IDEAS home Printed from https://ideas.repec.org/e/pdj35.html
   My authors  Follow this author

Antoine Alex Djogbenou

Personal Details

First Name:Antoine
Middle Name:Alex
Last Name:Djogbenou
Suffix:
RePEc Short-ID:pdj35
[This author has chosen not to make the email address public]
https://sites.google.com/site/djogbenouantoine/

Affiliation

Department of Economics
York University

Toronto, Canada
http://econ.laps.yorku.ca/
RePEc:edi:dyorkca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone, 2022. "An econometric panel data model of the COVID-19 pandemic," Post-Print hal-03641783, HAL.
  2. Antoine Djogbenou & Razvan Sufana, 2021. "Tests for Group-Specific Heterogeneity in High-Dimensional Factor Models," Papers 2109.09049, arXiv.org, revised Feb 2022.
  3. Antoine Djogbenou & Christian Gouri'eroux & Joann Jasiak & Maygol Bandehali, 2021. "Composite Likelihood for Stochastic Migration Model with Unobserved Factor," Papers 2109.09043, arXiv.org, revised Nov 2023.
  4. Antoine A. Djogbenou & James G. MacKinnon & Morten Ørregaard Nielsen, 2019. "Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors," CREATES Research Papers 2019-05, Department of Economics and Business Economics, Aarhus University.
  5. Antoine A. Djogbenou, 2018. "Comovements In The Real Activity Of Developed And Emerging Economies: A Test Of Global Versus Specific International Factors," Working Paper 1392, Economics Department, Queen's University.
  6. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
  7. Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2017. "Validity Of Wild Bootstrap Inference With Clustered Errors," Working Paper 1383, Economics Department, Queen's University.
  8. Silvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2016. "Bootstrap prediction intervals for factor models," CIRANO Working Papers 2016s-19, CIRANO.
  9. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.
    repec:ags:quedwp:274718 is not listed on IDEAS
    repec:ags:quedwp:274717 is not listed on IDEAS
    repec:ags:quedwp:274709 is not listed on IDEAS
    repec:ags:quedwp:274725 is not listed on IDEAS

Articles

  1. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.
  2. Djogbenou, Antoine & Sufana, Razvan, 2024. "Tests for group-specific heterogeneity in high-dimensional factor models," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
  3. Djogbenou, Antoine & Inan, Emre & Jasiak, Joann, 2023. "Time-varying coefficient DAR model and stability measures for stablecoin prices: An application to Tether," Journal of International Money and Finance, Elsevier, vol. 139(C).
  4. C Gourieroux & A Djogbenou & J Jasiak, 2022. "Testing for Endogeneity of Covid-19 Patient Assignments [The Value of Life and Health for Public Policy]," Journal of Financial Econometrics, Oxford University Press, vol. 20(5), pages 875-901.
  5. Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone & Maygol Bandehali, 2022. "Transition model for coronavirus management," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 665-704, February.
  6. Antoine Djogbenou & Christian Gouriéroux & Joann Jasiak & Paul Rilstone, 2022. "An Econometric Panel Data Model of the COVID-19 Pandemic," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(1), pages 1-3.
  7. Antoine A. Djogbenou, 2021. "Model selection in factor-augmented regressions with estimated factors," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 470-503, April.
  8. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
  9. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
  10. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
  11. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.

