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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. Sébastien Bubeck & Rémi Munos & Gilles Stoltz, 2010. "Pure Exploration for Multi-Armed Bandit Problems," Working Papers hal-00257454, HAL.
  7. 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.
  8. 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.
  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:31:y:2006:i:3:p:562-580 is not listed on IDEAS
    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

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. Emilio Colombo & Matteo Pelagatti, 2019. "Statistical Learning and Exchange Rate Forecasting," DISEIS - Quaderni del Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo dis1901, Università Cattolica del Sacro Cuore, Dipartimento di Economia internazionale, delle istituzioni e dello sviluppo (DISEIS).
    5. Fouliard, Jeremy & Howell, Michael & Rey, Hélène & Stavrakeva, Vania, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, C.E.P.R. Discussion Papers.
    6. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
    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. Yin-Wong Cheung & Wenhao Wang, 2020. "Uncovered Interest Rate Parity Redux: Non- Uniform Effects," GRU Working Paper Series GRU_2020_004, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    9. 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).
    10. 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).
    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. 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.
    13. Brégère, Margaux & Huard, Malo, 2022. "Online hierarchical forecasting for power consumption data," International Journal of Forecasting, Elsevier, vol. 38(1), pages 339-351.
    14. 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.
    15. Martin Casta, 2022. "How Credit Improves the Exchange Rate Forecast," Working Papers 2022/7, Czech National Bank.
    16. 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. Gina Christelle Pieters, 2017. "Bitcoin Reveals Exchange Rate Manipulation and Detects Capital Controls," 2017 Papers ppi307, Job Market Papers.
    2. 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.
    3. Ausloos, Marcel & Cerqueti, Roy & Mir, Tariq A., 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 238-256.
    4. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," LSE Research Online Documents on Economics 103943, London School of Economics and Political Science, LSE Library.
    5. Banu Demir Pakel & Beata Smarzynska Javorcik & Beata Smarzynska Javorcik, 2018. "Forensics, Elasticities and Benford's Law," CESifo Working Paper Series 7266, CESifo.
    6. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.
    7. Chen, Yi & Fan, Ziying & Gu, Xiaomin & Zhou, Li-An, 2018. "Arrival of Young Talents: The Send-down Movement and Rural Education in China," GLO Discussion Paper Series 272, Global Labor Organization (GLO).
    8. Holz, Carsten, 2013. "The Quality of China's GDP Statistics," MPRA Paper 51864, University Library of Munich, Germany.
    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. 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.
    11. Aineas Kostas Mallios, 2023. "Manipulation in reported dividends: Empirical evidence from US banks," Economics Bulletin, AccessEcon, vol. 43(1), pages 441-461.
    12. 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).
    13. 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.
    14. Cong, Lin William & Landsman, Wayne & Maydew, Edward & Rabetti, Daniel, 2023. "Tax-loss harvesting with cryptocurrencies," Journal of Accounting and Economics, Elsevier, vol. 76(2).
    15. 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.
    16. McDonald, Bruce D. III & Goodman, Christopher B, 2020. "The Truth about Honesty in the Nonprofit Sector," SocArXiv 48g5c, Center for Open Science.
    17. 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).
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    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. 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.
    24. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    25. Koch, Christoffer & Okamura, Ken, 2020. "Benford’s Law and COVID-19 reporting," Economics Letters, Elsevier, vol. 196(C).
    26. 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.
    27. Demir, Banu & Javorcik, Beata, 2020. "Trade policy changes, tax evasion and Benford's law," Journal of Development Economics, Elsevier, vol. 144(C).
    28. 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.
    29. 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.
    30. 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.
    31. Tariq Ahmad Mir, 2012. "The leading digit distribution of the worldwide Illicit Financial Flows," Papers 1201.3432, arXiv.org, revised Nov 2012.
    32. 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.
    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. 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.
    35. 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.
    36. 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.
    37. 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.

  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. Maximilian Kasy & Anja Sautmann, 2019. "Adaptive Treatment Assignment in Experiments for Policy Choice," CESifo Working Paper Series 7778, CESifo.
    4. Masahiro Kato & Kaito Ariu, 2021. "The Role of Contextual Information in Best Arm Identification," Papers 2106.14077, arXiv.org, revised Feb 2024.
    5. 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.
    6. 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.
    7. Mohammed Shahid Abdulla & L Ramprasath, 2021. "BBECT: Bandit -based Ethical Clinical Trials," Working papers 459, Indian Institute of Management Kozhikode.
    8. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    9. 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.
    10. 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.

