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ArCo: An artificial counterfactual approach for high-dimensional panel time-series data

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Cited by:

  1. Marçal, Emerson Fernandes & Cunha, Ronan & Merlin, Giovanni Tondin & Simões, Oscar, 2017. "The aftermath of 2008 turmoil on Brazilian economy: Tsunami or “Marolinha”?," Textos para discussão 459, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
  2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
  3. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
  4. Ricardo D. Brito & Robison F. Kudamatsu & Vladimir K. Teles, 2021. "Inflation Targeting Mattered: a multivariate synthetic control approach," Working Papers, Department of Economics 2021_26, University of São Paulo (FEA-USP).
  5. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
  6. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
  7. Dasgupta, Kabir & Mason, Brenden J., 2020. "The effect of interest rate caps on bankruptcy: Synthetic control evidence from recent payday lending bans," Journal of Banking & Finance, Elsevier, vol. 119(C).
  8. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
  9. Bruno Ferman, 2021. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1764-1772, October.
  10. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
  11. Santamaria, J., 2022. "‘When a Stranger Shall Sojourn with Thee': The Impact of the Venezuelan Exodus on Colombian Labor Markets," Documentos de trabajo - Alianza EFI 20046, Alianza EFI.
  12. Carlos Viana de Carvalho & Ricardo Masini & Marcelo Cunha Medeiros, 2016. "The perils of Counterfactual Analysis with Integrated Processes," Textos para discussão 654, Department of Economics PUC-Rio (Brazil).
  13. repec:ags:aaea22:335710 is not listed on IDEAS
  14. Chamon, Marcos & Garcia, Márcio & Souza, Laura, 2017. "FX interventions in Brazil: A synthetic control approach," Journal of International Economics, Elsevier, vol. 108(C), pages 157-168.
  15. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
  16. Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
  17. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
  18. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2022. "Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 574-590, April.
  19. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
  20. Klinenberg, Danny, 2024. "Selling Violent Extremism," Institute on Global Conflict and Cooperation, Working Paper Series qt2rj4t2rh, Institute on Global Conflict and Cooperation, University of California.
  21. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  22. Ferrara, Gerardo & Mueller, Philippe & Viswanath-Natraj, Ganesh & Wang, Junxuan, 2022. "Central bank swap lines: micro-level evidence," Bank of England working papers 977, Bank of England.
  23. Giambattista Salinari & Federico Benassi & Gianni Carboni, 2023. "The Effect of the Great Recession on Italian Life Expectancy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(1), pages 1-15, February.
  24. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
  25. Jason Poulos & Andrea Albanese & Andrea Mercatanti & Fan Li, 2021. "Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment," Papers 2106.00788, arXiv.org.
  26. Kim, Hyejin & Lee, Jungmin, 2019. "Can employment subsidies save jobs? Evidence from a shipbuilding city in South Korea," Labour Economics, Elsevier, vol. 61(C).
  27. Helder Ferreira de Mendonça & Iven Silva Valpassos, 2022. "Combination of economic policies: how the perfect storm wrecked the Brazilian economic growth," Empirical Economics, Springer, vol. 63(3), pages 1135-1157, September.
  28. Silvia Goncalves & Serena Ng, 2024. "Imputation of Counterfactual Outcomes when the Errors are Predictable," Papers 2403.08130, arXiv.org, revised May 2024.
  29. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  30. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
  31. Ankitha Nandipura Prasanna & Priscila Grecov & Angela Dieyu Weng & Christoph Bergmeir, 2022. "Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand," Papers 2209.08885, arXiv.org, revised Oct 2022.
  32. Shi, Zhentao & Huang, Jingyi, 2023. "Forward-selected panel data approach for program evaluation," Journal of Econometrics, Elsevier, vol. 234(2), pages 512-535.
  33. Krzysztof Drachal & Daniel González Cortés, 2022. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data," IJERPH, MDPI, vol. 20(1), pages 1-24, December.
  34. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.
  35. Carlos B. Carneiro & I'uri H. Ferreira & Marcelo C. Medeiros & Henrique F. Pires & Eduardo Zilberman, 2020. "Lockdown effects in US states: an artificial counterfactual approach," Papers 2009.13484, arXiv.org, revised Feb 2021.
  36. Ricardo Masini, 2022. "Distributional Counterfactual Analysis in High-Dimensional Setup," Papers 2202.11671, arXiv.org, revised Sep 2023.
  37. Luya Wang & Jeffrey S. Racine & Qiaoyu Wang, 2024. "Bootstrap Inference on a Factor Model Based Average Treatment Effects Estimator," Department of Economics Working Papers 2024-03, McMaster University.
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