AlcoholTobacco.RdData on budget shares of alcohol and tobacco for 2724 Belgian households, taken from the Belgian household budget survey of 1995/96.
data("AlcoholTobacco", package = "htobit")
A data frame containing 2724 observations on 9 variables.
Budget share of alcohol.
Budget share of tobacco.
Total expenditure.
10-year age intervals ranging from 0 (younger than 30) to 4 (60 or older).
Number of adults.
Number of old kids (2 years or older).
Number of young kinds (younger than 2 years).
Factor indicating occupation of household head
("blue" collar worker, "white" collar worker, or "other").
Factor indicating region of residence (Dutch-speaking "flanders",
French-speaking "wallonie", or bilingual "brussels").
Supplementary material for Verbeek (2004), originally provided by the National Institute of Statistics (NIS), Belgium.
Verbeek, M. (2004). A Guide to Modern Econometrics, 2nd ed. Chichester, UK: John Wiley.
data("AlcoholTobacco", package = "htobit") ## homoscedastic tobit model for budget share of alcohol ma <- htobit(alcohol ~ (age + adults) * log(expenditure) + oldkids + youngkids, data = AlcoholTobacco) summary(ma)#> #> Call: #> htobit(formula = alcohol ~ (age + adults) * log(expenditure) + oldkids + #> youngkids, data = AlcoholTobacco) #> #> Standardized residuals: #> Min 1Q Median 3Q Max #> -1.0698 -0.4407 -0.1364 0.3934 8.3170 #> #> Coefficients (location model): #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -0.1591533 0.0437782 -3.635 0.000278 *** #> age 0.0134938 0.0108824 1.240 0.214989 #> adults 0.0291901 0.0169469 1.722 0.084989 . #> log(expenditure) 0.0126679 0.0032156 3.939 8.17e-05 *** #> oldkids -0.0026408 0.0006049 -4.366 1.27e-05 *** #> youngkids -0.0038789 0.0023835 -1.627 0.103651 #> age:log(expenditure) -0.0008093 0.0008006 -1.011 0.312067 #> adults:log(expenditure) -0.0022484 0.0012232 -1.838 0.066051 . #> #> Coefficients (scale model with log link): #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -3.71236 0.01533 -242.1 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Log-likelihood: 4755 on 9 Df #> Number of iterations in BFGS optimization: 16## homoscedastic tobit model for budget share of tobacco mt <- htobit(tobacco ~ (age + adults) * log(expenditure) + oldkids + youngkids, data = AlcoholTobacco) summary(mt)#> #> Call: #> htobit(formula = tobacco ~ (age + adults) * log(expenditure) + oldkids + #> youngkids, data = AlcoholTobacco) #> #> Standardized residuals: #> Min 1Q Median 3Q Max #> -0.9993 0.3364 0.5176 0.7105 4.2755 #> #> Coefficients (location model): #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) 0.5899799 0.0930472 6.341 2.29e-10 *** #> age -0.1258528 0.0241330 -5.215 1.84e-07 *** #> adults 0.0153699 0.0379745 0.405 0.68567 #> log(expenditure) -0.0444314 0.0068613 -6.476 9.44e-11 *** #> oldkids 0.0042697 0.0013242 3.224 0.00126 ** #> youngkids -0.0099719 0.0054708 -1.823 0.06834 . #> age:log(expenditure) 0.0088221 0.0017798 4.957 7.17e-07 *** #> adults:log(expenditure) -0.0006007 0.0027448 -0.219 0.82675 #> #> Coefficients (scale model with log link): #> Estimate Std. Error z value Pr(>|z|) #> (Intercept) -3.03666 0.02465 -123.2 <2e-16 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Log-likelihood: 758.7 on 9 Df #> Number of iterations in BFGS optimization: 31