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Francesco Bravo

Personal Details

First Name:Francesco
Middle Name:
Last Name:Bravo
Suffix:
RePEc Short-ID:pbr434
[This author has chosen not to make the email address public]
https://sites.google.com/a/york.ac.uk/francescobravo/
Terminal Degree:2000 (from RePEc Genealogy)

Affiliation

Department of Economics and Related Studies
University of York

York, United Kingdom
http://www.york.ac.uk/economics/
RePEc:edi:deyoruk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Bravo, Francesco & Juan Carlos, Escanciano & Ingrid Van Keilegom, Ingrid, 2020. "Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Reprints ISBA 2020046, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  2. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  3. Bravo, Francesco & Chu, Ba & Jacho-Chavez, David, 2013. "Semiparametric estimation of moment condition models with weakly dependent data," MPRA Paper 79686, University Library of Munich, Germany, revised 2016.
  4. Francesco Bravo & Federico Crudu, 2012. "Efficient bootstrap with weakly dependent processes," Discussion Papers 12/08, Department of Economics, University of York.
  5. Francesco Bravo & Juan Carlos Escanciano & Taisuke Otsu, 2011. "A Simple Test for Identification in GMM under Conditional Moment Restrictions," Cowles Foundation Discussion Papers 1789, Cowles Foundation for Research in Economics, Yale University.
  6. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
  7. Francesco Bravo, "undated". "Empirical likelihood inference with applications to some econometric models," Discussion Papers 00/05, Department of Economics, University of York.
  8. Francesco Bravo, "undated". "On the density of generalised quadratic forms with applications to asymptotic expansions for test statistics," Discussion Papers 00/32, Department of Economics, University of York.
  9. Francesco Bravo, "undated". "Higher order asymptotics and the bootstrap for empirical likelihood J tests," Discussion Papers 00/30, Department of Economics, University of York.
  10. Francesco Bravo, "undated". "Empirical likelihood specification testing in linear regression models," Discussion Papers 00/28, Department of Economics, University of York.
  11. Francesco Bravo, "undated". "Sieve Nonparametric Likelihood Methods for Unit Root Tests," Discussion Papers 05/33, Department of Economics, University of York.
  12. Francesco Bravo, "undated". "Bartlett-type Adjustments for Empirical Discrepancy Test Statistics," Discussion Papers 04/14, Department of Economics, University of York.