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. Antoine A. Djogbenou & James G. MacKinnon & Morten Ørregaard Nielsen, 2019. "Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors," CREATES Research Papers 2019-05, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. James G. MacKinnon & Matthew D. Webb, 2019. "Randomization Inference For Difference-in-differences With Few Treated Clusters," Working Paper 1355, Economics Department, Queen's University.
    2. Alexander Klein & Guy Tchuente, 2024. "Spatial differencing for sample selection models with ‘site-specific’ unobserved local effects," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 235-257.
    3. Klein, Alexander & Meissner, Christopher M., 2024. "Did Tariffs Make American Manufacturing Great? New Evidence from the Gilded Age," CAGE Online Working Paper Series 729, Competitive Advantage in the Global Economy (CAGE).
    4. Timothy G. Conley & Sílvia Gonçalves & Min Seong Kim & Benoit Perron, 2023. "Bootstrap inference under cross‐sectional dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 511-569, May.
    5. Doko Tchatoka, Firmin & Wang, Wenjie, 2021. "Size-corrected Bootstrap Test after Pretesting for Exogeneity with Heteroskedastic or Clustered Data," MPRA Paper 110899, University Library of Munich, Germany.
    6. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Cluster-Robust Jackknife and Bootstrap Inference for Binary Response Models," Working Paper 1515, Economics Department, Queen's University.
    7. James G. MacKinnon, 2021. "Fast cluster bootstrap methods for linear regression models," Working Paper 1465, Economics Department, Queen's University.
    8. Bruce E. Hansen & Seojeong Lee, 2019. "Asymptotic Theory for Clustered Samples," Papers 1902.01497, arXiv.org.
    9. Markus Leibrecht & Christian Bellak, 2023. "Investment policy reform as a driver of foreign direct investment: Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(4), pages 1035-1053, October.
    10. Bharati, Tushar & Chang, Simon & Li, Qing, 2023. "Does tertiary education expansion affect the fertility of women past the college-entry age?," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 1029-1055.
    11. Binz, Oliver & Mayew, William J. & Nallareddy, Suresh, 2022. "Firms’ response to macroeconomic estimation errors," Journal of Accounting and Economics, Elsevier, vol. 73(2).
    12. Bellak, Christian & Leibrecht, Markus & Chaisse, Julien, 2022. "Reforming International Investment Agreements," Department of Economics Working Paper Series 328, WU Vienna University of Economics and Business.
    13. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2023. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust," Stata Journal, StataCorp LP, vol. 23(4), pages 942-982, December.
    14. Dario Tortarolo & Guillermo Cruces & Gonzalo Vazquez-Bare, 2023. "Design of partial population experiments with an application to spillovers in tax compliance," IFS Working Papers W23/17, Institute for Fiscal Studies.
    15. Gerling, Lena & Kellermann, Kim Leonie, 2019. "The impact of election information shocks on populist party preferences: Evidence from Germany," CIW Discussion Papers 3/2019, University of Münster, Center for Interdisciplinary Economics (CIW).
    16. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2024. "Jackknife Inference with Two-Way Clustering," Working Paper 1516, Economics Department, Queen's University.
    17. Matthew D. Webb, 2014. "Reworking Wild Bootstrap Based Inference For Clustered Errors," Working Paper 1315, Economics Department, Queen's University.
    18. Yuya Sasaki & Yulong Wang, 2022. "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method," Papers 2210.16991, arXiv.org, revised Dec 2022.
    19. Tom Boot & Gianmaria Niccodemi & Tom Wansbeek, 2023. "Unbiased estimation of the OLS covariance matrix when the errors are clustered," Empirical Economics, Springer, vol. 64(6), pages 2511-2533, June.
    20. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2022. "Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference," Working Paper 1485, Economics Department, Queen's University.
    21. Wang, Wenjie & Zhang, Yichong, 2024. "Wild bootstrap inference for instrumental variables regressions with weak and few clusters," Journal of Econometrics, Elsevier, vol. 241(1).
    22. McKibbin, Rebecca, 2023. "The effect of RCTs on drug demand: Evidence from off-label cancer drugs," Journal of Health Economics, Elsevier, vol. 90(C).
    23. Christian Bellak & Markus Leibrecht & Julien Chaisse, 2022. "Reforming International Investment Agreements: The Case of China and Foreign Direct Investment," Department of Economics Working Papers wuwp328, Vienna University of Economics and Business, Department of Economics.
    24. Sander Lammers & Massimo Giuliodori & Robert Schmitz & Adam Elbourne, 2023. "Bank Funding, SME lending and Risk Taking," CPB Discussion Paper 447, CPB Netherlands Bureau for Economic Policy Analysis.
    25. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2020. "Wild Bootstrap and Asymptotic Inference with Multiway Clustering," CREATES Research Papers 2020-06, Department of Economics and Business Economics, Aarhus University.
    26. Antoine A. Djogbenou & James G. MacKinnon & Morten Ørregaard Nielsen, 2019. "Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors," CREATES Research Papers 2019-05, Department of Economics and Business Economics, Aarhus University.
    27. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2022. "Cluster-Robust Inference: A Guide to Empirical Practice," Papers 2205.03285, arXiv.org.
