mq_data {emIRT}

Martin-Quinn Judicial Ideology Scores

Description

This data set contains materials related to the Martin-Quinn measures of judicial ideology, estimated for every justice serving from October 1937 to October 2013. The materials are based on the July 23, 2014 (2014 Release 01) release of the Supreme Court Database, which contain the votes of each Supreme Court justice on each case heard in the court. The data is set up for input to dynIRT(), and also includes point estimates of the same model obtained using standard MCMC techniques. Start values and priors input to this model are identical to those used in the MCMC estimates, and were provided by Kevin Quinn.

Usage

data(mq_data)

Format

list, containing the following elements:

data.mq

Justice voting data, formatted for input to dynIRT().

cur.mq

Start values, formatted for input to dynIRT().

priors.mq

Priors, formatted for input to dynIRT().

mq_mcmc

Ideal point estimates with data via MCMC.

justiceName

A vector of names identifying the justice that goes with each estimated ideal point.

References

Original model and the example is based off of Andrew Martin and Kevin Quinn, “Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953-1999.” Political Analysis 10(2) 134-153.

Variational model is described in Kosuke Imai, James Lo, and Jonathan Olmsted (2016). “Fast Estimation of Ideal Points with Massive Data.” American Political Science Review, Vol. 110, No. 4 (December), pp. 631-656.

See Also

'dynIRT'.

Examples

### Replication of Martin-Quinn Judicial Ideology Scores
### Based on July 23, 2014 (2014 Release 01) release of the Supreme Court Database
### Start values and priors based on replication code provided by Kevin Quinn

data(mq_data)

## Estimate dynamic variational model using dynIRT()
lout <- dynIRT(.data = mq_data$data.mq,
                    .starts = mq_data$cur.mq,
                    .priors = mq_data$priors.mq,
                    .control = {list(
                    threads = 1,
                    verbose = TRUE,
                    thresh = 1e-6,
		    maxit=500
                    )})

## Extract estimate from variational model
## Delete point estimates of 0, which are justices missing from that session
vi.out <- c(t(lout$means$x))
vi.out[vi.out==0] <- NA
vi.out <- na.omit(vi.out)

## Compare correlation against MCMC-estimated result
## Correlates at r=0.93 overall, and 0.96 when excluding Douglas
cor(vi.out, mq_data$mq_mcmc)	
cor(vi.out[mq_data$justiceName != "Douglas"],
 mq_data$mq_mcmc[mq_data$justiceName != "Douglas"])