Forecasting 2024 US Presidential Election by States Using County Level Data: Too Close to Call

Mohammad Pesaran and Hayun Song

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

Abstract: This document is a follow up to the paper by Ahmed and Pesaran (2020, AP) and reports state-level forecasts for the 2024 US presidential election. It updates the 3,107 county level data used by AP and uses the same machine learning techniques as before to select the variables used in forecasting voter turnout and the Republican vote shares by states for 2024. The models forecast the non-swing states correctly but give mixed results for the swing states (Nevada, Arizona, Wisconsin, Michigan, Pennsylvania, North Carolina, and Georgia). Our forecasts for the swing states do not make use of any polling data but confirm the very close nature of the 2024 election, much closer than APÂ’s predictions for 2020. The forecasts are too close to call.

Keywords: Voter Turnout; Popular and Electoral College Votes; Simultaneity and Recursive Identification; High Dimensional Forecasting Models; Lasso; OCMT (search for similar items in EconPapers)
JEL-codes: C53 C55 D72 (search for similar items in EconPapers)
Date: 2024-10-21
New Economics Papers: this item is included in nep-big, nep-cdm and nep-pol
Note: mhp1
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Working Paper: Forecasting 2024 US Presidential Election by States Using County Level Data: Too Close to Call (2024)
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