IDEAS home Printed from https://ideas.repec.org/f/pst788.html

Gilles Stoltz

Personal Details

First Name:Gilles
Middle Name:
Last Name:Stoltz
Suffix:
RePEc Short-ID:pst788
[This author has chosen not to make the email address public]
http://stoltz.perso.math.cnrs.fr

Affiliation

HEC Paris (École des Hautes Études Commerciales)

Jouy-en-Josas, France
http://www.hec.fr/
RePEc:edi:hecpafr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
  2. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.
  3. Gilles Stoltz & Sébastien Bubeck & Rémi Munos, 2011. "Pure exploration in finitely-armed and continuous-armed bandits," Post-Print hal-00609550, HAL.
  4. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2011. "X-Armed Bandits," Post-Print hal-00450235, HAL.
  5. Gilles Stoltz & Shie Mannor, 2010. "A Geometric Proof of Calibration," Post-Print hal-00586044, HAL.
  6. Gilles Stoltz, 2010. "Agrégation séquentielle de prédicteurs : méthodologie générale et applications à la prévision de la qualité de l'air et à celle de la consommation électrique," Post-Print hal-00637060, HAL.
  7. Tomasz Michalski & Gilles Stoltz, 2010. "Do Countries falsify Economic Data Strategically? Some Evidence That They Do," DEGIT Conference Papers c015_018, DEGIT, Dynamics, Economic Growth, and International Trade.
  8. Sébastien Bubeck & Rémi Munos & Gilles Stoltz, 2010. "Pure Exploration for Multi-Armed Bandit Problems," Working Papers hal-00257454, HAL.
  9. Gabor Lugosi & Omiros Papaspiliopoulos & Gilles Stoltz, 2009. "Online Multi-task Learning with Hard Constraints," Working Papers hal-00362643, HAL.
  10. Gilles Stoltz & Vincent Rivoirard, 2009. "Statistique en action," Post-Print hal-00494905, HAL.
  11. Gabor Lugosi & Shie Mannor & Gilles Stoltz, 2008. "Strategies for prediction under imperfect monitoring," Post-Print hal-00124679, HAL.
  12. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2008. "Online Optimization in X-Armed Bandits," Post-Print inria-00329797, HAL.

Articles

  1. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
  2. Tomasz Michalski & Gilles Stoltz, 2013. "Do Countries Falsify Economic Data Strategically? Some Evidence That They Might," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 591-616, May.
  3. Stoltz, Gilles & Lugosi, Gabor, 2007. "Learning correlated equilibria in games with compact sets of strategies," Games and Economic Behavior, Elsevier, vol. 59(1), pages 187-208, April.
    RePEc:inm:ormoor:v:33:y:2008:i:3:p:513-528 is not listed on IDEAS
    RePEc:inm:ormoor:v:35:y:2010:i:4:p:721-727 is not listed on IDEAS
    RePEc:inm:ormoor:v:31:y:2006:i:3:p:562-580 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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.

    Mentioned in:

    1. When countries manipulate economic data
      by Economic Logician in Economic Logic on 2014-01-09 22:30:00

Working papers

  1. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.

    Cited by:

    1. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    2. Jin Shang & Shigeyuki Hamori, 2023. "Do Large Datasets or Hybrid Integrated Models Outperform Simple Ones in Predicting Commodity Prices and Foreign Exchange Rates?," JRFM, MDPI, vol. 16(6), pages 1-25, June.
    3. Biswas, Rita & Li, Xiao & Piccotti, Louis R., 2023. "Do macroeconomic variables drive exchange rates independently?," Finance Research Letters, Elsevier, vol. 52(C).
    4. Jérémy Fouliard & Michael Howell & Hélène Rey, 2021. "Answering the Queen: Machine learning and financial crises," BIS Working Papers 926, Bank for International Settlements.
    5. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    6. Yoonsik Hong & Diego Klabjan, 2025. "Graph Learning for Foreign Exchange Rate Prediction and Statistical Arbitrage," Papers 2508.14784, arXiv.org.
    7. Feng, Wenjun & Zhang, Zhengjun, 2023. "Currency exchange rate predictability: The new power of Bitcoin prices," Journal of International Money and Finance, Elsevier, vol. 132(C).
    8. Paresh Date & Janeeta Maunthrooa, 2025. "Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 606-622, March.
    9. Brégère, Margaux & Huard, Malo, 2022. "Online hierarchical forecasting for power consumption data," International Journal of Forecasting, Elsevier, vol. 38(1), pages 339-351.
    10. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    11. AsadUllah, Muhammad & Mujahid, Hira & I. Tabash, Mosab & Ayubi, Sharique & Sabri, Rabia, 2020. "Forecasting indian rupee/us dollar: arima, exponential smoothing, naïve, nardl, combination techniques," MPRA Paper 111150, University Library of Munich, Germany.
    12. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    13. Cheung, Yin-Wong & Wang, Wenhao, 2022. "Uncovered interest rate parity redux: Non-uniform effects," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 133-151.
    14. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
    15. Li, Haiqi & Zhang, Ni & Zhou, Jin, 2025. "A new self-normalized forecast comparison test," Economics Letters, Elsevier, vol. 256(C).
    16. Malo Huard & Rémy Garnier & Gilles Stoltz, 2020. "Hierarchical robust aggregation of sales forecasts at aggregated levels in e-commerce, based on exponential smoothing and Holt's linear trend method," Working Papers hal-02794320, HAL.
    17. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    18. Eric Adjakossa & Yannig Goude & Olivier Wintenberger, 2024. "Kalman recursions Aggregated Online," Statistical Papers, Springer, vol. 65(2), pages 909-944, April.
    19. Martin Casta, 2022. "How Credit Improves the Exchange Rate Forecast," Working Papers 2022/7, Czech National Bank, Research and Statistics Department.
    20. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    21. Lin, Chien-Hsiu & Liu, Tao & Vincent, Kendro, 2025. "Should economic theories guide the machine learning model in forecasting exchange rate?," Economic Modelling, Elsevier, vol. 151(C).
    22. Simiso MSOMI & Harold NGALAWA, 2023. "The Movement of Exchange Rate and Expected Income: Case of South Africa," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(2), pages 65-89.

  2. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.

    Cited by:

