🔧 What Stacked Regression and Poststratification (SRP) Is:

SRP is a new procedure for estimating local-area public opinion that generalizes classical multilevel regression and poststratification (MRP). It replaces a single parametric first-stage model with a diverse ensemble of predictive algorithms to improve the cross-validated fit of first-stage predictions.

🧩 How the Models Are Combined:

• The ensemble includes multilevel regression, LASSO, k-nearest neighbors, random forest, and gradient boosting.
• These models are stacked so that their predictions are combined to produce improved inputs for poststratification.

🧪 How Performance Was Evaluated:

• A Monte Carlo simulation tests performance under controlled data-generating processes.
• An empirical application assesses local public opinion estimates across a broad range of issue areas using real data.

📈 Key Findings:

• SRP significantly outperforms classical MRP in the Monte Carlo simulation when the data-generating process contains deep interactions.
• This performance gain occurs without requiring the researcher to specify a complex parametric model in advance.
• In the empirical application, SRP produces superior local public opinion estimates across many issue areas, with the advantage especially pronounced when trained on large datasets.
• The ensemble approach improves the cross-validated fit of first-stage predictions, yielding more accurate poststratified estimates.

⚖️ Why It Matters:

• SRP offers a practical way to improve subnational opinion estimates by leveraging machine-learning ensembles while preserving the poststratification framework.
• The method reduces reliance on correctly specifying complex interaction structures and scales well with large datasets, making it useful for researchers and practitioners interested in more accurate local-area public opinion measurement.