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Lorenzo Camponovo

Personal Details

First Name:Lorenzo
Middle Name:
Last Name:Camponovo
Suffix:
RePEc Short-ID:pca1318
[This author has chosen not to make the email address public]

Affiliation

School of Economics
University of Surrey

Guildford, United Kingdom
http://www.surrey.ac.uk/school-economics
RePEc:edi:desuruk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Lorenzo Camponovo & Taisuke Otsu, 2017. "Relative error accurate statistic based on nonparametric likelihood," STICERD - Econometrics Paper Series 593, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  2. Lorenzo Camponovo & Yukitoshi Matsushita & Taisuke Otsu, 2017. "Empirical likelihood for high frequency data," STICERD - Econometrics Paper Series 591, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  3. Lorenzo CAMPONOVO & Olivier SCAILLET & Fabio TROJANI, 2016. "Comments on: Nonparametric Tail Risk, Stock Returns and the Macroeconomy," Swiss Finance Institute Research Paper Series 16-41, Swiss Finance Institute.
  4. Huber, Martin & Camponovo, Lorenzo & Bodory, Hugo & Lechner, Michael, 2016. "A wild bootstrap algorithm for propensity score matching estimators," FSES Working Papers 470, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  5. Bodory, Hugo & Huber, Martin & Camponovo, Lorenzo & Lechner, Michael, 2016. "The finite sample performance of inference methods for propensity score matching and weighting estimators," FSES Working Papers 466, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
  6. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
  7. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
  8. Lorenzo Camponovo & Yukitoshi Matsushita & Taisuke Otsu, 2015. "Nonparametric likelihood for volatility under high frequency data," STICERD - Econometrics Paper Series /2015/581, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  9. Lorenzo Camponovo & Taisuke Otsu, 2014. "Robustness of bootstrap in instrumental variable regression," STICERD - Econometrics Paper Series 572, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  10. Francesco Audrino & Lorenzo Camponovo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Papers 1312.1473, arXiv.org.
  11. Lorenzo Camponovo & Taisuke Otsu, 2011. "Breakdown Point Theory for Implied Probability Bootstrap," Cowles Foundation Discussion Papers 1793, Cowles Foundation for Research in Economics, Yale University.
  12. Lorenzo Camponovo & Taisuke Otsu, 2011. "On Bartlett Correctability of Empirical Likelihood in Generalized �Power Divergence Family," Cowles Foundation Discussion Papers 1825, Cowles Foundation for Research in Economics, Yale University.
  13. Lorenzo CAMPONOVO & Olivier SCAILLET & Fabio TROJANI, 2009. "Robust Resampling Methods for Time Series," Swiss Finance Institute Research Paper Series 09-38, Swiss Finance Institute.
  14. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2006. "Robust Subsampling," Swiss Finance Institute Research Paper Series 06-33, Swiss Finance Institute.

Articles

  1. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2017. "Erratum to Comment on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 505-505.
  2. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2017. "Comment on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 377-387.
  3. Lorenzo Camponovo, 2016. "Asymptotic refinements of nonparametric bootstrap for quasi‐likelihood ratio tests for classes of extremum estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-54, February.
  4. Lorenzo Camponovo & Taisuke Otsu, 2015. "Robustness of Bootstrap in Instrumental Variable Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 352-393, March.
  5. Camponovo, Lorenzo, 2015. "Differencing Transformations And Inference In Predictive Regression Models," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1331-1358, December.
  6. L. Camponovo, 2015. "On the validity of the pairs bootstrap for lasso estimators," Biometrika, Biometrika Trust, vol. 102(4), pages 981-987.
  7. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
  8. Camponovo, Lorenzo & Otsu, Taisuke, 2014. "On Bartlett correctability of empirical likelihood in generalized power divergence family," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 38-43.
  9. Camponovo, Lorenzo & Scaillet, Olivier & Trojani, Fabio, 2012. "Robust subsampling," Journal of Econometrics, Elsevier, vol. 167(1), pages 197-210.
  10. Lorenzo Camponovo & Taisuke Otsu, 2012. "Breakdown point theory for implied probability bootstrap," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 32-55, February.

