Medicaid1986 {AER} | R Documentation |
Cross-section data originating from the 1986 Medicaid Consumer Survey. The data comprise two groups of Medicaid eligibles at two sites in California (Santa Barbara and Ventura counties): a group enrolled in a managed care demonstration program and a fee-for-service comparison group of non-enrollees.
data("Medicaid1986")
A data frame containing 996 observations on 14 variables.
"cauc"
or "other"
)."afdc"
) or
non-institutionalized Supplementary Security Income ("ssi"
).Journal of Applied Econometrics Data Archive.
http://qed.econ.queensu.ca/jae/1997-v12.3/gurmu/
Gurmu, S. (1997). Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization. Journal of Applied Econometrics, 12, 225–242.
## data and packages data("Medicaid1986") library("pscl") ## scale regressors Medicaid1986$age2 <- Medicaid1986$age^2 / 100 Medicaid1986$school <- Medicaid1986$school / 10 Medicaid1986$income <- Medicaid1986$income / 10 ## subsets afdc <- subset(Medicaid1986, program == "afdc")[, c(1, 3:4, 15, 5:9, 11:13)] ssi <- subset(Medicaid1986, program == "ssi")[, c(1, 3:4, 15, 5:13)] ## Gurmu (1997): ## Table VI., Poisson and negbin models afdc_pois <- glm(visits ~ ., data = afdc, family = poisson) summary(afdc_pois) coeftest(afdc_pois, vcov = sandwich) afdc_nb <- glm.nb(visits ~ ., data = afdc) ssi_pois <- glm(visits ~ ., data = ssi, family = poisson) ssi_nb <- glm.nb(visits ~ ., data = ssi) ## Table VII., Hurdle models (without semi-parametric effects) afdc_hurdle <- hurdle(visits ~ . | . - access, data = afdc, dist = "negbin") ssi_hurdle <- hurdle(visits ~ . | . - access, data = ssi, dist = "negbin") ## Table VIII., Observed and expected frequencies round(cbind( Observed = table(afdc$visits)[1:8], Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(afdc_pois)))), Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(afdc_nb), size = afdc_nb$theta))), Hurdle = colSums(predict(afdc_hurdle, type = "prob")[,1:8]) )/nrow(afdc), digits = 3) * 100 round(cbind( Observed = table(ssi$visits)[1:8], Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(ssi_pois)))), Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(ssi_nb), size = ssi_nb$theta))), Hurdle = colSums(predict(ssi_hurdle, type = "prob")[,1:8]) )/nrow(ssi), digits = 3) * 100