Chapter 21 Further Reading: Building a Simple Election Model

Foundational Texts

Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. Penguin Press. Essential reading for anyone building forecasting models. Chapters on political forecasting, calibration, and the distinction between uncertainty and ignorance directly inform the architecture developed in this chapter.

Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. The statistical foundation for the regression-based approaches underlying fundamentals models and MRP. Chapter 22 on forecasting is particularly relevant.

Erikson, R.S., & Wlezien, C. (2012). The Timeline of Presidential Elections: How Campaigns Do (and Do Not) Matter. University of Chicago Press. Empirical analysis of how the predictive value of polls vs. fundamentals shifts as Election Day approaches — the theoretical basis for the dynamic blending weight discussed in this chapter.

Poll Aggregation Methods

Linzer, D.A. (2013). "Dynamic Bayesian Forecasting of Presidential Elections in the States." Journal of the American Statistical Association, 108(501), 124–134. The academic paper that underlies many state-level election forecasting approaches; introduces dynamic state-space models for tracking evolving poll averages.

Strauss, A. (2007). "Florida or Ohio? Forecasting Presidential State Outcomes Using Pre-election Polls." Political Research Quarterly, 60(2), 237–248. Examines the predictive validity of poll aggregation at the state level with attention to data quality weighting.

FiveThirtyEight (2020). How FiveThirtyEight's 2020 Presidential Forecast Works — And What's Different Because of COVID-19. FiveThirtyEight. Detailed non-technical description of a professional aggregator's methodology including pollster quality rating, correlated state errors, and fundamentals integration. Available at fivethirtyeight.com.

Fundamentals Models

Abramowitz, A.I. (2008). "Forecasting the 2008 Presidential Election with the Time-for-Change Model." PS: Political Science & Politics, 41(4), 691–695. Describes one of the most influential simple fundamentals models — incorporating presidential approval, GDP growth, and incumbency. The methodological simplicity makes it an excellent reference implementation.

Campbell, J.E. (2008). "The Trial-Heat and Seats-in-Trouble Forecasts of the 2008 Presidential and Congressional Elections." PS: Political Science & Politics, 41(4), 697–701. Congressional seat forecasting using fundamentals — directly relevant to Chapter 22's down-ballot extension.

Hibbs, D.A. (2000). "Bread and Peace Voting in U.S. Presidential Elections." Public Choice, 104(1-2), 149–180. Economic voting theory with a minimalist fundamentals model based on income growth and war casualties. Demonstrates how few inputs a parsimonious fundamentals model actually requires.

Monte Carlo Methods and Uncertainty

Robert, C., & Casella, G. (2004). Monte Carlo Statistical Methods. (2nd ed.) Springer. The technical reference for Monte Carlo methods; more mathematical than required for this chapter but essential for understanding the theoretical basis of simulation-based inference.

Taleb, N.N. (2001). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Texere. Accessible introduction to the epistemology of probabilistic thinking — directly relevant to the chapter's theme of distinguishing genuine uncertainty from false precision.

Gelman, A., et al. (2013). Bayesian Data Analysis. (3rd ed.) CRC Press. Chapter 6 on model checking and posterior predictive distributions is the theoretical foundation for the sensitivity analysis approaches described in this chapter.

Python Tools for Election Modeling

McKinney, W. (2022). Python for Data Analysis. (3rd ed.) O'Reilly Media. Comprehensive reference for pandas, the primary data manipulation library used in this chapter.

VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. Covers numpy, pandas, matplotlib, and scipy — all libraries used in the chapter's Python files. Available free online at jakevdp.github.io.

Harris, C.R., et al. (2020). "Array Programming with NumPy." Nature, 585, 357–362. The foundational paper for numpy; useful for understanding the vectorized operations underlying the Monte Carlo simulation.

Applied Electoral Forecasting

Heidemanns, M., Gelman, A., & Morris, G.E. (2020). "An Updated Dynamic Bayesian Forecasting Model for the US Presidential Election." Harvard Data Science Review, 2(4). Describes The Economist's 2020 presidential model with explicit discussion of fundamentals integration and uncertainty quantification — the closest published model to what this chapter builds.

Wasserman, D. (Annual). "How to Read 2024 Election Night Returns." The Cook Political Report. Annual guides to election night interpretation, including discussion of polling models and their limitations. Excellent practical complement to the modeling theory in this chapter.

Cuzán, A.G. (2015). "Five laws of politics." PS: Political Science & Politics, 48(3), 415–419. Empirical regularities in electoral outcomes that inform fundamentals model construction: incumbency effects, economic voting, midterm patterns.