Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms
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DOI: 10.1016/j.econmod.2024.106819
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- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022.
"Urban economics in a historical perspective: Recovering data with machine learning,"
Regional Science and Urban Economics, Elsevier, vol. 94(C).
- Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
- May Elsayyad, 2012. "Bargaining over Tax Information Exchange," Working Papers bargaining_over_tax_infor, Max Planck Institute for Tax Law and Public Finance.
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- Chisik, Richard & Davies, Ronald B., 2004.
"Asymmetric FDI and tax-treaty bargaining: theory and evidence,"
Journal of Public Economics, Elsevier, vol. 88(6), pages 1119-1148, June.
- Richard Chisik & Ronald B. Davies, 2001. "Asymmetric FDI and Tax-Treaty Bargaining: Theory and Evidence"," University of Oregon Economics Department Working Papers 2001-2, University of Oregon Economics Department, revised 01 Jun 2002.
- Chisik, Richard & Ronald B. Davies, 2002. "Asymmetric FDI and Tax-Treaty Bargaining: Theory and Evidence," Royal Economic Society Annual Conference 2002 48, Royal Economic Society.
- Richard Chisik & Ronald B. Davies, 2010. "Asymmetric FDI and Tax-Treaty Bargaining: Theory and Evidence," Working Papers 020, Toronto Metropolitan University, Department of Economics.
- Ron Davies & Richard Chisik, 2004. "Asymmetric FDI and Tax-Treaty Bargaining: Theory and Evidence," Econometric Society 2004 Latin American Meetings 64, Econometric Society.
- Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022.
"How effective is carbon pricing?—A machine learning approach to policy evaluation,"
Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
- Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2021. "How effective is carbon pricing? A machine learning approach to policy evaluation," ZEW Discussion Papers 21-039, ZEW - Leibniz Centre for European Economic Research.
- Kasy, Maximilian, 2018. "Optimal taxation and insurance using machine learning — Sufficient statistics and beyond," Journal of Public Economics, Elsevier, vol. 167(C), pages 205-219.
- Sunghoon Hong, 2018. "Tax treaties and foreign direct investment: a network approach," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 25(5), pages 1277-1320, October.
- Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
- Julia Braun & Martin Zagler, 2018.
"The true art of the tax deal: Evidence on aid flows and bilateral double tax agreements,"
The World Economy, Wiley Blackwell, vol. 41(6), pages 1478-1507, June.
- Braun, Julia & Zagler, Martin, 2017. "The true art of the tax deal: Evidence on aid flows and bilateral double tax agreements," ZEW Discussion Papers 17-011, ZEW - Leibniz Centre for European Economic Research.
- Julia Braun & Martin Zagler, 2017. "The true art of the tax deal: Evidence on aid flows and bilateral double tax agreements," Department of Economics Working Papers wuwp242, Vienna University of Economics and Business, Department of Economics.
- Braun, Julia & Zagler, Martin, 2017. "The true art of the tax deal: Evidence on aid flows and bilateral double tax agreements," Department of Economics Working Paper Series 242, WU Vienna University of Economics and Business.
- Braun, Julia & Zagler, Martin, 2017. "The true art of the tax deal: Evidence of aid flows and bilateral double tax agreements," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168084, Verein für Socialpolitik / German Economic Association.
- Giovanni Cerulli, 2022.
"Machine learning using Stata/Python,"
Stata Journal, StataCorp LP, vol. 22(4), pages 772-810, December.
- Giovanni Cerulli, 2021. "Machine learning using Stata/Python," 2021 Stata Conference 25, Stata Users Group.
- Giovanni Cerulli, 2022. "Machine learning using Stata/Python," Italian Stata Users' Group Meetings 2022 02, Stata Users Group.
- Giovanni Cerulli, 2021. "Improving econometric prediction by machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 28(16), pages 1419-1425, September.
- Bruce A. Blonigen & Ronald B. Davies, 2004.
"The Effects of Bilateral Tax Treaties on U.S. FDI Activity,"
International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 11(5), pages 601-622, September.
