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David Rothschild

Personal Details

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

Affiliation

Microsoft Research

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

Research output

as
Jump to: Articles

Articles

  1. 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.
  2. 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.
  3. Rothschild, David, 2015. "Combining forecasts for elections: Accurate, relevant, and timely," International Journal of Forecasting, Elsevier, vol. 31(3), pages 952-964.
  4. Daniel G. Goldstein & David Rothschild, 2014. "Lay understanding of probability distributions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 1-14, January.
  5. 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.
  6. 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.
  7. Florian Teschner & David Rothschild & Henner Gimpel, 0. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 0, pages 1-19.

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. 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. Andreas Graefe, 2018. "Predicting elections: Experts, polls, and fundamentals," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(4), pages 334-344, July.
    2. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona Graduate School of Economics.
    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.

  2. 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. 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.
    2. 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.
    3. José García-Montalvo & Omiros Papaspiliopoulos & Timothée Stumpf-Fétizon, 2018. "Bayesian Forecasting of Electoral Outcomes with new Parties' Competition," Working Papers 1065, Barcelona Graduate School of Economics.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Heng Chen & Marie-Hélène Felt & Christopher Henry, 2018. "2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation," Technical Reports 114, Bank of Canada.

  3. 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. 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.
    2. 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.
    3. 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.
    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. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.

  4. Daniel G. Goldstein & David Rothschild, 2014. "Lay understanding of probability distributions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 1-14, January.

    Cited by:

    1. Steffen Huck & Tobias Schmidt & Georg Weizsäcker, 2014. "The Standard Portfolio Choice Problem in Germany," SOEPpapers on Multidisciplinary Panel Data Research 650, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. Charles Bellemare & Sabine Kröger & Kouamé Marius Sossou, 2018. "Reporting probabilistic expectations with dynamic uncertainty about possible distributions," Journal of Risk and Uncertainty, Springer, vol. 57(2), pages 153-176, October.
    3. Edward J. D. Webb & David Meads & Ieva Eskyte & Natalie King & Naila Dracup & Jeremy Chataway & Helen L. Ford & Joachim Marti & Sue H. Pavitt & Klaus Schmierer & Ana Manzano, 2018. "A Systematic Review of Discrete-Choice Experiments and Conjoint Analysis Studies in People with Multiple Sclerosis," The Patient: Patient-Centered Outcomes Research, Springer;Johns Hopkins Bloomberg School of Public Health, vol. 11(4), pages 391-402, August.
    4. W.J. Wouter Botzen & Howard Kunreuther & Erwann Michel-Kerjan, 2015. "Divergence between individual perceptions and objective indicators of tail risks: Evidence from floodplain residents in New York City," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(4), pages 365-385, July.
    5. Christian Ehm & Christine Laudenbach & Martin Weber, 2018. "Focusing on volatility information instead of portfolio weights as an aid to investor decisions," Experimental Economics, Springer;Economic Science Association, vol. 21(2), pages 457-480, June.
    6. Cornelia Betsch & Niels Haase & Frank Renkewitz & Philipp Schmid, 2015. "The narrative bias revisited: What drives the biasing influence of narrative information on risk perceptions?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(3), pages 241-264, May.
    7. Lionel Page & Daniel G. Goldstein, 2016. "Subjective beliefs about the income distribution and preferences for redistribution," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 47(1), pages 25-61, June.

  5. 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. 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.
    2. 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.

  6. Florian Teschner & David Rothschild & Henner Gimpel, 0. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 0, pages 1-19.

    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.

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