Articles

  1. Bravo, Francesco, 2023. "Local polynomial estimation of nonparametric general estimating equations," Statistics & Probability Letters, Elsevier, vol. 197(C).
  2. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
  3. Francesco Bravo, 2022. "Second order expansions of estimators in nonparametric moment conditions models with weakly dependent data," Econometric Reviews, Taylor & Francis Journals, vol. 41(6), pages 583-606, July.
  4. Bravo, Francesco & Li, Degui & Tjøstheim, Dag, 2021. "Robust nonlinear regression estimation in null recurrent time series," Journal of Econometrics, Elsevier, vol. 224(2), pages 416-438.
  5. Francesco Bravo, 2020. "Semiparametric quantile regression with random censoring," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 265-295, February.
  6. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.
  7. Francesco Bravo, 2020. "Two-step combined nonparametric likelihood estimation of misspecified semiparametric models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(3), pages 769-792, July.
  8. Francesco Bravo & Ba M. Chu & David T. Jacho-Chávez, 2017. "Semiparametric estimation of moment condition models with weakly dependent data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 108-136, January.
  9. Bravo, Francesco & Chu, Ba M. & Jacho-Chávez, David T., 2017. "Generalized empirical likelihood M testing for semiparametric models with time series data," Econometrics and Statistics, Elsevier, vol. 4(C), pages 18-30.
  10. Francesco Bravo & David T. Jacho-Chávez, 2016. "Semiparametric quasi-likelihood estimation with missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(5), pages 1345-1369, March.
  11. Francesco Bravo, 2016. "Local Information Theoretic Methods for smooth Coefficients Dynamic Panel Data Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 690-708, September.
  12. Bravo, Francesco, 2015. "Semiparametric estimation with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 329-346.
  13. Francesco Bravo, 2014. "Varying coefficients partially linear models with randomly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 383-412, April.
  14. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
  15. Francesco Bravo & Leslie G. Godfrey, 2012. "Bootstrap HAC Tests for Ordinary Least Squares Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(6), pages 903-922, December.
  16. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
  17. Francesco Bravo, 2012. "Generalized empirical likelihood testing in semiparametric conditional moment restrictions models," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 1-31, February.
  18. Francesco Bravo, 2011. "Comment on: Subsampling weakly dependent time series and application to extremes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 483-486, November.
  19. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
  20. Francesco Bravo, 2011. "Improved generalized method of moments estimators for weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 680-698, November.
  21. Francesco Bravo, 2010. "Nonparametric likelihood inference for general autoregressive models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(1), pages 79-106, March.
  22. Francesco Bravo, 2009. "Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 208-231, July.
  23. Bravo, Francesco, 2009. "Two-step generalised empirical likelihood inference for semiparametric models," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1412-1431, August.
  24. Francesco Bravo, 2005. "Blockwise empirical entropy tests for time series regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 185-210, March.
  25. Bravo, Francesco, 2004. "Empirical Likelihood Based Inference With Applications To Some Econometric Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 231-264, April.
  26. Francesco Bravo, 2003. "Second-order power comparisons for a class of nonparametric likelihood-based tests," Biometrika, Biometrika Trust, vol. 90(4), pages 881-890, December.
  27. Francesco Bravo, 2002. "Testing linear restrictions in linear models with empirical likelihood," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 104-130, June.
  28. Bravo, Francesco, 2002. "Blockwise empirical Cressie-Read test statistics for [alpha]-mixing processes," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 319-325, July.
  29. Bravo, Francesco, 1999. "A Correction Factor For Unit Root Test Statistics," Econometric Theory, Cambridge University Press, vol. 15(2), pages 218-227, April.

Chapters

  1. Francesco Bravo & Juan Carlos Escanciano & Taisuke Otsu, 2012. "A Simple Test for Identification in GMM under Conditional Moment Restrictions," Advances in Econometrics, in: Essays in Honor of Jerry Hausman, pages 455-477, Emerald Group Publishing Limited.
  2. Francesco Bravo & Kim P. Huynh & David T. Jacho-Chávez, 2011. "Average Derivative Estimation with Missing Responses," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 129-154, Emerald Group Publishing Limited.

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. Bravo, Francesco & Juan Carlos, Escanciano & Ingrid Van Keilegom, Ingrid, 2020. "Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Reprints ISBA 2020046, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," Papers 2108.04852, arXiv.org, revised Aug 2024.
    2. Tang, Shengfang & Huang, Zhilin, 2022. "Empirical likelihood confidence interval for difference-in-differences estimator with panel data," Economics Letters, Elsevier, vol. 216(C).
    3. Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    4. Harold D. Chiang & Bing Yang Tan, 2020. "Empirical likelihood and uniform convergence rates for dyadic kernel density estimation," Papers 2010.08838, arXiv.org, revised May 2022.
    5. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    6. Xue, Liugen, 2024. "Empirical likelihood in a partially linear single-index model with censored response data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    7. 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.
    8. Yukitoshi Matsushita & Taisuke Otsu, 2019. "Jackknife, small bandwidth and high-dimensional asymptotics," STICERD - Econometrics Paper Series 605, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. Adusumilli, Karun & Otsu, Taisuke & Qiu, Chen, 2023. "Reweighted nonparametric likelihood inference for linear functionals," LSE Research Online Documents on Economics 120198, London School of Economics and Political Science, LSE Library.
    10. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," STICERD - Econometrics Paper Series 617, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

  2. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Matsushita, Yukitoshi & Otsu, Taisuke, 2018. "Likelihood inference on semiparametric models: average derivative and treatment effect," LSE Research Online Documents on Economics 85870, London School of Economics and Political Science, LSE Library.
    2. Yukitoshi Matsushita & Taisuke Otsu, 2018. "Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect," The Japanese Economic Review, Springer, vol. 69(2), pages 133-155, June.
    3. Yukitoshi Matsushita & Taisuke Otsu, 2016. "Likelihood inference on semiparametric models with generated regressors," STICERD - Econometrics Paper Series 587, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

  3. Bravo, Francesco & Chu, Ba & Jacho-Chavez, David, 2013. "Semiparametric estimation of moment condition models with weakly dependent data," MPRA Paper 79686, University Library of Munich, Germany, revised 2016.

