Model Tobit

Pengolahan Model Tobit dengan Stata Oleh: Akbar Suwardi

Syntax: tobit depvar [indepvars] [if] [in] [weight], ll[(#)] ul[(#)] [options]

dimana: * ll[(#)] adalah left-censoring limit * ul[(#)] adalah right-censoring limit

Gunakan data mroz.dta (753 observasi wanita) untuk melakukan estimasi persamaan penawaran jam kerja (dalam setahun) dari wanita yang telah menikah di bawah ini dengan model Tobit:

hours   0 

  1

nwifeinc

  2

educ   3exper   4 exper 2

  5

age   6 kidslt 6   7 kidsge6  u

Estimasi di Stata dengan Model tobit yang standar (dimana batas kiri hours= 0) dapat dilakukan dengan cara:

tobit

hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0)

Tobit regression

Log likelihood = -3819.0946

Number of obs LR chi2(7) Prob > chi2 Pseudo R2

= = = =

753 271.59 0.0000 0.0343

-----------------------------------------------------------------------------hours | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------nwifeinc | -8.814243 4.459096 -1.98 0.048 -17.56811 -.0603724 educ | 80.64561 21.58322 3.74 0.000 38.27453 123.0167 exper | 131.5643 17.27938 7.61 0.000 97.64231 165.4863 expersq | -1.864158 .5376615 -3.47 0.001 -2.919667 -.8086479 age | -54.40501 7.418496 -7.33 0.000 -68.96862 -39.8414 kidslt6 | -894.0217 111.8779 -7.99 0.000 -1113.655 -674.3887 kidsge6 | -16.218 38.64136 -0.42 0.675 -92.07675 59.64075 _cons | 965.3053 446.4358 2.16 0.031 88.88528 1841.725 -------------+---------------------------------------------------------------/sigma | 1122.022 41.57903 1040.396 1203.647 -----------------------------------------------------------------------------Obs. summary: 325 left-censored observations at hours<=0 428 uncensored observations 0 right-censored observations

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Pengolahan Model Tobit Dengan Stata

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Estimasi di Stata dengan Model tobit yang memliki batas kanan hours=4950 dapat dilakukan dengan cara:

tobit

hours nwifeinc educ exper expersq age kidslt6 kidsge6, ul(4950)

Tobit regression


Number of obs LR chi2(7) Prob > chi2 Pseudo R2

= = = =

753 232.20 0.0000 0.0188

-----------------------------------------------------------------------------hours | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------nwifeinc | -3.442164 2.533697 -1.36 0.175 -8.416189 1.531862 educ | 28.72457 12.90215 2.23 0.026 3.395722 54.05342 exper | 65.74106 9.922842 6.63 0.000 46.26104 85.22108 expersq | -.7017731 .3232376 -2.17 0.030 -1.336337 -.0672095 age | -30.55016 4.346349 -7.03 0.000 -39.08269 -22.01763 kidslt6 | -442.4172 58.60906 -7.55 0.000 -557.4756 -327.3589 kidsge6 | -32.739 23.08236 -1.42 0.157 -78.05311 12.57512 _cons | 1332.196 269.6927 4.94 0.000 802.749 1861.643 -------------+---------------------------------------------------------------/sigma | 747.1395 19.27648 709.2969 784.9821 -----------------------------------------------------------------------------Obs. summary: 0 left-censored observations 752 uncensored observations 1 right-censored observation at hours>=4950

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Estimasi di Stata dengan Model tobit yang memliki batas kiri hours = 0 dan batas kanan hours=4950 dapat dilakukan dengan cara:

tobit

hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0) ul(4950)

regression


Number of obs = LR chi2(7) Prob > chi2 Pseudo R2

753 = = =

271.37 0.0000 0.0344

-----------------------------------------------------------------------------hours | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------nwifeinc | -8.815546 4.470889 -1.97 0.049 -17.59257 -.0385255 educ | 80.70853 21.64041 3.73 0.000 38.22518 123.1919 exper | 131.931 17.33008 7.61 0.000 97.90948 165.9525 expersq | -1.870695 .5391501 -3.47 0.001 -2.929127 -.8122632 age | -54.57207 7.440187 -7.33 0.000 -69.17827 -39.96588 kidslt6 | -896.0375 112.1955 -7.99 0.000 -1116.294 -675.7811 kidsge6 | -16.02997 38.74414 38.74414 -0.41 0.679 -92.0905 60.03055 _cons | 968.154 447.6282 2.16 0.031 89.39313 1846.915 -------------+---------------------------------------------------------------/sigma | 1124.914 41.77934 1042.895 1206.933 -----------------------------------------------------------------------------Obs. summary: 325 left-censored observations at hours<=0 427 uncensored observations 1 right-censored observation at hours>=4950



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Untuk mendapatkan nilai prediksi dan marginal effects (mfx) setelah estimasi tobit dapat dilakukan dengan cara:

tobit hours nwifeinc educ exper expersq age kidslt6 kidsge6, ll(0) ul(4950) predict predict hourshat, hourshat, ystar(0,495 ystar(0,4950) 0) mfx, predic predict t (ystar(0, (ystar(0,4950)) 4950))

Marginal effects after tobit y = E(hours*|0|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------nwifeinc | -5.321742 2.69507 -1.97 0.048 -10.604 -.039507 20.129 educ | 48.72188 12.984 3.75 0.000 23.2731 74.1706 12.2869 12.2869 exper | 79.64371 10.323 7.71 0.000 59.4104 99.877 10.6308 expersq | -1.129296 .32381 -3.49 0.000 -1.76395 -.494641 178.039 age | -32.9439 4.46561 -7.38 0.000 -41.6963 -24.1915 42.5378 kidslt6 | -540.9172 66.738 -8.11 0.000 -671.721 -410.114 .237716 kidsge6 | -9.676925 23.399 -0.41 0.679 -55.5386 36.1847 1.35325 ------------------------------------------------------------------------------

Note: Penjelasan Penjelasan cara membaca output tobit dapat dilihat di Modul Logit dan Probit.

“Jika ada kritik dan saran atas modul ini atau ingin berdiskusi, silahkan email ke

[email protected] Segala kritik dan saran dan sangat berharga bagi penulis. ”



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Model Tobit

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