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

    Cited by:

    1. Yuqing Zhang & Neil Walton, 2019. "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches," Papers 1907.05381, arXiv.org.
    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. 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.
    4. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    5. 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.
    6. 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.
    7. Nicolas Della Penna & Mark D. Reid, 2011. "Bandit Market Makers," Papers 1112.0076, arXiv.org, revised Aug 2013.

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

    Cited by:

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

  6. 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. Sébastien Bubeck & Rémi Munos & Gilles Stoltz & Csaba Szepesvari, 2011. "X-Armed Bandits," Post-Print hal-00450235, HAL.
    4. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
    5. Daniel Russo, 2020. "Simple Bayesian Algorithms for Best-Arm Identification," Operations Research, INFORMS, vol. 68(6), pages 1625-1647, November.

  7. 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. Alquier Pierre & Li Xiaoyin & Wintenberger Olivier, 2013. "Prediction of time series by statistical learning: general losses and fast rates," Dependence Modeling, De Gruyter, vol. 1(2013), pages 65-93, January.
    2. Fouliard, Jeremy & Howell, Michael & Rey, Hélène & Stavrakeva, Vania, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, C.E.P.R. Discussion Papers.
    3. Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Working Papers halshs-01003914, HAL.
    4. 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.
    5. 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.

  8. 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. Gina Christelle Pieters, 2017. "Bitcoin Reveals Exchange Rate Manipulation and Detects Capital Controls," 2017 Papers ppi307, Job Market Papers.
    2. 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.
    3. Ausloos, Marcel & Cerqueti, Roy & Mir, Tariq A., 2017. "Data science for assessing possible tax income manipulation: The case of Italy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 238-256.
    4. Dang, Canh Thien & Owens, Trudy, 2020. "Does transparency come at the cost of charitable services? Evidence from investigating British charities," LSE Research Online Documents on Economics 103943, London School of Economics and Political Science, LSE Library.
    5. Michalski, Tomasz & Stoltz, Gilles, 2010. "Do countries falsify economic date strategically? Some evidence that they do," HEC Research Papers Series 930, HEC Paris.
    6. Banu Demir Pakel & Beata Smarzynska Javorcik & Beata Smarzynska Javorcik, 2018. "Forensics, Elasticities and Benford's Law," CESifo Working Paper Series 7266, CESifo.
    7. Tomasz Michalski & Gilles Stoltz, 2013. "Do countries falsify economic data strategically? Some evidence that they might," Post-Print halshs-00482106, HAL.
    8. Chen, Yi & Fan, Ziying & Gu, Xiaomin & Zhou, Li-An, 2018. "Arrival of Young Talents: The Send-down Movement and Rural Education in China," GLO Discussion Paper Series 272, Global Labor Organization (GLO).
    9. Holz, Carsten, 2013. "The Quality of China's GDP Statistics," MPRA Paper 51864, University Library of Munich, Germany.
    10. 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.
    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. 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).
    13. 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.
    14. McDonald, Bruce D. III & Goodman, Christopher B, 2020. "The Truth about Honesty in the Nonprofit Sector," SocArXiv 48g5c, Center for Open Science.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Huang, Yasheng & Niu, Zhiyong & Yang, Clair, 2020. "Testing firm-level data quality in China against Benford’s Law," Economics Letters, Elsevier, vol. 192(C).
    21. Koch, Christoffer & Okamura, Ken, 2020. "Benford’s Law and COVID-19 reporting," Economics Letters, Elsevier, vol. 196(C).
    22. 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.
    23. Demir, Banu & Javorcik, Beata, 2020. "Trade policy changes, tax evasion and Benford's law," Journal of Development Economics, Elsevier, vol. 144(C).
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.

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

  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. Fouliard, Jeremy & Howell, Michael & Rey, Hélène & Stavrakeva, Vania, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, C.E.P.R. Discussion Papers.
    2. Sergiu Hart & Yishay Mansour, 2006. "The Communication Complexity of Uncoupled Nash Equilibrium Procedures," Discussion Paper Series dp419, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    3. Germano, Fabrizio & Lugosi, Gabor, 2007. "Global Nash convergence of Foster and Young's regret testing," Games and Economic Behavior, Elsevier, vol. 60(1), pages 135-154, July.
    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. Sergiu Hart & Yishay Mansour, 2013. "How Long To Equilibrium? The Communication Complexity Of Uncoupled Equilibrium Procedures," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 10, pages 215-249, World Scientific Publishing Co. Pte. Ltd..
    6. Yuichi Noguchi, 2009. "Note on universal conditional consistency," International Journal of Game Theory, Springer;Game Theory Society, vol. 38(2), pages 193-207, June.
    7. 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.
    8. 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.

More information

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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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