    Cited by:

    1. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
    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. Bravo, Francesco & Chu, Ba M. & Jacho-Chávez, David T., 2017. "Generalized empirical likelihood M testing for semiparametric models with time series data," Econometrics and Statistics, Elsevier, vol. 4(C), pages 18-30.

  4. Francesco Bravo & Federico Crudu, 2012. "Efficient bootstrap with weakly dependent processes," Discussion Papers 12/08, Department of Economics, University of York.

    Cited by:

    1. Paulo M.D.C. Parente & Richard J. Smith, 2018. "Generalised Empirical Likelihood Kernel Block Bootstrapping," Working Papers REM 2018/55, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

  5. Francesco Bravo & Juan Carlos Escanciano & Taisuke Otsu, 2011. "A Simple Test for Identification in GMM under Conditional Moment Restrictions," Cowles Foundation Discussion Papers 1789, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Antoine, Bertille & Renault, Eric, 2020. "Testing identification strength," Journal of Econometrics, Elsevier, vol. 218(2), pages 271-293.
    2. Forneron, Jean-Jacques, 2024. "Detecting identification failure in moment condition models," Journal of Econometrics, Elsevier, vol. 238(1).
    3. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.

  6. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.

    Cited by:

    1. Seojeong Lee, 2014. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators," Discussion Papers 2014-02, School of Economics, The University of New South Wales.
    2. 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.
    3. Seojeong Lee, 2015. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Discussion Papers 2015-01, School of Economics, The University of New South Wales.
    4. Seojeong Lee, 2013. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Method of Moments Estimators," Discussion Papers 2013-09, School of Economics, The University of New South Wales.
    5. Seojeong Lee, 2018. "Asymptotic Refinements of a Misspecification-Robust Bootstrap for Generalized Empirical Likelihood Estimators," Papers 1806.00953, arXiv.org, revised Jun 2018.

  7. Francesco Bravo, "undated". "Bartlett-type Adjustments for Empirical Discrepancy Test Statistics," Discussion Papers 04/14, Department of Economics, University of York.

    Cited by:

    1. Francesco Bravo, "undated". "Empirical likelihood specification testing in linear regression models," Discussion Papers 00/28, Department of Economics, University of York.
    2. Grendar, Marian & Judge, George G., 2006. "Large Deviations Theory and Empirical Estimator Choice," CUDARE Working Papers 25084, University of California, Berkeley, Department of Agricultural and Resource Economics.
    3. Kakizawa, Yoshihide, 2011. "Improved additive adjustments for the LR/ELR test statistics," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1245-1255, August.
    4. Kakizawa, Yoshihide, 2009. "Third-order power comparisons for a class of tests for multivariate linear hypothesis under general distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 473-496, March.
    5. 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.
    6. Kakizawa, Yoshihide, 2010. "Comparison of Bartlett-type adjusted tests in the multiparameter case," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1638-1655, August.

Articles

  1. Bravo, Francesco & Li, Degui & Tjøstheim, Dag, 2021. "Robust nonlinear regression estimation in null recurrent time series," Journal of Econometrics, Elsevier, vol. 224(2), pages 416-438.

    Cited by:

    1. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models," Monash Econometrics and Business Statistics Working Papers 2/23, Monash University, Department of Econometrics and Business Statistics.
    2. Chaohua Dong & Jiti Gao & Bin Peng & Yundong Tu, 2023. "Smoothing the Nonsmoothness," Papers 2309.16348, arXiv.org.

  2. Francesco Bravo, 2020. "Two-step combined nonparametric likelihood estimation of misspecified semiparametric models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(3), pages 769-792, July.

