AER (version 0.2-2)

MurderRates: Determinants of Murder Rates in the United States

Description

Cross-section data on states in 1950.

Usage

data("MurderRates")

source

Maddala (2001), Table 8.4, p. 330

References

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.

Examples

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))