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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. 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.
    4. Aristotelis Boukouras & Will Jennings & Lunzheng Li & Zacharias Maniadis, 2019. "Can Biased Polls Distort Electoral Results? Evidence from the Lab and the Field," Levine's Working Paper Archive 786969000000001528, David K. Levine.
    5. 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. 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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. 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. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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. 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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. Breunig, Christoph & Huck, Steffen & Schmidt, Tobias & Weizsäcker, Georg, 2019. "The Standard Portfolio Choice Problem in Germany," Rationality and Competition Discussion Paper Series 171, CRC TRR 190 Rationality and Competition.
    2. Corgnet, Brice & Gächter, Simon & González, Roberto Hernán, 2020. "Working Too Much for Too Little: Stochastic Rewards Cause Work Addiction," IZA Discussion Papers 12992, Institute of Labor Economics (IZA).
    3. 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.
    4. Jingni Yang, 2020. "The uniqueness of local proper scoring rules: the logarithmic family," Theory and Decision, Springer, vol. 88(2), pages 315-322, March.
    5. 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;International Academy of Health Preference Research, vol. 11(4), pages 391-402, August.
    6. 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.
    7. Camilleri, Adrian R. & Newell, Ben R., 2019. "Better calibration when predicting from experience (rather than description)," Organizational Behavior and Human Decision Processes, Elsevier, vol. 150(C), pages 62-82.
    8. 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.
    9. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    10. 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.
    11. Steffen Huck & Tobias Schmidt & Georg Weizsäcker, 2015. "The Standard Portfolio Choice Problem in Germany," CESifo Working Paper Series 5441, CESifo.
    12. 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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