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. Lorenzo CAMPONOVO & Olivier SCAILLET & Fabio TROJANI, 2016. "Comments on: Nonparametric Tail Risk, Stock Returns and the Macroeconomy," Swiss Finance Institute Research Paper Series 16-41, Swiss Finance Institute.

    Cited by:

    1. Philippe Bernard & Najat El Mekkaoui de Freitas & Bertrand Maillet, 2022. "A Financial Fraud Detection Indicator for Investors: An IDeA," Post-Print hal-02312401, HAL.

  2. Huber, Martin & Camponovo, Lorenzo & Bodory, Hugo & Lechner, Michael, 2016. "A wild bootstrap algorithm for propensity score matching estimators," FSES Working Papers 470, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    Cited by:

    1. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute of Labor Economics (IZA).
    2. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org, revised Aug 2024.
    3. Lajos Baráth & Imre Fertő & Štefan Bojnec, 2020. "The Effect of Investment, LFA and Agri‐environmental Subsidies on the Components of Total Factor Productivity: The Case of Slovenian Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 853-876, September.
    4. Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

  3. Bodory, Hugo & Huber, Martin & Camponovo, Lorenzo & Lechner, Michael, 2016. "The finite sample performance of inference methods for propensity score matching and weighting estimators," FSES Working Papers 466, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    Cited by:

    1. Chlond, Bettina & Gavard, Claire & Jeuck, Lisa, 2021. "Supporting residential energy conservation under constrained public budget: Cost-effectiveness and redistribution analysis of public financial schemes in France," ZEW Discussion Papers 21-056, ZEW - Leibniz Centre for European Economic Research.
    2. Mohebalian, Phillip M. & Aguilar, Francisco X., 2018. "Beneath the Canopy: Tropical Forests Enrolled in Conservation Payments Reveal Evidence of Less Degradation," Ecological Economics, Elsevier, vol. 143(C), pages 64-73.
    3. Lutz Bellmann & Marco Caliendo & Stefan Tübbicke, 2018. "The Post‐Reform Effectiveness of the New German Start‐Up Subsidy for the Unemployed," LABOUR, CEIS, vol. 32(3), pages 293-319, September.
    4. Pawlowski, Tim & Steckenleiter, Carina & Wallrafen, Tim & Lechner, Michael, 2019. "Individual labor market effects of local public expenditures on sports," Economics Working Paper Series 1906, University of St. Gallen, School of Economics and Political Science.
    5. Ferman, Bruno, 2017. "Matching Estimators with Few Treated and Many Control Observations," MPRA Paper 78940, University Library of Munich, Germany.
    6. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    7. Arun Advani & Toru Kitagawa & Tymon S{l}oczy'nski, 2018. "Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection," Papers 1809.09527, arXiv.org, revised Apr 2019.
    8. Bettina Chlond & Claire Gavard & Lisa Jeuck, 2023. "How to Support Residential Energy Conservation Cost-Effectively? An analysis of Public Financial Schemes in France," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 85(1), pages 29-63, May.
    9. Lombardi, Stefano & van den Berg, Gerard J. & Vikström, Johan, 2021. "Empirical Monte Carlo Evidence on Estimation of Timing-of-Events Models," IZA Discussion Papers 14015, Institute of Labor Economics (IZA).
    10. Krumer, Alex & Lechner, Michael, 2016. "Midweek Effect on Performance: Evidence from the German Soccer Bundesliga," Economics Working Paper Series 1609, University of St. Gallen, School of Economics and Political Science.
    11. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    12. Cushman, David O. & De Vita, Glauco, 2017. "Exchange rate regimes and FDI in developing countries: A propensity score matching approach," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 143-163.
    13. Arun Advani & Tymon Sloczynski, 2013. "Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies," CeMMAP working papers CWP64/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Donna Feir & Kelly Foley & Maggie E. C. Jones, 2021. "The Distributional Impacts of Active Labor Market Programs for Indigenous Populations," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 216-220, May.
    15. Krumer, Alex & Lechner, Michael, 2016. "First In First Win: Evidence on Unfairness of Round-Robin Tournaments in Mega-Events," Economics Working Paper Series 1611, University of St. Gallen, School of Economics and Political Science.
    16. Lajos Baráth & Imre Fertő & Štefan Bojnec, 2020. "The Effect of Investment, LFA and Agri‐environmental Subsidies on the Components of Total Factor Productivity: The Case of Slovenian Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 853-876, September.
    17. Samuel Dodini, 2023. "Insurance Subsidies, the Affordable Care Act, and Financial Stability," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 97-136, January.
    18. Janka Goldan & Lena Nusser & Michael Gebel, 2022. "School-related Subjective Well-being of Children with and without Special Educational Needs in Inclusive Classrooms," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(4), pages 1313-1337, August.
    19. Seonho Shin, 2022. "Evaluating the Effect of the Matching Grant Program for Refugees: An Observational Study Using Matching, Weighting, and the Mantel-Haenszel Test," Journal of Labor Research, Springer, vol. 43(1), pages 103-133, March.
    20. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    21. Hugo Bodory & Martin Huber & Michael Lechner, 2022. "The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates," Papers 2212.07379, arXiv.org.
    22. Tübbicke, Stefan, 2023. "How sensitive are matching estimates of active labor market policy effects to typically unobserved confounders?," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-26.
    23. Anthony Strittmatter & Michael Lechner, 2019. "Sorting on the Used-Car Market After the Volkswagen Emission Scandal," Papers 1908.09609, arXiv.org.
    24. Goller, Daniel & Krumer, Alex, 2019. "Let’s meet as usual: Do games on non-frequent days differ? Evidence from top European soccer leagues," Economics Working Paper Series 1907, University of St. Gallen, School of Economics and Political Science.
    25. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    26. Huber, Martin & Camponovo, Lorenzo & Bodory, Hugo & Lechner, Michael, 2016. "A wild bootstrap algorithm for propensity score matching estimators," FSES Working Papers 470, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    27. Finn Tarp & Sam Jones & Felix Schilling, 2021. "Doing business while holding public office: Evidence from Mozambique’s firm registry," DERG working paper series 21-08, University of Copenhagen. Department of Economics. Development Economics Research Group (DERG).
    28. Marco Caliendo & Stefan Tübbicke, 2019. "Do Start-Up Subsidies for the Unemployed Affect Participants’ Well-Being? A Rigorous Look at (Un-)Intended Consequences of Labor Market Policies," CEPA Discussion Papers 14, Center for Economic Policy Analysis.
    29. Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2024. "Nonparametric bootstrap for propensity score matching estimators," Statistics & Probability Letters, Elsevier, vol. 208(C).
    30. Goller, Daniel & Krumer, Alex, 2020. "Let's meet as usual: Do games played on non-frequent days differ? Evidence from top European soccer leagues," European Journal of Operational Research, Elsevier, vol. 286(2), pages 740-754.
    31. Krumer, Alex & Lechner, Michael, 2017. "First in first win: Evidence on schedule effects in round-robin tournaments in mega-events," European Economic Review, Elsevier, vol. 100(C), pages 412-427.
    32. Tenglong Li & Jordan Lawson, 2021. "A generalized bootstrap procedure of the standard error and confidence interval estimation for inverse probability of treatment weighting," Papers 2109.00171, arXiv.org.
    33. Philipp Breidenbach & Timo Mitze, 2022. "Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 15-45.
    34. Alex Krumer & Michael Lechner, 2018. "Midweek Effect On Soccer Performance: Evidence From The German Bundesliga," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 193-207, January.

  4. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.

    Cited by:

    1. Davide La Vecchia & Alban Moor & O. Scaillet, 2020. "A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data," Swiss Finance Institute Research Paper Series 20-01, Swiss Finance Institute.
    2. Gabriele Fiorentini & Enrique Sentana, 2019. "New testing approaches for mean-variance predictability," Working Paper series 19-01, Rimini Centre for Economic Analysis.
    3. Hui Chen & Nengjiu Ju & Jianjun Miao, 2014. "Dynamic Asset Allocation with Ambiguous Return Predictability," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(4), pages 799-823, October.
    4. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    5. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    6. Tim Bollerslev & Viktor Todorov & Lai Xu, 2014. "Tail Risk Premia and Return Predictability," CREATES Research Papers 2014-49, Department of Economics and Business Economics, Aarhus University.
    7. K. Victor Chow & Wanjun Jiang & Bingxin Li & Jingrui Li, 2020. "Decomposing the VIX: Implications for the predictability of stock returns," The Financial Review, Eastern Finance Association, vol. 55(4), pages 645-668, November.