    1. Hong Lee & Joseph R. Mason, 2025. "Appraisal quality and loan characteristics: evidence from Newcomb–Benford Law," Empirical Economics, Springer, vol. 69(6), pages 3721-3758, December.
    2. Kathleen J. Brown, 2025. "Why hide? Africa’s unreported debt to China," The Review of International Organizations, Springer, vol. 20(1), pages 1-32, March.
    3. Gina Christelle Pieters, 2017. "Bitcoin Reveals Exchange Rate Manipulation and Detects Capital Controls," 2017 Papers ppi307, Job Market Papers.
    4. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    5. Demir, Banu & Javorcik, Beata, 2020. "Trade policy changes, tax evasion and Benford's law," Journal of Development Economics, Elsevier, vol. 144(C).
    6. T. Mir, 2016. "The leading digit distribution of the worldwide illicit financial flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 271-281, January.
    7. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    8. Ioana Sorina Deleanu, 2017. "Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford’s Law," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    9. Zhang, Ping & Shi, XunPeng & Sun, YongPing & Cui, Jingbo & Shao, Shuai, 2019. "Have China's provinces achieved their targets of energy intensity reduction? Reassessment based on nighttime lighting data," Energy Policy, Elsevier, vol. 128(C), pages 276-283.
    10. Javorcik, Beata & Demir, Banu, 2018. "Forensics, Elasticities and Benford's Law," CEPR Discussion Papers 12798, Centre for Economic Policy Research.
    11. Xinfei Li & Chang Xu & Baodong Cheng & Jingyang Duan & Yueming Li, 2021. "Does Environmental Regulation Improve the Green Total Factor Productivity of Chinese Cities? A Threshold Effect Analysis Based on the Economic Development Level," IJERPH, MDPI, vol. 18(9), pages 1-21, April.
    12. Marcel Ausloos & Roy Cerqueti & Tariq A. Mir, 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Papers 1709.02129, arXiv.org.
    13. Riccioni, Jessica & Cerqueti, Roy, 2018. "Regular paths in financial markets: Investigating the Benford's law," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 186-194.
    14. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.
    15. Dan Amiram & Zahn Bozanic & Ethan Rouen, 2015. "Financial statement errors: evidence from the distributional properties of financial statement numbers," Review of Accounting Studies, Springer, vol. 20(4), pages 1540-1593, December.
    16. Cano-Rodríguez, Manuel, 2025. "Deviations from Benford’s law in asset valuations: Market prices vs. expert estimates," Finance Research Letters, Elsevier, vol. 86(PB).
    17. Aineas Kostas Mallios, 2023. "Manipulation in reported dividends: Empirical evidence from US banks," Economics Bulletin, AccessEcon, vol. 43(1), pages 441-461.
    18. Biswas, Amit K. & von Hagen, Jürgen & Sarkar, Sandip, 2022. "FDI Mismatch, trade Mis-reporting, and hidden capital Movements: The USA - China case," Journal of International Money and Finance, Elsevier, vol. 120(C).
    19. Theoharry Grammatikos & Nikolaos I. Papanikolaou, 2021. "Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 115-142, April.
    20. MM. Andranik Muradyan, 2020. "Procedure for Assessing the Investment Attractivenessof Foreign Markets.Comparative Analysis of Former USSR Countries," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(10), pages 24-48.
    21. Cong, Lin William & Landsman, Wayne & Maydew, Edward & Rabetti, Daniel, 2023. "Tax-loss harvesting with cryptocurrencies," Journal of Accounting and Economics, Elsevier, vol. 76(2).
    22. Tariq Ahmad Mir & Marcel Ausloos & Roy Cerqueti, 2014. "Benford's law predicted digit distribution of aggregated income taxes: the surprising conformity of Italian cities and regions," Papers 1410.2890, arXiv.org.
    23. Tariq Ahmad Mir, 2012. "The leading digit distribution of the worldwide Illicit Financial Flows," Papers 1201.3432, arXiv.org, revised Nov 2012.
    24. Alvarez, Sean P. & Geloso, Vincent & Scheck, Macy, 2024. "Revisiting the relationship between economic freedom and development to account for statistical deception by autocratic regimes," European Journal of Political Economy, Elsevier, vol. 85(C).