    Cited by:

    1. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.

  3. Francesco Bravo & Ba M. Chu & David T. Jacho-Chávez, 2017. "Semiparametric estimation of moment condition models with weakly dependent data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 108-136, January.
    See citations under working paper version above.
  4. Bravo, Francesco & Chu, Ba M. & Jacho-Chávez, David T., 2017. "Generalized empirical likelihood M testing for semiparametric models with time series data," Econometrics and Statistics, Elsevier, vol. 4(C), pages 18-30.

    Cited by:

    1. Bravo, Francesco & Chu, Ba & Jacho-Chavez, David, 2013. "Semiparametric estimation of moment condition models with weakly dependent data," MPRA Paper 79686, University Library of Munich, Germany, revised 2016.
    2. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.

  5. Francesco Bravo & David T. Jacho-Chávez, 2016. "Semiparametric quasi-likelihood estimation with missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(5), pages 1345-1369, March.

    Cited by:

    1. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.

  6. Bravo, Francesco, 2015. "Semiparametric estimation with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 329-346.

    Cited by:

    1. Mojirsheibani, Majid & Shaw, Crystal, 2018. "Classification with incomplete functional covariates," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 40-46.
    2. Ana M. Bianco & Graciela Boente & Wenceslao González-Manteiga & Ana Pérez-González, 2019. "Plug-in marginal estimation under a general regression model with missing responses and covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 106-146, March.
    3. Francesco Bravo, 2020. "Robust estimation and inference for general varying coefficient models with missing observations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 966-988, December.
    4. Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
    5. Eric Han & Majid Mojirsheibani, 2021. "On histogram-based regression and classification with incomplete data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 635-662, July.

  7. Francesco Bravo, 2014. "Varying coefficients partially linear models with randomly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 383-412, April.

    Cited by:

    1. Yang, Seong J. & Shin, Hyejin & Lee, Sang Han & Lee, Seokho, 2020. "Functional linear regression model with randomly censored data: Predicting conversion time to Alzheimer ’s disease," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    2. Ai-Ai Liu & Han-Ying Liang, 2017. "Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models," Statistical Papers, Springer, vol. 58(1), pages 95-122, March.
    3. Zhou, Xing-cai & Xu, Ying-zhi & Lin, Jin-guan, 2017. "Wavelet estimation in varying coefficient models for censored dependent data," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 179-189.
    4. K. Hendrickx & P. Janssen & A. Verhasselt, 2018. "Penalized spline estimation in varying coefficient models with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 871-895, December.

  8. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.

    Cited by:

    1. Jun Zhang & Nanguang Zhou & Zipeng Sun & Gaorong Li & Zhenghong Wei, 2016. "Statistical inference on restricted partial linear regression models with partial distortion measurement errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 304-331, November.

  9. Francesco Bravo & Leslie G. Godfrey, 2012. "Bootstrap HAC Tests for Ordinary Least Squares Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(6), pages 903-922, December.

    Cited by:

    1. Harman Preet Singh & Ajay Singh & Fakhre Alam & Vikas Agrawal, 2022. "Impact of Sustainable Development Goals on Economic Growth in Saudi Arabia: Role of Education and Training," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    2. Andrea Fracasso & Giuseppe Vittucci Marzetti, 2012. "International R&D spillovers, absorptive capacity and relative backwardness: a panel smooth transition regression model," Department of Economics Working Papers 1203, Department of Economics, University of Trento, Italia.
    3. Pavel Svoboda, 2014. "Predicting Electricity Consumption in Corporate Sector—Case of the Czech Republic," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 20(2), pages 235-236, May.

  10. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    See citations under working paper version above.
  11. Francesco Bravo, 2012. "Generalized empirical likelihood testing in semiparametric conditional moment restrictions models," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 1-31, February.

    Cited by:

    1. Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
    3. Ivan Korolev, 2018. "A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models," Papers 1810.07620, arXiv.org, revised Nov 2019.

  12. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.