  5. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Arnaud Dufays & Jeroen V.K. Rombouts, 2016. "Sparse Change-point HAR Models for Realized Variance," Cahiers de recherche 1607, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    2. Yao, Xingzhi & Izzeldin, Marwan & Li, Zhenxiong, 2019. "A novel cluster HAR-type model for forecasting realized volatility," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1318-1331.
    3. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    4. Niu, Zibo & Ma, Feng & Zhang, Hongwei, 2022. "The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic," Energy Economics, Elsevier, vol. 112(C).

  6. Lorenzo Camponovo & Taisuke Otsu, 2014. "Robustness of bootstrap in instrumental variable regression," STICERD - Econometrics Paper Series 572, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    Cited by:

    1. Rachel Bocquet & Christian Le Bas & Caroline Mothe & Nicolas Poussing, 2017. "CSR, Innovation, and Firm Performance in Sluggish Growth Contexts: A Firm-Level Empirical Analysis," Journal of Business Ethics, Springer, vol. 146(1), pages 241-254, November.

  7. Francesco Audrino & Lorenzo Camponovo, 2013. "Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models," Papers 1312.1473, arXiv.org.

    Cited by:

    1. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
    2. Francesco Audrino & Simon D. Knaus, 2016. "Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
    3. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    4. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
    5. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2021. "Forecasting realised volatility: Does the LASSO approach outperform HAR?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).

  8. Lorenzo Camponovo & Taisuke Otsu, 2011. "Breakdown Point Theory for Implied Probability Bootstrap," Cowles Foundation Discussion Papers 1793, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Camponovo, Lorenzo & Otsu, Taisuke, 2015. "Robustness of bootstrap in instrumental variable regression," LSE Research Online Documents on Economics 60185, London School of Economics and Political Science, LSE Library.
    2. Marc G. Genton & Peter Hall, 2016. "A tilting approach to ranking influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 77-97, January.
    3. Ferrari, Davide & Zheng, Chao, 2016. "Reliable inference for complex models by discriminative composite likelihood estimation," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 68-80.
    4. Cristian Roner & Claudia Di Caterina & Davide Ferrari, 2021. "Exponential Tilting for Zero-inflated Interval Regression with Applications to Cyber Security Survey Data," BEMPS - Bozen Economics & Management Paper Series BEMPS85, Faculty of Economics and Management at the Free University of Bozen.

  9. Lorenzo Camponovo & Taisuke Otsu, 2011. "On Bartlett Correctability of Empirical Likelihood in Generalized �Power Divergence Family," Cowles Foundation Discussion Papers 1825, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2017. "Empirical likelihood ratio in penalty form and the convex hull problem," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 507-529, November.
    2. Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Likelihood inference on semiparametric models with generated regressors," LSE Research Online Documents on Economics 102696, London School of Economics and Political Science, LSE Library.
    3. Kun Chen & Ngai Hang Chan & Chun Yip Yau, 2016. "Bartlett Correction of Empirical Likelihood for Non-Gaussian Short-Memory Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 624-649, September.
    4. Nicola Lunardon & Gianfranco Adimari, 2016. "Second-order Accurate Confidence Regions Based on Members of the Generalized Power Divergence Family," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 213-227, March.

  10. Lorenzo CAMPONOVO & Olivier SCAILLET & Fabio TROJANI, 2009. "Robust Resampling Methods for Time Series," Swiss Finance Institute Research Paper Series 09-38, Swiss Finance Institute.

    Cited by:

    1. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
    2. Camponovo, Lorenzo & Otsu, Taisuke, 2015. "Robustness of bootstrap in instrumental variable regression," LSE Research Online Documents on Economics 60185, London School of Economics and Political Science, LSE Library.

  11. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2006. "Robust Subsampling," Swiss Finance Institute Research Paper Series 06-33, Swiss Finance Institute.

