Determinants of Murder Rates in the United States
Cross-section data on states in 1950.
data("MurderRates")A data frame containing 44 observations on 8 variables.
Murder rate per 100,000 (FBI estimate, 1950).
Number of convictions divided by number of murders in 1950.
Average number of executions during 1946–1950 divided by convictions in 1950.
Median time served (in months) of convicted murderers released in 1951.
Median family income in 1949 (in 1,000 USD).
Labor force participation rate in 1950 (in percent).
Proportion of population that is non-Caucasian in 1950.
Factor indicating region.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.