    25. Abhiroop Mukherjee & George Panayotov & Janghoon Shon, 2019. "Eye in the sky: private satellites and government macro data," HKUST IEMS Working Paper Series 2019-68, HKUST Institute for Emerging Market Studies, revised Oct 2019.
    26. Thomas Stoerk, 2015. "Statistical corruption in Beijing’s air quality data has likely ended in 2012," GRI Working Papers 194, Grantham Research Institute on Climate Change and the Environment.
    27. Andrew C. Chang & Phillip Li, 2018. "Measurement Error In Macroeconomic Data And Economics Research: Data Revisions, Gross Domestic Product, And Gross Domestic Income," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1846-1869, July.
    28. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    29. Dan Amiram & Evgeny Lyandres & Daniel Rabetti, 2025. "Trading Volume Manipulation and Competition Among Centralized Crypto Exchanges," Management Science, INFORMS, vol. 71(10), pages 8604-8622, October.
    30. Hao, Zhuang & Zhang, Xudong & Wang, Yuze, 2024. "Assessing the accuracy of self-reported health expenditure data: Evidence from two public surveys in China," Social Science & Medicine, Elsevier, vol. 356(C).
    31. McDonald, Bruce D. III & Goodman, Christopher B, 2020. "The Truth about Honesty in the Nonprofit Sector," SocArXiv 48g5c, Center for Open Science.
    32. Koch, Christoffer & Okamura, Ken, 2020. "Benford’s Law and COVID-19 reporting," Economics Letters, Elsevier, vol. 196(C).
    33. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina," NBER Working Papers 22103, National Bureau of Economic Research, Inc.
    34. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.
    35. Mukherjee, Abhiroop & Panayotov, George & Shon, Janghoon, 2021. "Eye in the sky: Private satellites and government macro data," Journal of Financial Economics, Elsevier, vol. 141(1), pages 234-254.
    36. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 314-343.
    37. Liu, Renliang & Sheng, Liugang & Wang, Jian, 2023. "Faking trade for capital control evasion: Evidence from dual exchange rate arbitrage in China," Journal of International Money and Finance, Elsevier, vol. 138(C).
    38. Mir, T.A., 2014. "The Benford law behavior of the religious activity data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 1-9.
    39. Yi Chen & Ziying Fan & Xiaomin Gu & Li-An Zhou, 2020. "Arrival of Young Talent: The Send-Down Movement and Rural Education in China," American Economic Review, American Economic Association, vol. 110(11), pages 3393-3430, November.
    40. Joras Ferwerda & Ioana Sorina Deleanu & Brigitte Unger, 2019. "Strategies to avoid blacklisting: The case of statistics on money laundering," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    41. Ensminger, Jean & Leder-Luis, Jetson, 2025. "Detecting Corruption: Evidence from a World Bank project in Kenya," World Development, Elsevier, vol. 188(C).
    42. Camacho, Maximo & Dal Bianco, Marcos & Martinez-Martin, Jaime, 2015. "Toward a more reliable picture of the economic activity: An application to Argentina," Economics Letters, Elsevier, vol. 132(C), pages 129-132.
    43. Eutsler, Jared & Kathleen Harris, M. & Tyler Williams, L. & Cornejo, Omar E., 2023. "Accounting for partisanship and politicization: Employing Benford's Law to examine misreporting of COVID-19 infection cases and deaths in the United States," Accounting, Organizations and Society, Elsevier, vol. 108(C).
    44. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 59-108.
    45. Holz, Carsten, 2013. "The Quality of China's GDP Statistics," MPRA Paper 51864, University Library of Munich, Germany.
    46. Das, Subhasish & Biswas, Amit K., 2023. "Can authorities curtail falsified trade & investment data that hide capital movements? Evidence from flows between BRICS and the USA," Journal of Policy Modeling, Elsevier, vol. 45(5), pages 957-974.
    47. Barone, Guglielmo & Letta, Marco, 2025. "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, vol. 101(C).