- Bruce A. Blonigen & Ronald B. Davies, 2000. "The Effects of Bilateral Tax Treaties on U.S. FDI Activity," NBER Working Papers 7929, National Bureau of Economic Research, Inc.
- Bruce A. Blonigen & Ronald B. Davies, 2001. "The Effects of Bilateral Tax Treaties on U.S. FDI Activity," University of Oregon Economics Department Working Papers 2001-14, University of Oregon Economics Department, revised 01 Jan 2001.
- Maddalena Conte & Pierre Cotterlaz & Thierry Mayer, 2022. "The CEPII Gravity Database," Working Papers 2022-05, CEPII research center.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
- Dimitri Paolini & Pasquale Pistone & Giuseppe Pulina & Martin Zagler, 2016.
"Tax treaties with developing countries and the allocation of taxing rights,"
European Journal of Law and Economics, Springer, vol. 42(3), pages 383-404, December.
- PAOLINI, Dimitri & PISTONE, Pasquale & pulina, GIUSEPPE & ZAGLER, Martin, 2011. "Tax treaties and the allocation of taxing rights with developing countries," LIDAM Discussion Papers CORE 2011042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Dimitri PAOLINI & Pasquale PISTONE & Giuseppe PULINA & Martin ZAGLER, 2016. "Tax treaties with developing countries and the allocation of taxing rights," LIDAM Reprints CORE 2899, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
- Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024.
"Predicting dropout from higher education: Evidence from Italy,"
Economic Modelling, Elsevier, vol. 130(C).
- Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2020. "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series 22-06, Department of Economics at the University of Luxembourg.
- Isaiah Hull & Anna Grodecka-Messi, 2022. "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers 2203.14751, arXiv.org.
- Thomas Rixen & Peter Schwarz, 2009. "Bargaining over the Avoidance of Double Taxation: Evidence from German Tax Treaties," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 65(4), pages 442-471, December.
- Martin Hearson, 2018. "When Do Developing Countries Negotiate Away Their Corporate Tax Base?," Journal of International Development, John Wiley & Sons, Ltd., vol. 30(2), pages 233-255, March.
- Kunka Petkova & Andrzej Leszek Stasio & Martin Zagler, 2020. "Bilateral Tax Competition and Regional Spillovers in Tax Treaty Formation," JRC Working Papers on Taxation & Structural Reforms 2020-07, Joint Research Centre.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Kunka Petkova, 2021. "Withholding tax rates on dividends: symmetries versus asymmetries or single- versus multi-rated double tax treaties," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(4), pages 890-940, August.
- Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
- Muhammad Yaseen Khan & Abdul Qayoom & Muhammad Suffian Nizami & Muhammad Shoaib Siddiqui & Shaukat Wasi & Syed Muhammad Khaliq-ur-Rahman Raazi & Shahzad Sarfraz, 2021. "Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding-Based Deep Learning Techniques," Complexity, Hindawi, vol. 2021, pages 1-18, September.
- Mr. Olaf Unteroberdoerster & Cynthia Leung, 2008. "Hong Kong SAR as a Financial Center for Asia: Trends and Implications," IMF Working Papers 2008/057, International Monetary Fund.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Mohammad Zoynul Abedin & M. Kabir Hassan & Imran Khan & Ivan F. Julio, 2022. "Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 39(04), pages 1-26, August.
- Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
- repec:idb:brikps:9167 is not listed on IDEAS
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Cited by:
- Caravaggio, Nicola & Resce, Giuliano & Idola Francesca, Spanò, 2024. "Is Local Taxation Predictable? A Machine Learning Approach," Economics & Statistics Discussion Papers esdp24098, University of Molise, Department of Economics.
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More about this item
Keywords
Machine learning; Treaty formation; Double tax treaty;All these keywords.
JEL classification:
- F53 - International Economics - - International Relations, National Security, and International Political Economy - - - International Agreements and Observance; International Organizations
- H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
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