我正在处理一些数据,这些数据仅限于有两个或多个孩子的未婚妇女,以了解税收政策是否会影响其年度工作时间。
但是,当我包含时间变量(在本例中为h_year(6年列表:2008、2009、2010、2012、2013、2014)时,与我的预期有很大不同,轨道模型中的所有解释变量都变为无穷大。很奇怪。
下面是代码和摘要统计信息:
tobit_model
summary(tobit_mod)
这给了我一个汇总表,看起来像:
Call:
censReg(formula = yworking_hrs ~ other_income_val + fownu6 +
nonwhite + a_age + age_sqrd + year_education + year_educ_sqrd +
h_year + gestcen + unemp + minwage + three_kids + post_arra +
three_x_arra,left = 0,data = Mcps_HS)
Observations:
Total Left-censored Uncensored Right-censored
1666 548 1118 0
Coefficients:
Estimate Std. error t value Pr(> t)
(Intercept) -6139.205 Inf 0 1
other_income_val 61.544 Inf 0 1
fownu6 -139.087 Inf 0 1
nonwhite -99.584 Inf 0 1
a_age 103.132 Inf 0 1
age_sqrd -1.198 Inf 0 1
year_education -96.878 Inf 0 1
year_educ_sqrd 6.655 Inf 0 1
h_year 2.059 Inf 0 1
gestcen 3.149 Inf 0 1
unemp -45.452 Inf 0 1
minwage 215.183 Inf 0 1
three_kids -189.103 Inf 0 1
post_arra -73.396 Inf 0 1
three_x_arra 51.153 Inf 0 1
logSigma 7.167 Inf 0 1
Newton-Raphson maximisation,8 iterations
Return code 2: successive function values within tolerance limit
Log-likelihood: -10113.75 on 16 Df
相比之下,如果我排除h_year,那么它似乎可以正常工作。
Call:
censReg(formula = yworking_hrs ~ other_income_val + fownu6 +
nonwhite + a_age + age_sqrd + year_education + year_educ_sqrd +
gestcen + unemp + minwage + three_kids + post_arra + three_x_arra,data = Mcps_BH)
Observations:
Total Left-censored Uncensored Right-censored
9461 1680 7781 0
Coefficients:
Estimate Std. error t value Pr(> t)
(Intercept) -5.632e+03 1.260e+03 -4.471 7.79e-06 ***
other_income_val -1.102e+01 8.401e+00 -1.312 0.189474
fownu6 -3.152e+01 1.506e+01 -2.093 0.036392 *
nonwhite -3.117e+01 2.464e+01 -1.265 0.205814
a_age 1.421e+02 9.495e+00 14.969 < 2e-16 ***
age_sqrd -1.711e+00 1.361e-01 -12.574 < 2e-16 ***
year_education 5.283e+02 1.520e+02 3.477 0.000508 ***
year_educ_sqrd -1.439e+01 4.756e+00 -3.025 0.002489 **
gestcen 6.562e-01 4.265e-01 1.539 0.123868
unemp -2.722e+01 6.489e+00 -4.195 2.73e-05 ***
minwage -1.269e+01 5.123e+01 -0.248 0.804385
three_kids -6.690e+01 3.394e+01 -1.971 0.048703 *
post_arra -1.621e+01 2.918e+01 -0.556 0.578497
three_x_arra -7.084e+00 4.796e+01 -0.148 0.882568
logSigma 6.947e+00 8.476e-03 819.658 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Newton-Raphson maximisation,14 iterations
Return code 2: successive function values within tolerance limit
Log-likelihood: -67075.13 on 15 Df
有人可以帮忙吗?
谢谢!
(此外,我想知道logSigma代表什么。即使我参加了计量经济学,我也没有听说过。)