  3. Gilles Stoltz & Sébastien Bubeck & Rémi Munos, 2011. "Pure exploration in finitely-armed and continuous-armed bandits," Post-Print hal-00609550, HAL.

    Cited by:

    1. Marie Billaud Friess & Arthur Macherey & Anthony Nouy & Clémentine Prieur, 2022. "A PAC algorithm in relative precision for bandit problem with costly sampling," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 96(2), pages 161-185, October.
    2. Alessandro Lizzeri & Eran Shmaya & Leeat Yariv, 2024. "Disentangling Exploration from Exploitation," Working Papers 334, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. Eren Ozbay & Vijay Kamble, 2024. "Maximal Objectives in the Multiarmed Bandit with Applications," Management Science, INFORMS, vol. 70(12), pages 8853-8874, December.
    4. Saeid Delshad & Amin Khademi, 2020. "Information theory for ranking and selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 239-253, June.
    5. Maximilian Kasy & Anja Sautmann, 2019. "Adaptive Treatment Assignment in Experiments for Policy Choice," CESifo Working Paper Series 7778, CESifo.
    6. Masahiro Kato & Kaito Ariu, 2021. "The Role of Contextual Information in Best Arm Identification," Papers 2106.14077, arXiv.org, revised Feb 2024.
    7. Mohammed Shahid Abdulla & L Ramprasath, 2021. "BBECT: Bandit -based Ethical Clinical Trials," Working papers 459, Indian Institute of Management Kozhikode.
    8. Chao Qin & Daniel Russo, 2024. "Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification," Papers 2402.10592, arXiv.org, revised Jul 2024.
    9. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    10. Hyeong Soo Chang, 2020. "An asymptotically optimal strategy for constrained multi-armed bandit problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(3), pages 545-557, June.
    11. Masahiro Kato & Masaaki Imaizumi & Takuya Ishihara & Toru Kitagawa, 2023. "Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds," Papers 2302.02988, arXiv.org, revised Jul 2023.
    12. Yifan Feng & René Caldentey & Christopher Thomas Ryan, 2022. "Robust Learning of Consumer Preferences," Operations Research, INFORMS, vol. 70(2), pages 918-962, March.