    28. David Roodman & James G. MacKinnon & Matthew D. Webb & Morten Ø. Nielsen, 2018. "Fast And Wild: Bootstrap Inference In Stata Using Boottest," Working Paper 1406, Economics Department, Queen's University.
    29. Philippe Kabore & Nicholas Rivers, 2020. "Manufacturing Output and Extreme Temperature: Evidence from Canada," Working Papers 2006E, University of Ottawa, Department of Economics.
    30. Rho, Seunghwa & Vogelsang, Timothy J., 2021. "Inference in time series models using smoothed-clustered standard errors," Journal of Econometrics, Elsevier, vol. 224(1), pages 113-133.
    31. Adnan M. S. Fakir & Tushar Bharati, 2021. "Healthy, nudged, and wise: Experimental evidence on the role of cost reminders in healthy decision-making," Economics Discussion / Working Papers 21-13, The University of Western Australia, Department of Economics.
    32. Solórzano Diego & Dixon Huw, 2020. "The Relationship Between Nominal Wage and Price Flexibility: New Evidence," Working Papers 2020-20, Banco de México.
    33. Guillermo Cruces & Dario Tortarolo & Gonzalo Vazquez-Bare, 2022. "Design of two-stage experiments with an application to spillovers in tax compliance," IFS Working Papers W22/32, Institute for Fiscal Studies.
    34. Helfer, Fabienne & Grossmann, Volker & Osikominu, Aderonke, 2023. "How Does Immigration Affect Housing Costs in Switzerland?," CEPR Discussion Papers 17966, C.E.P.R. Discussion Papers.
    35. Luther Yap, 2023. "Asymptotic Theory for Two-Way Clustering," Papers 2301.03805, arXiv.org, revised Jun 2024.
    36. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
    37. Kojevnikov, Denis & Song, Kyungchul, 2023. "Some impossibility results for inference with cluster dependence with large clusters," Journal of Econometrics, Elsevier, vol. 237(2).
    38. Andreas Hagemann, 2020. "Inference with a single treated cluster," Papers 2010.04076, arXiv.org.
    39. Harold D. Chiang & Jiatong Li & Yuya Sasaki, 2021. "Algorithmic subsampling under multiway clustering," Papers 2103.00557, arXiv.org, revised Oct 2022.
    40. James G. MacKinnon, 2022. "Using Large Samples in Econometrics," Working Paper 1482, Economics Department, Queen's University.
    41. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    42. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2023. "Testing for the appropriate level of clustering in linear regression models," Papers 2301.04522, arXiv.org, revised Mar 2023.
    43. Wang, Wenjie, 2021. "Wild Bootstrap for Instrumental Variables Regression with Weak Instruments and Few Clusters," MPRA Paper 106227, University Library of Munich, Germany.
    44. Wenjie Wang & Yichong Zhang, 2024. "Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters," Papers 2408.10686, arXiv.org.
    45. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2018. "The wild bootstrap with a "small" number of "large" clusters," CeMMAP working papers CWP27/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Alexander Klein & Guy Tchuente, 2020. "Spatial Differencing for Sample Selection Models with Unobserved Heterogeneity," Papers 2009.06570, arXiv.org.
    47. Zach Raff & Andrew Meyer, 2022. "CAFOs and Surface Water Quality: Evidence from Wisconsin," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(1), pages 161-189, January.
    48. James G. MacKinnon, 2019. "How cluster-robust inference is changing applied econometrics," Working Paper 1413, Economics Department, Queen's University.
    49. Andreas Hagemann, 2019. "Permutation inference with a finite number of heterogeneous clusters," Papers 1907.01049, arXiv.org, revised Feb 2023.
    50. Arjan Trinks & Erik Hille, 2023. "Carbon costs and industrial firm performance: Evidence from international microdata," CPB Discussion Paper 445, CPB Netherlands Bureau for Economic Policy Analysis.
    51. Leila Agha & Soomi Kim & Danielle Li, 2020. "Insurance Design and Pharmaceutical Innovation," NBER Working Papers 27563, National Bureau of Economic Research, Inc.
    52. Hwang, Jungbin, 2021. "Simple and trustworthy cluster-robust GMM inference," Journal of Econometrics, Elsevier, vol. 222(2), pages 993-1023.
    53. Adnan M. S. Fakir & Tushar Bharati, 2022. "Healthy, nudged, and wise: Experimental evidence on the role of information salience in reducing tobacco intake," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1129-1166, June.
    54. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    55. Jianghao Chu & Tae-Hwy Lee & Aman Ullah & Haifeng Xu, 2020. "Exact Distribution of the F-statistic under Heteroskedasticity of Unknown Form for Improved Inference," Working Papers 202027, University of California at Riverside, Department of Economics.
    56. Mansfield, Jonathan & Slichter, David, 2021. "The Long-Run Effects of Consequential School Accountability," IZA Discussion Papers 14503, Institute of Labor Economics (IZA).
    57. Gradstein, Mark & Klemp, Marc, 2020. "Natural resource access and local economic growth," European Economic Review, Elsevier, vol. 127(C).
    58. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    59. Fabian Grünwald & Tom Stargardt, 2024. "Harmonizing regulatory market approval of products with high safety requirements: Evidence from the European pharmaceutical market," Health Economics, John Wiley & Sons, Ltd., vol. 33(7), pages 1546-1564, July.
    60. Li, Wenbo, 2023. "The effect of China's driving restrictions on air pollution: The role of a policy announcement without a stated expiration," Resource and Energy Economics, Elsevier, vol. 72(C).
    61. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.

