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Arco: an artificial counterfactual approach for high-dimensional panel time-series data

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Cited by:

  1. 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.
  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. 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.
  4. 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.
  5. 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).
  6. 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.
  7. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
  8. 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).
  9. 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.
  10. 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.
  11. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
  12. 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," LISER Working Paper Series 2021-07, Luxembourg Institute of Socio-Economic Research (LISER).
  13. 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).
  14. Kim, Hyejin & Lee, Jungmin, 2019. "Can employment subsidies save jobs? Evidence from a shipbuilding city in South Korea," Labour Economics, Elsevier, vol. 61(C).
  15. 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.
  16. 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.
  17. 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.
  18. Silvia Goncalves & Serena Ng, 2024. "Imputation of Counterfactual Outcomes when the Errors are Predictable," Papers 2403.08130, arXiv.org, revised May 2024.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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).
  24. repec:ags:aaea22:335710 is not listed on IDEAS
  25. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
  26. 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.
  27. Shi, Zhentao & Huang, Jingyi, 2023. "Forward-selected panel data approach for program evaluation," Journal of Econometrics, Elsevier, vol. 234(2), pages 512-535.
  28. 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.
  29. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.
  30. 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.
  31. 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).
  32. 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.
  33. 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.
  34. Ricardo Masini, 2022. "Distributional Counterfactual Analysis in High-Dimensional Setup," Papers 2202.11671, arXiv.org, revised Sep 2023.
  35. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
  36. Claudio Cardoso Flores & Marcelo Cunha Medeiros, 2020. "Online Action Learning in High Dimensions: A Conservative Perspective," Papers 2009.13961, arXiv.org, revised Mar 2024.
  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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