  4. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2011. "X-Armed Bandits," Post-Print hal-00450235, HAL.

    Cited by:

    1. Saeid Delshad & Amin Khademi, 2020. "Information theory for ranking and selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(4), pages 239-253, June.
    2. Daniel Russo & Benjamin Van Roy, 2018. "Learning to Optimize via Information-Directed Sampling," Operations Research, INFORMS, vol. 66(1), pages 230-252, January.
    3. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    4. Ningyuan Chen & Guillermo Gallego, 2018. "A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint," Papers 1812.09234, arXiv.org, revised Oct 2021.
    5. Nicolas Della Penna & Mark D. Reid, 2011. "Bandit Market Makers," Papers 1112.0076, arXiv.org, revised Aug 2013.
    6. Pooriya Beyhaghi & Ryan Alimo & Thomas Bewley, 2020. "A derivative-free optimization algorithm for the efficient minimization of functions obtained via statistical averaging," Computational Optimization and Applications, Springer, vol. 76(1), pages 1-31, May.
    7. Yuqing Zhang & Neil Walton, 2019. "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches," Papers 1907.05381, arXiv.org.

  5. Gilles Stoltz & Shie Mannor, 2010. "A Geometric Proof of Calibration," Post-Print hal-00586044, HAL.

    Cited by:

    1. Olszewski, Wojciech, 2015. "Calibration and Expert Testing," Handbook of Game Theory with Economic Applications,, Elsevier.
    2. Dean Foster & Rakesh Vohra, 2011. "Calibration: Respice, Adspice, Prospice," Discussion Papers 1537, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. Vianney Perchet, 2015. "Exponential Weight Approachability, Applications to Calibration and Regret Minimization," Dynamic Games and Applications, Springer, vol. 5(1), pages 136-153, March.
    4. Vladimir V'yugin, 2014. "Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem," Papers 1410.5996, arXiv.org, revised Jun 2015.

  6. Gilles Stoltz, 2010. "Agrégation séquentielle de prédicteurs : méthodologie générale et applications à la prévision de la qualité de l'air et à celle de la consommation électrique," Post-Print hal-00637060, HAL.

    Cited by:

    1. Jérémy Fouliard & Michael Howell & Hélène Rey, 2021. "Answering the Queen: Machine learning and financial crises," BIS Working Papers 926, Bank for International Settlements.
    2. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2014. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    3. Vincent Margot & Christophe Geissler & Carmine de Franco & Bruno Monnier, 2021. "ESG Investments: Filtering versus Machine Learning Approaches," Applied Economics and Finance, Redfame publishing, vol. 8(2), pages 1-16, March.
    4. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    5. Michaël Zamo & Liliane Bel & Olivier Mestre, 2021. "Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 202-225, January.

  7. Tomasz Michalski & Gilles Stoltz, 2010. "Do Countries falsify Economic Data Strategically? Some Evidence That They Do," DEGIT Conference Papers c015_018, DEGIT, Dynamics, Economic Growth, and International Trade.

