324) (41.869)
## factor(year)84 93.791*** 101.147** 101.565**
## (23.847) (41.238) (40.903)
## factor(year)85 88.737*** 91.105** 95.283**
## (23.900) (42.506) (42.043)
## factor(year)86 82.662*** 85.978** 83.203**
## (23.932) (41.260) (40.811)
## factor(year)87 64.285*** 51.638 56.972
## (24.022) (44.761) (44.232)
## factor(year)88 63.792*** 60.385 50.785
## (24.131) (45.873) (45.586)
## factor(year)89 72.985*** 75.422* 78.550*
## (24.220) (44.009) (43.974)
## factor(year)90 71.250*** 81.835* 72.446*
## (24.297) (43.929) (43.107)
## factor(year)91 64.680*** 76.771* 69.739*
## (24.399) (41.540) (41.151)
## factor(year)92 16.012 18.735 8.011
## (24.516) (36.776) (36.746)
## nwifeincb -5.890***
## (0.418)
## ch0_2b -292.789***
## (87.255)
## ch3_5b 552.908***
## (83.706)
## ch6_17b 51.703***
## (13.410)
## marrb 477.199***
## (73.540)
## logSigmaMu 6.561*** 6.504***
## (0.009) (0.009)
## logSigmaNu 6.449*** 6.440***
## (0.003) (0.003)
## Constant 1,426.356*** 1,431.644***
## (30.553) (31.740)
## ------------------------------------------------------------------------
## Observations 11,674 11,674 11,674
## R2 0.072
## Adjusted R2 -0.007
## Log Likelihood -70,851.510 -70,820.850
## Akaike Inf. Crit. 141,743.000 141,691.700
## Bayesian Inf. Crit. 141,890.300 141,875.800
## F Statistic 48.993*** (df = 17; 10759)
## ========================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01