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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

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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. James Schmitz & David Rothschild, 2019. "Understanding market functionality and trading success," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-28, August.
  3. 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.
  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. 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.
  10. 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.
  11. 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.

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. 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. Maarten Meeuwis & Jonathan A. Parker & Antoinette Schoar & Duncan I. Simester, 2018. "Belief Disagreement and Portfolio Choice," NBER Working Papers 25108, National Bureau of Economic Research, Inc.
    2. 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. 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. 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.
    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 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. 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.
    7. Andrew Gelman & Jessica Hullman & Christopher Wlezien & George Elliott Morris, 2020. "Information, incentives, and goals in election forecasts," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 863-880, September.

  3. 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.

  4. 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. 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. Marcin Hitczenko, 2021. "Sample Bias Related to Household Role," FRB Atlanta Working Paper 2021-9, Federal Reserve Bank of Atlanta.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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, Open Access Journal, vol. 11(22), pages 1-18, November.
    11. 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.
    12. 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.
    13. 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.
    14. Sebasti'an Morales & Charles Thraves, 2020. "On the Resource Allocation for Political Campaigns," Papers 2012.02856, arXiv.org.
    15. 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.
    16. Cerina, Roberto & Duch, Raymond, 2020. "Measuring public opinion via digital footprints," International Journal of Forecasting, Elsevier, vol. 36(3), pages 987-1002.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Heng Chen & Marie-Hélène Felt & Christopher Henry, 2018. "2017 Methods-of-Payment Survey: Sample Calibration and Variance Estimation," Technical Reports 114, Bank of Canada.

  5. 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. Munzert, Simon, 2017. "Forecasting elections at the constituency level: A correction–combination procedure," International Journal of Forecasting, Elsevier, vol. 33(2), pages 467-481.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. Urmee Khan & Robert Lieli, 2016. "Information Flow Between Prediction Markets, Polls and Media: Evidence from the 2008 Presidential Primaries," Working Papers 201610, University of California at Riverside, Department of Economics.
    8. Bunker, Kenneth, 2020. "A two-stage model to forecast elections in new democracies," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1407-1419.
    9. 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.
    10. 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.

  6. 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. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Jingni Yang, 2020. "The uniqueness of local proper scoring rules: the logarithmic family," Theory and Decision, Springer, vol. 88(2), pages 315-322, March.
    10. Yael Grushka-Cockayne & Victor Richmond R. Jose & Kenneth C. Lichtendahl Jr., 2017. "Ensembles of Overfit and Overconfident Forecasts," Management Science, INFORMS, vol. 63(4), pages 1110-1130, April.
    11. 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.
    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.

  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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