  2. Antoine A. Djogbenou, 2018. "Comovements In The Real Activity Of Developed And Emerging Economies: A Test Of Global Versus Specific International Factors," Working Paper 1392, Economics Department, Queen's University.

    Cited by:

    1. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2023. "Expecting the unexpected: Stressed scenarios for economic growth," Working Papers 202314, University of California at Riverside, Department of Economics.
    2. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    3. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    4. Djogbenou, Antoine & Sufana, Razvan, 2024. "Tests for group-specific heterogeneity in high-dimensional factor models," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    5. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    6. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

  3. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.

    Cited by:

    1. Marine Carrasco & Barbara Rossi, 2016. "In-sample inference and forecasting in misspecified factor models," Economics Working Papers 1530, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    4. Djogbenou, Antoine & Sufana, Razvan, 2024. "Tests for group-specific heterogeneity in high-dimensional factor models," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    5. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..

  4. Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2017. "Validity Of Wild Bootstrap Inference With Clustered Errors," Working Paper 1383, Economics Department, Queen's University.

    Cited by:

    1. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    2. James G. MacKinnon & Matthew D. Webb & Morten Ø. Nielsen, 2017. "Bootstrap And Asymptotic Inference With Multiway Clustering," Working Paper 1386, Economics Department, Queen's University.
    3. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    4. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2018. "The wild bootstrap with a "small" number of "large" clusters," CeMMAP working papers CWP27/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Laurent Davezies & Xavier D'Haultfoeuille & Yannick Guyonvarch, 2018. "Asymptotic results under multiway clustering," Papers 1807.07925, arXiv.org, revised Aug 2018.

  5. Silvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2016. "Bootstrap prediction intervals for factor models," CIRANO Working Papers 2016s-19, CIRANO.

    Cited by:

    1. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    2. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2023. "Expecting the unexpected: Stressed scenarios for economic growth," Working Papers 202314, University of California at Riverside, Department of Economics.
    3. Cheng, Tingting & Gao, Jiti & Yan, Yayi, 2019. "Regime switching panel data models with interactive fixed effects," Economics Letters, Elsevier, vol. 177(C), pages 47-51.
    4. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.
    5. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
    6. GONÇALVES, Sílvia & PERRON, Benoit, 2018. "Bootstrapping factor models with cross sectional dependence," Cahiers de recherche 2018-07, Universite de Montreal, Departement de sciences economiques.
    7. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    8. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    9. YAMAMOTO, Yohei & 山本, 庸平, 2018. "Identifying Factor-Augmented Vector Autoregression Models via Changes in Shock Variances," Discussion paper series HIAS-E-72, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    10. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    11. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Antoine Djogbenou & Sílvia Gonçalves & Benoit Perron, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 481-502, May.
    12. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    13. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    14. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    15. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    16. Diego Fresoli & Pilar Poncela & Esther Ruiz, 2024. "Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors," Papers 2407.06883, arXiv.org.
    17. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  6. Antoine Djogbenou & Silvia Gonçalves & Benoit Perron, 2015. "Bootstrap inference in regressions with estimated factors and serial correlation," CIRANO Working Papers 2015s-20, CIRANO.

    Cited by:

    1. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    2. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    3. Stauskas, Ovidijus & De Vos, Ignace, 2024. "Handling Distinct Correlated Effects with CCE," MPRA Paper 120194, University Library of Munich, Germany.
    4. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
    5. GONÇALVES, Sílvia & PERRON, Benoit, 2018. "Bootstrapping factor models with cross sectional dependence," Cahiers de recherche 2018-07, Universite de Montreal, Departement de sciences economiques.
    6. Antoine A. Djogbenou, 2021. "Model selection in factor-augmented regressions with estimated factors," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 470-503, April.
    7. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2017. "Density Forecasts With Midas Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 783-801, June.
    8. Djogbenou, Antoine & Sufana, Razvan, 2024. "Tests for group-specific heterogeneity in high-dimensional factor models," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    9. YAMAMOTO, Yohei & 山本, 庸平, 2018. "Identifying Factor-Augmented Vector Autoregression Models via Changes in Shock Variances," Discussion paper series HIAS-E-72, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    10. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    11. Lixiong Yang, 2020. "State-dependent biases and the quality of China’s preliminary GDP announcements," Empirical Economics, Springer, vol. 59(6), pages 2663-2687, December.
    12. Fosten, Jack, 2017. "Confidence intervals in regressions with estimated factors and idiosyncratic components," Economics Letters, Elsevier, vol. 157(C), pages 71-74.
    13. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).
    14. Hande Karabiyik & Joakim Westerlund, 2021. "Forecasting using cross-section average–augmented time series regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 315-333.
    15. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

Articles

  1. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

    Cited by:

    1. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    2. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.

  2. Djogbenou, Antoine & Inan, Emre & Jasiak, Joann, 2023. "Time-varying coefficient DAR model and stability measures for stablecoin prices: An application to Tether," Journal of International Money and Finance, Elsevier, vol. 139(C).

    Cited by:

    1. Chen, Yan & Zhang, Lei & Bouri, Elie, 2024. "Co-Bubble transmission across clean and dirty Cryptocurrencies: Network and portfolio analysis," Journal of International Money and Finance, Elsevier, vol. 145(C).

  3. Antoine A. Djogbenou, 2021. "Model selection in factor-augmented regressions with estimated factors," Econometric Reviews, Taylor & Francis Journals, vol. 40(5), pages 470-503, April.
    See citations under working paper version above.
  4. Antoine A. Djogbenou, 2020. "Comovements in the real activity of developed and emerging economies: A test of global versus specific international factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 344-370, April.
    See citations under working paper version above.
  5. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    See citations under working paper version above.
  6. Sílvia Gonçalves & Benoit Perron & Antoine Djogbenou, 2017. "Bootstrap Prediction Intervals for Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 53-69, January.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 10 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 (9) 2015-06-20 2016-04-16 2017-06-18 2017-11-05 2017-11-05 2018-03-12 2018-09-10 2021-09-27 2021-09-27. Author is listed
  2. NEP-FOR: Forecasting (4) 2015-06-20 2016-04-16 2017-11-05 2018-09-10. Author is listed
  3. NEP-ISF: Islamic Finance (2) 2021-09-27 2021-09-27
  4. NEP-ORE: Operations Research (2) 2017-06-18 2021-09-27
  5. NEP-ETS: Econometric Time Series (1) 2015-06-20

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Antoine Alex Djogbenou should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.