    Cited by:

    1. Francesco Bravo, 2013. "Partially linear varying coefficient models with missing at random responses," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(4), pages 721-762, August.
    2. Biao Zhang, 2016. "Empirical Likelihood in Causal Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(2), pages 201-231, February.
    3. Kim P. Huynh & David T. Jacho-Chávez & James K. Self, 2015. "The Distributional Efficacy of Collaborative Learning on Student Outcomes," The American Economist, Sage Publications, vol. 60(2), pages 98-119, September.

  13. Francesco Bravo, 2011. "Improved generalized method of moments estimators for weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 680-698, November.

    Cited by:

    1. Yiying Cheng & Yaozhong Hu & Hongwei Long, 2020. "Generalized moment estimators for $$\alpha $$α-stable Ornstein–Uhlenbeck motions from discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 53-81, April.

  14. Francesco Bravo, 2009. "Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models," Econometrics Journal, Royal Economic Society, vol. 12(2), pages 208-231, July.

    Cited by:

    1. Nordman, Daniel J. & Bunzel, Helle & Lahiri, Soumendra N., 2013. "A Nonstandard Empirical Likelihood for Time Series," Staff General Research Papers Archive 37203, Iowa State University, Department of Economics.
    2. Fumiya Akashi, 2017. "Self-weighted generalized empirical likelihood methods for hypothesis testing in infinite variance ARMA models," Statistical Inference for Stochastic Processes, Springer, vol. 20(3), pages 291-313, October.
    3. Wu, Rongning & Cao, Jiguo, 2011. "Blockwise empirical likelihood for time series of counts," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 661-673, March.
    4. Francesco Bravo & Federico Crudu, 2012. "Efficient bootstrap with weakly dependent processes," Discussion Papers 12/08, Department of Economics, University of York.
    5. Feifan Jiang & Lihong Wang, 2018. "Adjusted blockwise empirical likelihood for long memory time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 319-332, June.
    6. Chioneso S. Marange & Yongsong Qin & Raymond T. Chiruka & Jesca M. Batidzirai, 2023. "A Blockwise Empirical Likelihood Test for Gaussianity in Stationary Autoregressive Processes," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    7. Akashi, Fumiya & Taniguchi, Masanobu & Monti, Anna Clara, 2020. "Robust causality test of infinite variance processes," Journal of Econometrics, Elsevier, vol. 216(1), pages 235-245.

  15. Bravo, Francesco, 2009. "Two-step generalised empirical likelihood inference for semiparametric models," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1412-1431, August.

    Cited by:

    1. Francesco Bravo, 2014. "Varying coefficients partially linear models with randomly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 383-412, April.
    2. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.
    3. Bravo, Francesco & Chu, Ba M. & Jacho-Chávez, David T., 2017. "Generalized empirical likelihood M testing for semiparametric models with time series data," Econometrics and Statistics, Elsevier, vol. 4(C), pages 18-30.

  16. Francesco Bravo, 2005. "Blockwise empirical entropy tests for time series regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 185-210, March.

    Cited by:

    1. Jason Allen & Allan Gregory & Katsumi Shimotsu, 2008. "Empirical Likelihood Block Bootstrapping," Staff Working Papers 08-18, Bank of Canada.
    2. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Cowles Foundation Discussion Papers 1569, Cowles Foundation for Research in Economics, Yale University.
    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. Nordman, Daniel J. & Bunzel, Helle & Lahiri, Soumendra N., 2013. "A Nonstandard Empirical Likelihood for Time Series," Staff General Research Papers Archive 37203, Iowa State University, Department of Economics.
    5. Francesco Bravo, 2016. "Local Information Theoretic Methods for smooth Coefficients Dynamic Panel Data Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 690-708, September.
    6. 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.
    7. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.
    8. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," Levine's Bibliography 321307000000000307, UCLA Department of Economics.
    9. Chioneso S. Marange & Yongsong Qin & Raymond T. Chiruka & Jesca M. Batidzirai, 2023. "A Blockwise Empirical Likelihood Test for Gaussianity in Stationary Autoregressive Processes," Mathematics, MDPI, vol. 11(4), pages 1-20, February.

