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Andrii Babii

Personal Details

First Name:Andrii
Middle Name:
Last Name:Babii
Suffix:
RePEc Short-ID:pba1689
[This author has chosen not to make the email address public]
http://ababii.github.io/
Twitter: @babii_andrii
Terminal Degree:2017 Toulouse School of Economics (TSE) (from RePEc Genealogy)

Affiliation

Department of Economics
University of North Carolina-Chapel-Hill

Chapel Hill, North Carolina (United States)
http://www.unc.edu/depts/econ/
RePEc:edi:deuncus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Andrii Babii & Marine Carrasco & Idriss Tsafack, 2024. "Functional Partial Least-Squares: Optimal Rates and Adaptation," Papers 2402.11134, arXiv.org.
  2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
  3. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
  4. Andrii Babii & Eric Ghysels & Junsu Pan, 2022. "Tensor Principal Component Analysis," Papers 2212.12981, arXiv.org, revised Aug 2023.
  5. Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Discussion Papers LFIN 2021004, Université catholique de Louvain, Louvain Finance (LFIN).
  6. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
  7. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
  8. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
  9. Andrii Babii & Rohit Kumar, 2019. "Isotonic Regression Discontinuity Designs," Papers 1908.05752, arXiv.org, revised Dec 2020.
  10. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
  11. Andrii Babii & Jean-Pierre Florens, 2017. "Is completeness necessary? Estimation in nonidentified linear models," Papers 1709.03473, arXiv.org, revised Nov 2021.
  12. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
  13. Andrii Babii, 2016. "Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models," Papers 1611.03015, arXiv.org, revised Dec 2020.

Articles

  1. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
  2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
  3. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
  4. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
  5. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
  6. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
  7. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
  8. Babii, Andrii & Chen, Xi & Ghysels, Eric, 2019. "Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty," Journal of Econometrics, Elsevier, vol. 212(1), pages 47-77.

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.

Working papers

  1. Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Discussion Papers LFIN 2021004, Université catholique de Louvain, Louvain Finance (LFIN).

    Cited by:

    1. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    2. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    3. Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints LFIN 2022007, Université catholique de Louvain, Louvain Finance (LFIN).
    4. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    5. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
    6. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    7. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    8. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    9. Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023. "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers 34837, University of Essex, Essex Business School.
    10. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    11. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
    12. 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).
    13. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    14. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    15. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    16. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    17. Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024. "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, vol. 40, pages 149-176.
    18. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive 20-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    19. d’Aspremont, Alexandre & Arous, Simon Ben & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2024. "Satellites turn “concrete”: tracking cement with satellite data and neural networks," Working Paper Series 2900, European Central Bank.
    20. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," Working Papers 2022-06, Joint Research Centre, European Commission.
    21. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
    22. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
    23. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    24. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2022. "The boosted HP filter is more general than you might think," Papers 2209.09810, arXiv.org, revised Apr 2024.
    25. Luigi Longo & Massimo Riccaboni & Armando Rungi, 2021. "A Neural Network Ensemble Approach for GDP Forecasting," Working Papers 02/2021, IMT School for Advanced Studies Lucca, revised Mar 2021.
    26. Kajal Lahiri & Cheng Yang, 2021. "Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York," CESifo Working Paper Series 9365, CESifo.
    27. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    28. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    29. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    30. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    31. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
    32. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
    33. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    34. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    35. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
    36. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    37. Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.
    38. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    39. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    40. Anders Bredahl Kock & Rasmus S{o}ndergaard Pedersen & Jesper Riis-Vestergaard S{o}rensen, 2024. "Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions," Papers 2403.06657, arXiv.org.
    41. Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
    42. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting," Papers 2404.02671, arXiv.org, revised Sep 2024.
    43. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    44. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    45. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
    46. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    47. Sander Barendse, 2023. "Expected Shortfall LASSO," Papers 2307.01033, arXiv.org, revised Jan 2024.
    48. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "On LASSO Inference for High Dimensional Predictive Regression," Papers 2409.10030, arXiv.org.

  2. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.

    Cited by:

    1. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    2. Babii, Andrii & Florens, Jean-Pierre, 2020. "Is completeness necessary? Estimation in nonidentified linear models," TSE Working Papers 20-1091, Toulouse School of Economics (TSE).

  3. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.

    Cited by:

    1. Lu, Xuefei & Calabrese, Raffaella, 2023. "The Cohort Shapley value to measure fairness in financing small and medium enterprises in the UK," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    3. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
    4. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.

  4. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.

    Cited by:

    1. Hafner, Christian M. & Wang, Linqi, 2022. "Dynamic portfolio selection with sector-specific regularization," LIDAM Reprints LFIN 2022007, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    3. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    4. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.

  5. Andrii Babii & Rohit Kumar, 2019. "Isotonic Regression Discontinuity Designs," Papers 1908.05752, arXiv.org, revised Dec 2020.

    Cited by:

    1. Andrii Babii & Jean-Pierre Florens, 2020. "Are unobservables separable?," Working Papers hal-02532383, HAL.
    2. Matias D. Cattaneo & Rocio Titiunik & Gonzalo Vazquez-Bare, 2019. "The Regression Discontinuity Design," Papers 1906.04242, arXiv.org, revised Jun 2020.
    3. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.
    4. Koohyun Kwon & Soonwoo Kwon, 2020. "Inference in Regression Discontinuity Designs under Monotonicity," Papers 2011.14216, arXiv.org.

