Stacked Regression and Poststratification. (23rd April 2020)

• Record Type: Journal Article
• Authors: Ornstein, Joseph T.
• Is Part Of: Political analysis. Volume 28:Number 2(2020)
• Page Start: 293
• Page End: 301
• Publication Date: 2020-04-23
• Subjects: public opinion estimation, -- machine learning, -- ensemble methods; Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1017/pan.2019.43
• Languages: English
• ISSNs: 1047-1987

Abstract

Abstract : I develop a procedure for estimating local-area public opinion called stacked regression and poststratification (SRP), a generalization of classical multilevel regression and poststratification (MRP). This procedure employs a diverse ensemble of predictive models—including multilevel regression, LASSO, k-nearest neighbors, random forest, and gradient boosting—to improve the cross-validated fit of the first-stage predictions. In a Monte Carlo simulation, SRP significantly outperforms MRP when there are deep interactions in the data generating process, without requiring the researcher to specify a complex parametric model in advance. In an empirical application, I show that SRP produces superior local public opinion estimates on a broad range of issue areas, particularly when trained on large datasets.