    Cited by:

    1. Hong Lee & Joseph R. Mason, 2025. "Appraisal quality and loan characteristics: evidence from Newcomb–Benford Law," Empirical Economics, Springer, vol. 69(6), pages 3721-3758, December.
    2. Kathleen J. Brown, 2025. "Why hide? Africa’s unreported debt to China," The Review of International Organizations, Springer, vol. 20(1), pages 1-32, March.
    3. Gina Christelle Pieters, 2017. "Bitcoin Reveals Exchange Rate Manipulation and Detects Capital Controls," 2017 Papers ppi307, Job Market Papers.
    4. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    5. Demir, Banu & Javorcik, Beata, 2020. "Trade policy changes, tax evasion and Benford's law," Journal of Development Economics, Elsevier, vol. 144(C).
    6. T. Mir, 2016. "The leading digit distribution of the worldwide illicit financial flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 271-281, January.
    7. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    8. Ioana Sorina Deleanu, 2017. "Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford’s Law," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    9. Zhang, Ping & Shi, XunPeng & Sun, YongPing & Cui, Jingbo & Shao, Shuai, 2019. "Have China's provinces achieved their targets of energy intensity reduction? Reassessment based on nighttime lighting data," Energy Policy, Elsevier, vol. 128(C), pages 276-283.
    10. Javorcik, Beata & Demir, Banu, 2018. "Forensics, Elasticities and Benford's Law," CEPR Discussion Papers 12798, Centre for Economic Policy Research.
    11. Xinfei Li & Chang Xu & Baodong Cheng & Jingyang Duan & Yueming Li, 2021. "Does Environmental Regulation Improve the Green Total Factor Productivity of Chinese Cities? A Threshold Effect Analysis Based on the Economic Development Level," IJERPH, MDPI, vol. 18(9), pages 1-21, April.
    12. Marcel Ausloos & Roy Cerqueti & Tariq A. Mir, 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Papers 1709.02129, arXiv.org.
    13. Riccioni, Jessica & Cerqueti, Roy, 2018. "Regular paths in financial markets: Investigating the Benford's law," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 186-194.
    14. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.
    15. Dan Amiram & Zahn Bozanic & Ethan Rouen, 2015. "Financial statement errors: evidence from the distributional properties of financial statement numbers," Review of Accounting Studies, Springer, vol. 20(4), pages 1540-1593, December.
    16. Cano-Rodríguez, Manuel, 2025. "Deviations from Benford’s law in asset valuations: Market prices vs. expert estimates," Finance Research Letters, Elsevier, vol. 86(PB).
    17. Aineas Kostas Mallios, 2023. "Manipulation in reported dividends: Empirical evidence from US banks," Economics Bulletin, AccessEcon, vol. 43(1), pages 441-461.
    18. Biswas, Amit K. & von Hagen, Jürgen & Sarkar, Sandip, 2022. "FDI Mismatch, trade Mis-reporting, and hidden capital Movements: The USA - China case," Journal of International Money and Finance, Elsevier, vol. 120(C).
    19. MM. Andranik Muradyan, 2020. "Procedure for Assessing the Investment Attractivenessof Foreign Markets.Comparative Analysis of Former USSR Countries," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(10), pages 24-48.
    20. Tariq Ahmad Mir & Marcel Ausloos & Roy Cerqueti, 2014. "Benford's law predicted digit distribution of aggregated income taxes: the surprising conformity of Italian cities and regions," Papers 1410.2890, arXiv.org.
    21. Michalski, Tomasz & Stoltz, Gilles, 2010. "Do countries falsify economic date strategically? Some evidence that they do," HEC Research Papers Series 930, HEC Paris.
    22. Alvarez, Sean P. & Geloso, Vincent & Scheck, Macy, 2024. "Revisiting the relationship between economic freedom and development to account for statistical deception by autocratic regimes," European Journal of Political Economy, Elsevier, vol. 85(C).
    23. Abhiroop Mukherjee & George Panayotov & Janghoon Shon, 2019. "Eye in the sky: private satellites and government macro data," HKUST IEMS Working Paper Series 2019-68, HKUST Institute for Emerging Market Studies, revised Oct 2019.
    24. Thomas Stoerk, 2015. "Statistical corruption in Beijing’s air quality data has likely ended in 2012," GRI Working Papers 194, Grantham Research Institute on Climate Change and the Environment.
    25. Andrew C. Chang & Phillip Li, 2018. "Measurement Error In Macroeconomic Data And Economics Research: Data Revisions, Gross Domestic Product, And Gross Domestic Income," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1846-1869, July.
    26. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    27. Dan Amiram & Evgeny Lyandres & Daniel Rabetti, 2025. "Trading Volume Manipulation and Competition Among Centralized Crypto Exchanges," Management Science, INFORMS, vol. 71(10), pages 8604-8622, October.
    28. Hao, Zhuang & Zhang, Xudong & Wang, Yuze, 2024. "Assessing the accuracy of self-reported health expenditure data: Evidence from two public surveys in China," Social Science & Medicine, Elsevier, vol. 356(C).
    29. McDonald, Bruce D. III & Goodman, Christopher B, 2020. "The Truth about Honesty in the Nonprofit Sector," SocArXiv 48g5c, Center for Open Science.
    30. Koch, Christoffer & Okamura, Ken, 2020. "Benford’s Law and COVID-19 reporting," Economics Letters, Elsevier, vol. 196(C).
    31. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina," NBER Working Papers 22103, National Bureau of Economic Research, Inc.
    32. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.
    33. Mukherjee, Abhiroop & Panayotov, George & Shon, Janghoon, 2021. "Eye in the sky: Private satellites and government macro data," Journal of Financial Economics, Elsevier, vol. 141(1), pages 234-254.
    34. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 314-343.
    35. Liu, Renliang & Sheng, Liugang & Wang, Jian, 2023. "Faking trade for capital control evasion: Evidence from dual exchange rate arbitrage in China," Journal of International Money and Finance, Elsevier, vol. 138(C).
    36. Mir, T.A., 2014. "The Benford law behavior of the religious activity data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 1-9.
    37. Yi Chen & Ziying Fan & Xiaomin Gu & Li-An Zhou, 2020. "Arrival of Young Talent: The Send-Down Movement and Rural Education in China," American Economic Review, American Economic Association, vol. 110(11), pages 3393-3430, November.
    38. Joras Ferwerda & Ioana Sorina Deleanu & Brigitte Unger, 2019. "Strategies to avoid blacklisting: The case of statistics on money laundering," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-13, June.
    39. Ensminger, Jean & Leder-Luis, Jetson, 2025. "Detecting Corruption: Evidence from a World Bank project in Kenya," World Development, Elsevier, vol. 188(C).
    40. Camacho, Maximo & Dal Bianco, Marcos & Martinez-Martin, Jaime, 2015. "Toward a more reliable picture of the economic activity: An application to Argentina," Economics Letters, Elsevier, vol. 132(C), pages 129-132.
    41. Eutsler, Jared & Kathleen Harris, M. & Tyler Williams, L. & Cornejo, Omar E., 2023. "Accounting for partisanship and politicization: Employing Benford's Law to examine misreporting of COVID-19 infection cases and deaths in the United States," Accounting, Organizations and Society, Elsevier, vol. 108(C).
    42. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 59-108.
    43. Holz, Carsten, 2013. "The Quality of China's GDP Statistics," MPRA Paper 51864, University Library of Munich, Germany.
    44. Das, Subhasish & Biswas, Amit K., 2023. "Can authorities curtail falsified trade & investment data that hide capital movements? Evidence from flows between BRICS and the USA," Journal of Policy Modeling, Elsevier, vol. 45(5), pages 957-974.
    45. Barone, Guglielmo & Letta, Marco, 2025. "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, vol. 101(C).