  6. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.

    Cited by:

    1. Yeonwoo Rho & Yun Liu & Hie Joo Ahn, 2020. "Revealing Cluster Structures Based on Mixed Sampling Frequencies," Papers 2004.09770, arXiv.org, revised Feb 2021.
    2. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
    3. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
    4. Christian Brownlees & Gu{dh}mundur Stef'an Gu{dh}mundsson, 2021. "Performance of Empirical Risk Minimization for Linear Regression with Dependent Data," Papers 2104.12127, arXiv.org, revised May 2023.
    5. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    6. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    7. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
    8. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.

  7. Andrii Babii & Jean-Pierre Florens, 2017. "Is completeness necessary? Estimation in nonidentified linear models," Papers 1709.03473, arXiv.org, revised Nov 2021.

    Cited by:

    1. Andrii Babii & Jean-Pierre Florens, 2020. "Are unobservables separable?," Working Papers hal-02532383, HAL.
    2. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    3. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    4. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    5. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    6. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
    7. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    8. Guay, Alain & Pelgrin, Florian, 2023. "Structural VAR models in the Frequency Domain," Journal of Econometrics, Elsevier, vol. 236(1).

  8. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.

    Cited by:

    1. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
    2. Andrii Babii & Rohit Kumar, 2019. "Isotonic Regression Discontinuity Designs," Papers 1908.05752, arXiv.org, revised Dec 2020.

  9. Andrii Babii, 2016. "Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models," Papers 1611.03015, arXiv.org, revised Dec 2020.

    Cited by:

    1. Andrii Babii & Jean-Pierre Florens, 2020. "Are unobservables separable?," Working Papers hal-02532383, HAL.
    2. Enache, Andreea & Florens, Jean-Pierre & Sbai, Erwann, 2023. "A functional estimation approach to the first-price auction models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1564-1588.
    3. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
    4. Kengo Kato & Yuya Sasaki & Takuya Ura, 2018. "Inference based on Kotlarski's Identity," Papers 1808.09375, arXiv.org, revised Sep 2019.

Articles

  1. Babii, Andrii & Ball, Ryan T. & Ghysels, Eric & Striaukas, Jonas, 2023. "Machine learning panel data regressions with heavy-tailed dependent data: Theory and application," Journal of Econometrics, Elsevier, vol. 237(2).
    See citations under working paper version above.
  2. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    See citations under working paper version above.
  3. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    See citations under working paper version above.
  4. Andrii Babii, 2022. "High-Dimensional Mixed-Frequency IV Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1470-1483, October.
    See citations under working paper version above.
  5. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    See citations under working paper version above.
  6. Babii, Andrii & Chen, Xi & Ghysels, Eric, 2019. "Commercial and Residential Mortgage Defaults: Spatial Dependence with Frailty," Journal of Econometrics, Elsevier, vol. 212(1), pages 47-77.

    Cited by:

    1. Enzo D'Innocenzo & André Lucas & Anne Opschoor & Xingmin Zhang, 2024. "Heterogeneity and dynamics in network models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 150-173, January.
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    4. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    5. Telg, Sean & Dubinova, Anna & Lucas, Andre, 2023. "Covid-19, credit risk management modeling, and government support," Journal of Banking & Finance, Elsevier, vol. 147(C).
    6. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.
    7. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    8. Anna Dubinova & Andre Lucas & Sean Telg, 2021. "COVID-19, Credit Risk and Macro Fundamentals," Tinbergen Institute Discussion Papers 21-059/III, Tinbergen Institute.
    9. Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    10. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2021. "Time-varying inter-urban housing price spillovers in China: Causes and consequences," Journal of Asian Economics, Elsevier, vol. 77(C).

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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 15 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-ECM: Econometrics (13) 2017-05-21 2017-05-21 2019-09-02 2020-01-13 2020-04-13 2020-05-04 2020-06-15 2020-08-31 2020-11-02 2023-01-16 2023-08-21 2023-10-02 2024-04-01. Author is listed
  2. NEP-BIG: Big Data (8) 2020-01-13 2020-06-15 2020-08-31 2020-11-02 2021-05-24 2022-02-28 2023-08-21 2023-10-02. Author is listed
  3. NEP-CMP: Computational Economics (6) 2020-06-15 2020-08-31 2021-05-24 2022-02-28 2023-08-21 2023-10-02. Author is listed
  4. NEP-ETS: Econometric Time Series (5) 2020-01-13 2020-04-13 2020-06-15 2023-01-16 2023-10-02. Author is listed
  5. NEP-ORE: Operations Research (3) 2017-05-21 2017-05-21 2020-05-04
  6. NEP-DCM: Discrete Choice Models (2) 2020-11-02 2021-05-24
  7. NEP-MAC: Macroeconomics (2) 2020-06-15 2022-02-28
  8. NEP-FDG: Financial Development and Growth (1) 2022-02-28
  9. NEP-MST: Market Microstructure (1) 2020-04-13

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