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David Rothschild

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First Name:David
Middle Name:
Last Name:Rothschild
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RePEc Short-ID:pro1033
http://researchdmr.com/

Affiliation

Microsoft Research

https://www.microsoft.com/en-us/research/group/microeconomics/
New York City

Research output

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Jump to: Articles

Articles

  1. Goel, Sharad & Meredith, Marc & Morse, Michael & Rothschild, David & Shirani-Mehr, Houshmand, 2020. "One Person, One Vote: Estimating the Prevalence of Double Voting in U.S. Presidential Elections," American Political Science Review, Cambridge University Press, vol. 114(2), pages 456-469, May.
  2. Masha Krupenkin & David Rothschild & Shawndra Hill & Elad Yom-Tov, 2019. "President Trump Stress Disorder: Partisanship, Ethnicity, and Expressive Reporting of Mental Distress After the 2016 Election," SAGE Open, , vol. 9(1), pages 21582440198, March.
  3. James Schmitz & David Rothschild, 2019. "Understanding market functionality and trading success," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-28, August.
  4. Gur Huberman & Tobias Konitzer & Masha Krupenkin & David Rothschild & Shawndra Hill, 2018. "Economic Expectations, Voting, and Economic Decisions around Elections," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 597-602, May.
  5. Houshmand Shirani-Mehr & David Rothschild & Sharad Goel & Andrew Gelman, 2018. "Disentangling Bias and Variance in Election Polls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 607-614, April.
  6. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.
  7. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.
  8. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
  9. Rothschild, David & Pennock, David M., 2014. "The extent of price misalignment in prediction markets," Algorithmic Finance, IOS Press, vol. 3(1-2), pages 3-20.
  10. Florian Teschner & David Rothschild, 2012. "Simplifying Market Access: A New Confidence-Based Interface," Journal of Prediction Markets, University of Buckingham Press, vol. 6(3), pages 27-41.
    RePEc:jdm:journl:v:9:y:2014:i:1:p:1-14 is not listed on IDEAS

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Masha Krupenkin & David Rothschild & Shawndra Hill & Elad Yom-Tov, 2019. "President Trump Stress Disorder: Partisanship, Ethnicity, and Expressive Reporting of Mental Distress After the 2016 Election," SAGE Open, , vol. 9(1), pages 21582440198, March.

    Cited by:

    1. Mukhopadhyay, Sankar, 2022. "Elections have (health) consequences: Depression, anxiety, and the 2020 presidential election," Economics & Human Biology, Elsevier, vol. 47(C).
    2. Teresa Perry, 2023. "Did the 2016 election cause changes in substance use? An intersectional approach," Economics and Politics, Wiley Blackwell, vol. 35(3), pages 1020-1069, November.
    3. Niederdeppe, Jeff & Avery, Rosemary J. & Liu, Jiawei & Gollust, Sarah E. & Baum, Laura & Barry, Colleen L. & Welch, Brendan & Tabor, Emmett & Lee, Nathaniel W. & Fowler, Erika Franklin, 2021. "Exposure to televised political campaign advertisements aired in the United States 2015–2016 election cycle and psychological distress," Social Science & Medicine, Elsevier, vol. 277(C).
    4. Morey, Brittany N. & García, San Juanita & Nieri, Tanya & Bruckner, Tim A. & Link, Bruce G., 2021. "Symbolic disempowerment and Donald Trump's 2016 presidential election: Mental health responses among Latinx and white populations," Social Science & Medicine, Elsevier, vol. 289(C).

  2. Gur Huberman & Tobias Konitzer & Masha Krupenkin & David Rothschild & Shawndra Hill, 2018. "Economic Expectations, Voting, and Economic Decisions around Elections," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 597-602, May.

    Cited by:

    1. Alexander Maas & Liang Lu, 2021. "Elections have Consequences: Partisan Politics may be Literally Killing Us," Applied Health Economics and Health Policy, Springer, vol. 19(1), pages 45-56, January.
    2. David Mitchell, 2023. "Covid-19 and the 2020 presidential election," Constitutional Political Economy, Springer, vol. 34(2), pages 188-209, June.
    3. Maarten Meeuwis & Jonathan A. Parker & Antoinette Schoar & Duncan Simester, 2022. "Belief Disagreement and Portfolio Choice," Journal of Finance, American Finance Association, vol. 77(6), pages 3191-3247, December.

  3. Houshmand Shirani-Mehr & David Rothschild & Sharad Goel & Andrew Gelman, 2018. "Disentangling Bias and Variance in Election Polls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 607-614, April.