  8. Sébastien Bubeck & Rémi Munos & Gilles Stoltz, 2010. "Pure Exploration for Multi-Armed Bandit Problems," Working Papers hal-00257454, HAL.

    Cited by:

    1. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2017. "Dynamic Information Acquisition from Multiple Sources," PIER Working Paper Archive 17-023, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Aug 2017.
    2. Caio Waisman & Harikesh S. Nair & Carlos Carrion, 2019. "Online Causal Inference for Advertising in Real-Time Bidding Auctions," Papers 1908.08600, arXiv.org, revised Feb 2024.
    3. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2020. "Treatment recommendation with distributional targets," Papers 2005.09717, arXiv.org, revised Apr 2022.
    4. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.
    5. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2011. "X-Armed Bandits," Post-Print hal-00450235, HAL.

  9. Gilles Stoltz & Vincent Rivoirard, 2009. "Statistique en action," Post-Print hal-00494905, HAL.

    Cited by:

    1. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.

  10. Gabor Lugosi & Shie Mannor & Gilles Stoltz, 2008. "Strategies for prediction under imperfect monitoring," Post-Print hal-00124679, HAL.

    Cited by:

    1. Ehud Lehrer & Eilon Solan, 2016. "A General Internal Regret-Free Strategy," Dynamic Games and Applications, Springer, vol. 6(1), pages 112-138, March.
    2. Ehud Lehrer & Eilon Solan, 2007. "Learning to play partially-specified equilibrium," Levine's Working Paper Archive 122247000000001436, David K. Levine.

  11. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2008. "Online Optimization in X-Armed Bandits," Post-Print inria-00329797, HAL.

    Cited by:

    1. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2011. "X-Armed Bandits," Post-Print hal-00450235, HAL.

Articles

  1. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    See citations under working paper version above.
  2. Tomasz Michalski & Gilles Stoltz, 2013. "Do Countries Falsify Economic Data Strategically? Some Evidence That They Might," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 591-616, May.
    See citations under working paper version above.
  3. Stoltz, Gilles & Lugosi, Gabor, 2007. "Learning correlated equilibria in games with compact sets of strategies," Games and Economic Behavior, Elsevier, vol. 59(1), pages 187-208, April.

    Cited by:

    1. Yuichi Noguchi, 2009. "Note on universal conditional consistency," International Journal of Game Theory, Springer;Game Theory Society, vol. 38(2), pages 193-207, June.
    2. Fabrizio Germano & Gábor Lugosi, 2004. "Global Nash convergence of Foster and Young's regret testing," Economics Working Papers 788, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Martin Bichler & Max Fichtl & Matthias Oberlechner, 2025. "Computing Bayes–Nash Equilibrium Strategies in Auction Games via Simultaneous Online Dual Averaging," Operations Research, INFORMS, vol. 73(2), pages 1102-1127, March.
    4. Fook Wai Kong & Polyxeni-Margarita Kleniati & Berç Rustem, 2012. "Computation of Correlated Equilibrium with Global-Optimal Expected Social Welfare," Journal of Optimization Theory and Applications, Springer, vol. 153(1), pages 237-261, April.
    5. Fouliard, Jeremy & Howell, Michael & Rey, Hélène & Stavrakeva, Vania, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, Centre for Economic Policy Research.
    6. Fook Kong & Berç Rustem, 2013. "Welfare-maximizing correlated equilibria using Kantorovich polynomials with sparsity," Journal of Global Optimization, Springer, vol. 57(1), pages 251-277, September.
    7. Stein, Noah D. & Parrilo, Pablo A. & Ozdaglar, Asuman, 2011. "Correlated equilibria in continuous games: Characterization and computation," Games and Economic Behavior, Elsevier, vol. 71(2), pages 436-455, March.
    8. Hart, Sergiu & Mansour, Yishay, 2010. "How long to equilibrium? The communication complexity of uncoupled equilibrium procedures," Games and Economic Behavior, Elsevier, vol. 69(1), pages 107-126, May.
    9. Sergiu Hart & Yishay Mansour, 2006. "The Communication Complexity of Uncoupled Nash Equilibrium Procedures," Levine's Bibliography 122247000000001299, UCLA Department of Economics.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (1) 2014-06-22
  2. NEP-IFN: International Finance (1) 2013-12-15
  3. NEP-MON: Monetary Economics (1) 2014-06-22

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