  17. Bravo, Francesco, 2004. "Empirical Likelihood Based Inference With Applications To Some Econometric Models," Econometric Theory, Cambridge University Press, vol. 20(2), pages 231-264, April.

    Cited by:

    1. Yoon-Jae Whang, 2003. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Econometrics 0310005, University Library of Munich, Germany.
    2. 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.
    3. Francesco Bravo, "undated". "Higher order asymptotics and the bootstrap for empirical likelihood J tests," Discussion Papers 00/30, Department of Economics, University of York.
    4. Kundhi, Gubhinder & Rilstone, Paul, 2012. "Edgeworth expansions for GEL estimators," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 118-146.
    5. Francesco Bravo, "undated". "Empirical likelihood specification testing in linear regression models," Discussion Papers 00/28, Department of Economics, University of York.
    6. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.
    7. Kakizawa, Yoshihide, 2016. "Some integrals involving multivariate Hermite polynomials: Application to evaluating higher-order local powers," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 162-168.
    8. Jose Blanchet & Yang Kang, 2021. "Sample Out-of-Sample Inference Based on Wasserstein Distance," Operations Research, INFORMS, vol. 69(3), pages 985-1013, May.
    9. Kakizawa, Yoshihide, 2010. "Comparison of Bartlett-type adjusted tests in the multiparameter case," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1638-1655, August.

  18. Francesco Bravo, 2003. "Second-order power comparisons for a class of nonparametric likelihood-based tests," Biometrika, Biometrika Trust, vol. 90(4), pages 881-890, December.

    Cited by:

    1. Kai-Tai Fang & Rahul Mukerjee, 2006. "Empirical-type likelihoods allowing posterior credible sets with frequentist validity: Higher-order asymptotics," Biometrika, Biometrika Trust, vol. 93(3), pages 723-733, September.
    2. Chang, In Hong & Mukerjee, Rahul, 2008. "Matching posterior and frequentist cumulative distribution functions with empirical-type likelihoods in the multiparameter case," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2793-2797, November.
    3. Hsin‐wen Chang & Ian W. McKeague, 2022. "Empirical likelihood‐based inference for functional means with application to wearable device data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1947-1968, November.
    4. In Chang & Rahul Mukerjee, 2012. "On the approximate frequentist validity of the posterior quantiles of a parametric function: results based on empirical and related likelihoods," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 156-169, March.
    5. Kakizawa, Yoshihide, 2016. "Some integrals involving multivariate Hermite polynomials: Application to evaluating higher-order local powers," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 162-168.
    6. Francesco Bravo, "undated". "Bartlett-type Adjustments for Empirical Discrepancy Test Statistics," Discussion Papers 04/14, Department of Economics, University of York.
    7. Kakizawa, Yoshihide, 2009. "Third-order power comparisons for a class of tests for multivariate linear hypothesis under general distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 473-496, March.
    8. Zang, Yangguang & Zhang, Sanguo & Li, Qizhai & Zhang, Qingzhao, 2016. "Jackknife empirical likelihood test for high-dimensional regression coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 302-316.
    9. Kakizawa, Yoshihide, 2010. "Comparison of Bartlett-type adjusted tests in the multiparameter case," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1638-1655, August.
    10. In Chang & Rahul Mukerjee, 2006. "Asymptotic Results on a General Class of Empirical Statistics: Power and Confidence Interval Properties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 427-440, September.
    11. In Hong Chang & Rahul Mukerjee, 2008. "Bayesian and frequentist confidence intervals arising from empirical-type likelihoods," Biometrika, Biometrika Trust, vol. 95(1), pages 139-147.

  19. Francesco Bravo, 2002. "Testing linear restrictions in linear models with empirical likelihood," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 104-130, June.

    Cited by:

    1. Yoon-Jae Whang, 2003. "Smoothed Empirical Likelihood Methods for Quantile Regression Models," Econometrics 0310005, University Library of Munich, Germany.
    2. Daniel Nordman, 2008. "An empirical likelihood method for spatial regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 351-363, November.

  20. