IDEAS home Printed from https://ideas.repec.org/r/eee/intfor/v31y2015i3p980-991.html
   My bibliography  Save this item

Forecasting elections with non-representative polls

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Fronzetti Colladon, Andrea, 2020. "Forecasting election results by studying brand importance in online news," International Journal of Forecasting, Elsevier, vol. 36(2), pages 414-427.
  2. Kolcava, Dennis, 2020. "Do citizens hold business accountable for greenwashing by demanding more government intervention?," OSF Preprints sj4dk, Center for Open Science.
  3. J. N. K. Rao, 2021. "On Making Valid Inferences by Integrating Data from Surveys and Other Sources," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 242-272, May.
  4. Sebasti'an Morales & Charles Thraves, 2020. "On the Resource Allocation for Political Campaigns," Papers 2012.02856, arXiv.org.
  5. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
  6. Nina Cesare & Hedwig Lee & Tyler McCormick & Emma Spiro & Emilio Zagheni, 2018. "Promises and Pitfalls of Using Digital Traces for Demographic Research," Demography, Springer;Population Association of America (PAA), vol. 55(5), pages 1979-1999, October.
  7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  8. Pedro Santander & Rodrigo Alfaro & Héctor Allende-Cid & Claudio Elórtegui & Cristian González, 2020. "Analyzing social media, analyzing the social? A methodological discussion about the demoscopic and predictive potential of social media," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(3), pages 903-923, June.
  9. Mark Richard & Jan Vecer, 2021. "Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis," Risks, MDPI, vol. 9(2), pages 1-20, February.
  10. Ana María Recio-Vivas & Isabel Font-Jiménez & José Miguel Mansilla-Domínguez & Angel Belzunegui-Eraso & David Díaz-Pérez & Laura Lorenzo-Allegue & David Peña-Otero, 2022. "Fear and Attitude towards SARS-CoV-2 (COVID-19) Infection in Spanish Population during the Period of Confinement," IJERPH, MDPI, vol. 19(2), pages 1-15, January.
  11. Jincheng Jiang & Jinsong Chen & Wei Tu & Chisheng Wang, 2019. "A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development," Sustainability, MDPI, vol. 11(22), pages 1-18, November.
  12. John L. Czajka & Amy Beyler, "undated". "Declining Response Rates in Federal Surveys: Trends and Implications (Background Paper)," Mathematica Policy Research Reports a714f76e878f4a74a6ad9f15d, Mathematica Policy Research.
  13. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona School of Economics.
  14. Ahmed, Rashad & Pesaran, M. Hashem, 2022. "Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation," International Journal of Forecasting, Elsevier, vol. 38(2), pages 662-687.
  15. Montalvo, José G. & Papaspiliopoulos, Omiros & Stumpf-Fétizon, Timothée, 2019. "Bayesian forecasting of electoral outcomes with new parties’ competition," European Journal of Political Economy, Elsevier, vol. 59(C), pages 52-70.
  16. Sebastián Morales & Charles Thraves, 2021. "On the Resource Allocation for Political Campaigns," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4140-4159, November.
  17. Brown, Alasdair & Reade, J. James & Vaughan Williams, Leighton, 2019. "When are prediction market prices most informative?," International Journal of Forecasting, Elsevier, vol. 35(1), pages 420-428.
  18. Sakshaug Joseph W. & Wiśniowski Arkadiusz & Ruiz Diego Andres Perez & Blom Annelies G., 2019. "Supplementing Small Probability Samples with Nonprobability Samples: A Bayesian Approach," Journal of Official Statistics, Sciendo, vol. 35(3), pages 653-681, September.
  19. Marcin Hitczenko, 2021. "Sample Bias Related to Household Role," FRB Atlanta Working Paper 2021-9, Federal Reserve Bank of Atlanta.
  20. Grow, André & Perrotta, Daniela & Del Fava, Emanuele & Cimentada, Jorge & Rampazzo, Francesco & Gil-Clavel, Sofia & Zagheni, Emilio, 2020. "Addressing Public Health Emergencies via Facebook Surveys: Advantages, Challenges, and Practical Considerations," SocArXiv ez9pb, Center for Open Science.
  21. Buil-Gil, David & Solymosi, Reka & Moretti, Angelo, 2019. "Non-parametric bootstrap and small area estimation to mitigate bias in crowdsourced data. Simulation study and application to perceived safety," SocArXiv 8hgjt, Center for Open Science.
  22. Mark Huberty, 2015. "Awaiting the Second Big Data Revolution: From Digital Noise to Value Creation," Journal of Industry, Competition and Trade, Springer, vol. 15(1), pages 35-47, March.
  23. Morgan R Frank & Manuel Cebrian & Galen Pickard & Iyad Rahwan, 2017. "Validating Bayesian truth serum in large-scale online human experiments," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-13, May.
  24. Kubinec, Robert & Milner, Helen, 2021. "Taxes in the Time of Revolution: An Experimental Test of the Rentier State during Algeria's Hirak," SocArXiv hu3vq, Center for Open Science.
  25. Laura C. Dawkins & Daniel B. Williamson & Stewart W. Barr & Sally R. Lampkin, 2020. "‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 251-280, January.
  26. Bruch, Christian & Felderer, Barbara, 2024. "An Approximation of Joint Distributions of Weighting Variables Using a Pseudo Population Approach," OSF Preprints pg2wt, Center for Open Science.
  27. Temporão, Mickael & Dufresne, Yannick & Savoie, Justin & Linden, Clifton van der, 2019. "Crowdsourcing the vote: New horizons in citizen forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 1-10.
  28. José Garcia Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian forecasting of electoral outcomes with new parties' competition," Economics Working Papers 1624, Department of Economics and Business, Universitat Pompeu Fabra.
  29. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
  30. José Miguel Mansilla Domínguez & Isabel Font Jiménez & Angel Belzunegui Eraso & David Peña Otero & David Díaz Pérez & Ana María Recio Vivas, 2020. "Risk Perception of COVID−19 Community Transmission among the Spanish Population," IJERPH, MDPI, vol. 17(23), pages 1-15, December.
  31. Yonatan Ben-Shalom & Ignacio Martinez & Mariel Finucane, "undated". "Risk of Workforce Exit Due to Disability: State Differences in 2003–2016," Mathematica Policy Research Reports 8aed03744a06419dbda68be8c, Mathematica Policy Research.
  32. Huberty, Mark, 2015. "Can we vote with our tweet? On the perennial difficulty of election forecasting with social media," International Journal of Forecasting, Elsevier, vol. 31(3), pages 992-1007.
  33. Rami Zeedan, 2019. "The 2016 US Presidential Elections: What Went Wrong in Pre-Election Polls? Demographics Help to Explain," J, MDPI, vol. 2(1), pages 1-18, March.
  34. Heng Chen & Marie-Hélène Felt & Christopher Henry, 2018. "2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation," Technical Reports 114, Bank of Canada.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.