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Forecasting

Supreme Court of the United States

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A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz Apr 2017

A General Approach For Predicting The Behavior Of The Supreme Court Of The United States, Daniel Katz

All Faculty Scholarship

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries ...


Forecasting The Senate Vote On The Supreme Court Vacancy, Scott J. Basinger, Maxwell Mak Jan 2016

Forecasting The Senate Vote On The Supreme Court Vacancy, Scott J. Basinger, Maxwell Mak

Publications and Research

This paper forecasts current senators’ votes on Merrick Garland’s nomination to the U.S. Supreme Court, in the unlikely case that a vote actually takes place. The forecasts are necessarily conditional, awaiting measurement of the nominee’s characteristics. Nonetheless, a model that combines parameters estimated from existing data with values of some measurable characteristics of senators—particularly their party affiliations, party loyalty levels, and ideological positions—is sufficient to identify potential swing voters in the Senate. By accounting for a more nuanced and refined understanding of the confirmation process, our model reveals that if President Obama were to nominate ...