I'm dealing with some data that is restricted to unmarried women with two or more children to find out if a tax policy affects their annual hours of work.
But, pretty much different from my expectation, when I include a time variable (in this case, h_year (it's a list of 6 years: 2008, 2009, 2010, 2012, 2013, 2014), the standard errors of the all explanatory variables in the tobit model goes to infinity. Very weird.
So below is the code and summary statistics:
tobit_model <- censReg(yworking_hrs ~ other_income_val + fownu6 + nonwhite + a_age + age_sqrd + year_education + year_educ_sqrd + h_year + gestcen + three_kids + post_arra + three_x_arra, data = Mcps_HS, left=0)
summary(tobit_mod)
This gives me a summary table that looks like:
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
By contrast, if I exclude h_year, then it seems it works normally.
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,
left = 0, 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
Can anyone help this?
Thanks!
(plus, I'm wondering what logSigma stands for. Even though I've taken Econometrics, I haven't heard about that..)