    Cited by:

    1. Aristotelis Boukouras & Will Jennings & Lunzheng Li & Zacharias Maniadis, 2019. "Can Biased Polls Distort Electoral Results? Evidence From The Lab And The Field," Discussion Papers in Economics 19/06, Division of Economics, School of Business, University of Leicester.
    2. Fetzer, Thiemo & Yotzov, Ivan, 2023. "(How) Do electoral surprises drive business cycles? Evidence from a new dataset," CAGE Online Working Paper Series 672, Competitive Advantage in the Global Economy (CAGE).
    3. 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.
    4. 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.
    5. 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.
    6. Jeff Dominitz & Charles F. Manski, 2024. "Using Total Margin of Error to Account for Non-Sampling Error in Election Polls: The Case of Nonresponse," Papers 2407.19339, arXiv.org, revised Oct 2024.
    7. 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.
    8. Dan Hedlin, 2020. "Is there a 'safe area' where the nonresponse rate has only a modest effect on bias despite non‐ignorable nonresponse?," International Statistical Review, International Statistical Institute, vol. 88(3), pages 642-657, December.

  4. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.

    Cited by:

    1. Florian Teschner & Henner Gimpel, 2018. "Crowd Labor Markets as Platform for Group Decision and Negotiation Research: A Comparison to Laboratory Experiments," Group Decision and Negotiation, Springer, vol. 27(2), pages 197-214, April.

  5. Wang, Wei & Rothschild, David & Goel, Sharad & Gelman, Andrew, 2015. "Forecasting elections with non-representative polls," International Journal of Forecasting, Elsevier, vol. 31(3), pages 980-991.

    Cited by:

    1. 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.
    2. 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.
    3. Kolcava, Dennis, 2020. "Do citizens hold business accountable for greenwashing by demanding more government intervention?," OSF Preprints sj4dk, Center for Open Science.
    4. 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.
    5. Marcin Hitczenko, 2021. "Sample Bias Related to Household Role," FRB Atlanta Working Paper 2021-9, Federal Reserve Bank of Atlanta.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Sebasti'an Morales & Charles Thraves, 2020. "On the Resource Allocation for Political Campaigns," Papers 2012.02856, arXiv.org.
    12. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    13. François t'Serstevens & Roberto Cerina & Giulia Piccillo, 2024. "Mapping the Risk of Spreading Fake-News via Wisdom-of-the-Crowd & MrP," CESifo Working Paper Series 11138, CESifo.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Skinner, Benjamin T. & Doyle, William R., 2024. "Predicting postsecondary attendance by family income in the United States using multilevel regression with poststratification," Economics of Education Review, Elsevier, vol. 99(C).
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    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. 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.
    34. 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.
    35. 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.
    36. Heng Chen & Marie-Hélène Felt & Christopher Henry, 2018. "2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation," Technical Reports 114, Bank of Canada.

  6. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.

    Cited by:

    1. Di, Chen & Dimitrov, Stanko & He, Qi-Ming, 2019. "Incentive compatibility in prediction markets: Costly actions and external incentives," International Journal of Forecasting, Elsevier, vol. 35(1), pages 351-370.
    2. 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.
    3. Chih‐Yu Chin & Cheng‐Lung Wang, 2021. "A new insight into combining forecasts for elections: The role of social media," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 132-143, January.
    4. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    5. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    6. Wiesen, Taylor, 2023. "Aggregate earnings and market expectations in United States presidential election prediction markets," Advances in accounting, Elsevier, vol. 60(C).
    7. 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.
    8. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    9. Oliver Merz & Raphael Flepp & Egon Franck, 2020. "Sonic Thunder vs. Brian the Snail : Are people affected by uninformative racehorse names?," Working Papers 384, University of Zurich, Department of Business Administration (IBW).
    10. Rajiv Sethi & Julie Seager & Emily Cai & Daniel M. Benjamin & Fred Morstatter, 2021. "Models, Markets, and the Forecasting of Elections," Papers 2102.04936, arXiv.org, revised May 2021.
    11. Rajiv Sethi & Jennifer Wortman Vaughan, 2016. "Belief Aggregation with Automated Market Makers," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 155-178, June.
    12. 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.

  7. Rothschild, David & Pennock, David M., 2014. "The extent of price misalignment in prediction markets," Algorithmic Finance, IOS Press, vol. 3(1-2), pages 3-20.

    Cited by:

    1. James Schmitz & David Rothschild, 2019. "Understanding market functionality and trading success," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-28, August.
    2. Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.
    3. Reade, J. James & Vaughan Williams, Leighton, 2019. "Polls to probabilities: Comparing prediction markets and opinion polls," International Journal of Forecasting, Elsevier, vol. 35(1), pages 336-350.

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