Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.08501

Electoral Intelligence: A Neural Zero-Inflated Negative Binomial Model for Forecasting Swedish Municipal Elections

Haodong Qi·Stefano M. Iacus·Pieter Bevelander·Derek S. Hutcheson
View original source ↗Read source document ↗Export metadata ↓

Abstract

Forecasting local elections is difficult because electoral competition is geographically heterogeneous, highly overdispersed, and characterized by many party-municipality combinations with little or no electoral activity. This article develops a bounded neural zero-inflated negative binomial (ZINB) model for forecasting party-level vote counts in Swedish municipal elections. The model combines a count component for expected votes, a zero-inflation component for structural electoral absence, municipality-specific dispersion, and learned embeddings capturing latent party, municipality, and party-municipality structure. Model training relies on data from the 2014, 2018, and 2022 Swedish municipal elections and performance is evaluated on a held-out set of municipalities. Testing results show strong predictive accuracy in both vote counts and vote shares. Explanatory diagnostics indicate that latent party structure, local party-municipality relationships, electoral persistence, organizational capacity, and candidate-electorate representational similarity are among the most important determinants of predictive performance. Following model evaluation and diagnostics, the selected specification is refit on all historical data and applied to generate forecasts for the 2026 municipal elections. The proposed framework provides a flexible approach for studying local electoral competition in multiparty systems characterized by heterogeneous party presence and substantial geographic variation.

Explore related subjects

econ.EMcs.LGstat.AP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haodong Qi, Stefano M. Iacus, Pieter Bevelander, Derek S. Hutcheson. 2026-08-31. Electoral Intelligence: A Neural Zero-Inflated Negative Binomial Model for Forecasting Swedish Municipal Elections. https://arxiv.org/abs/2611.08501

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Inference in clustered IV models with many and weak instruments

This paper studies inference in instrumental variable models with many, weak instruments and clustered data. It first examines how clustering affects identification strength, showing how to adapt the concentration parameter to account for within-cluster dependence. The adapted concentration parameter may be smaller and may grow more slowly asymptotically than the concentration parameter that ignores the dependence, thereby weakening identification. Next, the paper extends the identification-robust jackknife Anderson--Rubin and jackknife score statistics for independent data to clustered data by removing clusters rather than individual observations from the statistics. These statistics converge jointly to a normal distribution under many-instrument asymptotics, such that scaling with cluster cross-fit variance estimators yields identification-robust tests. Simulations show that the cluster jackknife tests achieve close to nominal size and have good power. A reanalysis of a study on the effect of queenly reign on war shows the empirical relevance of the new tests.

econ.EM↗
arXiv2026

Partially identified heteroskedastic SVARs

This paper studies the identification of Structural Vector Autoregressions (SVARs) exploiting a break in the variances of the structural shocks. Point-identification for this class of models relies on an eigen-decomposition involving the covariance matrices of reduced-form errors and requires that all the eigenvalues are distinct. This point-identification, however, fails in the presence of multiplicity of eigenvalues. This occurs in an empirically relevant scenario where, for instance, only a subset of structural shocks had the break in their variances, or where a group of variables shows a variance shift of the same amount. Together with zero or sign restrictions on the structural parameters and impulse responses, we derive the identified sets for impulse responses and show how to compute them. We perform inference on the impulse response functions, building on the robust Bayesian approach developed for set identified SVARs. To illustrate our proposal, we present an empirical example based on the literature on the global crude oil market where the identification is expected to fail due to multiplicity of eigenvalues.

econ.EM↗
arXiv2026

Nonfundamentalness or missing information ? Evidence from causal-noncausal VARs in macro-finance

This paper studies the presence of noncausal dynamics in standard macro-finance VAR models and asks whether they reflect genuine nonfundamentalness or omitted information available to economic agents but unobserved by the econometrician. To that end, we introduce a factor-filtering mixed causal-noncausal VARX approach designed to account for common macroeconomic information. We assess its performance in simulated settings, while showing also that the generalized covariance (GCov) estimator correctly recovers causal and noncausal dynamics when using several lags. Empirically, we revisit the well-known Stock-Watson monetary policy (S)VAR and show that the noncausal components detected in the baseline specification largely disappear once common factors are filtered out. Finally, we compare impulse responses from the filtered and original data to assess the transmission of monetary policy shocks and show that filtering further removes the price puzzle.

econ.EM↗