    Cited by:

    1. Paulo Parente & Richard J. Smith, 2019. "Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," CeMMAP working papers CWP60/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    3. Davide La Vecchia & Alban Moor & O. Scaillet, 2020. "A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data," Swiss Finance Institute Research Paper Series 20-01, Swiss Finance Institute.
    4. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
    5. Camponovo, Lorenzo & Otsu, Taisuke, 2015. "Robustness of bootstrap in instrumental variable regression," LSE Research Online Documents on Economics 60185, London School of Economics and Political Science, LSE Library.
    6. Ronchetti, Elvezio, 2020. "Accurate and robust inference," Econometrics and Statistics, Elsevier, vol. 14(C), pages 74-88.

Articles

  1. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2017. "Erratum to Comment on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 505-505.

    Cited by:

    1. Philippe Bernard & Najat El Mekkaoui de Freitas & Bertrand Maillet, 2022. "A Financial Fraud Detection Indicator for Investors: An IDeA," Post-Print hal-02312401, HAL.

  2. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2017. "Comment on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 377-387.
    See citations under working paper version above.
  3. Lorenzo Camponovo, 2016. "Asymptotic refinements of nonparametric bootstrap for quasi‐likelihood ratio tests for classes of extremum estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-54, February.

    Cited by:

    1. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).
    2. Paulo Parente & Richard J. Smith, 2024. "Implied probability kernel block bootstrap for time series moment condition models," CeMMAP working papers 08/24, Institute for Fiscal Studies.

  4. Lorenzo Camponovo & Taisuke Otsu, 2015. "Robustness of Bootstrap in Instrumental Variable Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 352-393, March.
    See citations under working paper version above.
  5. Camponovo, Lorenzo, 2015. "Differencing Transformations And Inference In Predictive Regression Models," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1331-1358, December.

    Cited by:

    1. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    2. Pitarakis, Jean-Yves, 2019. "Predictive Regressions," UC3M Working papers. Economics 28554, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

  6. L. Camponovo, 2015. "On the validity of the pairs bootstrap for lasso estimators," Biometrika, Biometrika Trust, vol. 102(4), pages 981-987.

    Cited by:

    1. Giuseppe Luca & Jan R. Magnus & Franco Peracchi, 2023. "Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1637-1664, April.
    2. Carlos Lamarche & Thomas Parker, 2022. "Wild Bootstrap Inference For Penalized Quantile Regression For Longitudinal Data," Working Papers 22003 Classification-C15,, University of Waterloo, Department of Economics.

  7. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.

    Cited by:

    1. Terezinha K. A. Ribeiro & Silvia L. P. Ferrari, 2023. "Robust estimation in beta regression via maximum L $$_q$$ q -likelihood," Statistical Papers, Springer, vol. 64(1), pages 321-353, February.

  8. Camponovo, Lorenzo & Otsu, Taisuke, 2014. "On Bartlett correctability of empirical likelihood in generalized power divergence family," Statistics & Probability Letters, Elsevier, vol. 86(C), pages 38-43.
    See citations under working paper version above.
  9. Camponovo, Lorenzo & Scaillet, Olivier & Trojani, Fabio, 2012. "Robust subsampling," Journal of Econometrics, Elsevier, vol. 167(1), pages 197-210.
    See citations under working paper version above.
  10. Lorenzo Camponovo & Taisuke Otsu, 2012. "Breakdown point theory for implied probability bootstrap," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 32-55, February.
    See citations under working paper version above.

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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 16 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 (12) 2007-10-20 2011-04-30 2011-04-30 2011-10-22 2013-10-25 2015-01-31 2015-02-05 2016-02-12 2016-07-16 2016-12-18 2017-03-05 2017-11-26. Author is listed
  2. NEP-ORE: Operations Research (9) 2013-10-25 2015-02-05 2015-02-05 2015-05-30 2015-11-01 2016-02-12 2016-07-16 2017-03-05 2017-11-26. Author is listed
  3. NEP-ETS: Econometric Time Series (3) 2013-10-25 2015-02-05 2017-03-05
  4. NEP-MST: Market Microstructure (2) 2015-01-31 2017-03-05
  5. NEP-CIS: Confederation of Independent States (1) 2011-10-22
  6. NEP-CSE: Economics of Strategic Management (1) 2016-08-07
  7. NEP-FOR: Forecasting (1) 2015-02-05
  8. NEP-GER: German Papers (1) 2016-07-16
  9. NEP-MAC: Macroeconomics (1) 2013-10-25
  10. NEP-RMG: Risk Management (1) 2016-08-07

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