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  • Search: subject:"high dimensional forecasting models"
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Year of publication
Subject
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Lasso 7 OCMT 7 Forecasting model 5 Presidential election 5 Prognoseverfahren 5 Präsidentschaftswahl 5 USA 5 United States 5 Voting behaviour 5 Wahlverhalten 5 high dimensional forecasting models 5 popular and electoral college votes 5 simultaneity and recursive identification 4 voter turnout 4 Simultaneity and Recursive Identification 2 Forecast 1 High Dimensional Forecasting Models 1 High dimensional forecasting models 1 One covariate at a time multiple testing 1 Popular and Electoral College Votes 1 Popular and electoral college votes 1 Prognose 1 Real-time forecasts 1 Simultaneity 1 Voter Turnout 1 oter turnout 1
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Online availability
All
Free 6 Undetermined 1
Type of publication
All
Book / Working Paper 6 Article 1
Type of publication (narrower categories)
All
Graue Literatur 4 Non-commercial literature 4 Working Paper 4 Arbeitspapier 2 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 7
Author
All
Pesaran, M. Hashem 7 Ahmed, Rashad 4 Song, Hayun 3
Published in...
All
CESifo Working Paper 2 CESifo working papers 2 Cambridge working papers in economics 2 Cambridge-INET working papers 1 International journal of forecasting 1
Source
All
ECONIS (ZBW) 5 EconStor 2
Showing 1 - 7 of 7
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Forecasting 2024 US Presidential Election by States Using County Level Data: Too Close to Call
Pesaran, M. Hashem; Song, Hayun - 2024
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...
Persistent link: https://www.econbiz.de/10015166166
Saved in:
Cover Image
Forecasting 2024 US presidential election by states using county level data : too close to call
Pesaran, M. Hashem; Song, Hayun - 2024
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...
Persistent link: https://www.econbiz.de/10015077850
Saved in:
Cover Image
Forecasting 2024 US presidential election by states using county level data : too close to call
Pesaran, M. Hashem; Song, Hayun - 2024
Persistent link: https://www.econbiz.de/10015481274
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Regional Heterogeneity and U.S. Presidential Elections
Ahmed, Rashad; Pesaran, M. Hashem - 2020
This paper develops a recursive model of voter turnout and voting outcomes at U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It is shown that the relationship between many socioeconomic variables and voting outcomes is not uniform across...
Persistent link: https://www.econbiz.de/10012314902
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Regional heterogeneity and U.S. presidential elections
Ahmed, Rashad; Pesaran, M. Hashem - 2020 - updated 14 October 2020
Persistent link: https://www.econbiz.de/10013206085
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Cover Image
Regional heterogeneity and U.S. presidential elections
Ahmed, Rashad; Pesaran, M. Hashem - 2020
This paper develops a recursive model of voter turnout and voting outcomes at the U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It exploits cross-section variations across U.S. counties and investigates the key determinants of the 2016...
Persistent link: https://www.econbiz.de/10012299498
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Regional heterogeneity and US presidential elections : real-time 2020 forecasts and evaluation
Ahmed, Rashad; Pesaran, M. Hashem - In: International journal of forecasting 38 (2022) 2, pp. 662-687
Persistent link: https://www.econbiz.de/10013348692
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