Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto
{"title":"2020年美国总统大选民意调查中的政权更迭和空间依赖","authors":"Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto","doi":"10.1016/j.spasta.2025.100927","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces a novel two-stage modeling framework that combines Markov Switching (MS) models with an autoregressive model augmented by spatial effects to analyze the dynamics and spatial interdependence of Biden’s polling percentages during the 2020 electoral campaign. In the first stage, we employ MS models to segment each state’s daily polling time series into distinct regimes — interpreted as phases of decline, stability, and growth. This segmentation captures abrupt changes and local trends in public opinion, enabling us to link regime shifts with key political events such as debates, party conventions, and milestone campaign achievements. The inherent nonlinearity of polling data would otherwise be lost by first differencing. By removing the regime-specific components, we generate stationary residuals modeled using an Autoregressive model with exogenous variables (ARX) that incorporates political spatial interactions through two complementary effects. The spillover effect captures lagged influences arising from politically influential states, while the contagion effect reflects the contemporaneous impact of neighboring states. A recursive algorithm based on partial correlations is implemented to select the most relevant spillover sources for each state. Empirical results, based on daily data from 13 swing states, reveal robust evidence of persistent regime structures and marked spatial dependencies. While contagion effects are uniformly significant across states, spillover dynamics exhibit considerable heterogeneity in both magnitude and direction. This integrated modeling approach enhances our understanding of the complex, nonlinear temporal evolution of polling trends and the spatial diffusion of political opinions that underpinned the 2020 electoral outcome.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"69 ","pages":"Article 100927"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regime changes and spatial dependence in the 2020 US presidential election polls\",\"authors\":\"Giampiero M. Gallo , Demetrio Lacava , Edoardo Otranto\",\"doi\":\"10.1016/j.spasta.2025.100927\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper introduces a novel two-stage modeling framework that combines Markov Switching (MS) models with an autoregressive model augmented by spatial effects to analyze the dynamics and spatial interdependence of Biden’s polling percentages during the 2020 electoral campaign. In the first stage, we employ MS models to segment each state’s daily polling time series into distinct regimes — interpreted as phases of decline, stability, and growth. This segmentation captures abrupt changes and local trends in public opinion, enabling us to link regime shifts with key political events such as debates, party conventions, and milestone campaign achievements. The inherent nonlinearity of polling data would otherwise be lost by first differencing. By removing the regime-specific components, we generate stationary residuals modeled using an Autoregressive model with exogenous variables (ARX) that incorporates political spatial interactions through two complementary effects. The spillover effect captures lagged influences arising from politically influential states, while the contagion effect reflects the contemporaneous impact of neighboring states. A recursive algorithm based on partial correlations is implemented to select the most relevant spillover sources for each state. Empirical results, based on daily data from 13 swing states, reveal robust evidence of persistent regime structures and marked spatial dependencies. While contagion effects are uniformly significant across states, spillover dynamics exhibit considerable heterogeneity in both magnitude and direction. This integrated modeling approach enhances our understanding of the complex, nonlinear temporal evolution of polling trends and the spatial diffusion of political opinions that underpinned the 2020 electoral outcome.</div></div>\",\"PeriodicalId\":48771,\"journal\":{\"name\":\"Spatial Statistics\",\"volume\":\"69 \",\"pages\":\"Article 100927\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spatial Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211675325000491\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial Statistics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211675325000491","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Regime changes and spatial dependence in the 2020 US presidential election polls
This paper introduces a novel two-stage modeling framework that combines Markov Switching (MS) models with an autoregressive model augmented by spatial effects to analyze the dynamics and spatial interdependence of Biden’s polling percentages during the 2020 electoral campaign. In the first stage, we employ MS models to segment each state’s daily polling time series into distinct regimes — interpreted as phases of decline, stability, and growth. This segmentation captures abrupt changes and local trends in public opinion, enabling us to link regime shifts with key political events such as debates, party conventions, and milestone campaign achievements. The inherent nonlinearity of polling data would otherwise be lost by first differencing. By removing the regime-specific components, we generate stationary residuals modeled using an Autoregressive model with exogenous variables (ARX) that incorporates political spatial interactions through two complementary effects. The spillover effect captures lagged influences arising from politically influential states, while the contagion effect reflects the contemporaneous impact of neighboring states. A recursive algorithm based on partial correlations is implemented to select the most relevant spillover sources for each state. Empirical results, based on daily data from 13 swing states, reveal robust evidence of persistent regime structures and marked spatial dependencies. While contagion effects are uniformly significant across states, spillover dynamics exhibit considerable heterogeneity in both magnitude and direction. This integrated modeling approach enhances our understanding of the complex, nonlinear temporal evolution of polling trends and the spatial diffusion of political opinions that underpinned the 2020 electoral outcome.
期刊介绍:
Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication.
Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.