MurderRates {AER} | R Documentation |
Cross-section data on states in 1950.
data("MurderRates")
A data frame containing 44 observations on 8 variables.
Maddala (2001), Table 8.4, p. 330
Maddala, G.S. (2001). Introduction to Econometrics, 3rd ed. New York: John Wiley.
McManus, W.S. (1985). Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs. Journal of Political Economy, 93, 417–425.
Stokes, H. (2004). On the Advantage of Using Two or More Econometric Software Systems to Solve the Same Problem. Journal of Economic and Social Measurement, 29, 307–320.
data("MurderRates") ## Maddala (2001, pp. 331) fm_lm <- lm(rate ~ . + I(executions > 0), data = MurderRates) summary(fm_lm) model <- I(executions > 0) ~ time + income + noncauc + lfp + southern fm_lpm <- lm(model, data = MurderRates) summary(fm_lpm) ## Binomial models. Note: southern coefficient fm_logit <- glm(model, data = MurderRates, family = binomial) summary(fm_logit) fm_logit2 <- glm(model, data = MurderRates, family = binomial, control = list(epsilon = 1e-15, maxit = 50, trace = FALSE)) summary(fm_logit2) fm_probit <- glm(model, data = MurderRates, family = binomial(link = "probit")) summary(fm_probit) fm_probit2 <- glm(model, data = MurderRates , family = binomial(link = "probit"), control = list(epsilon = 1e-15, maxit = 50, trace = FALSE)) summary(fm_probit2) ## Explanation: quasi-complete separation with(MurderRates, table(executions > 0, southern))