Predictive modeling the past

Meredith Paker, Judy Stephenson and Patrick Wallis

Economic History Working Papers from London School of Economics and Political Science, Department of Economic History

Abstract: Understanding long-run economic growth requires reliable historical data, yet the vast majority of long-run economic time series are drawn from incomplete records with significant temporal and geographic gaps. Conventional solutions to these gaps rely on linear regressions that risk bias or overfitting when data are scarce. We introduce “past predictive modeling,” a framework that leverages machine learning and out-of-sample predictive modeling techniques to reconstruct representative historical time series from scarce data. Validating our approach using nominal wage data from England, 1300-1900, we show that this new method leads to more accurate and generalizable estimates, with bootstrapped standard errors 72% lower than benchmark linear regressions. Beyond just bettering accuracy, these improved wage estimates for England yield new insights into the impact of the Black Death on inequality, the economic geography of pre-industrial growth, and productivity over the long-run.

Keywords: machine learning; predictive modeling; wages; black death; industrial revolution (search for similar items in EconPapers)
JEL-codes: C53 J31 N13 N33 N63 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2025-06-13
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:wpaper:128852

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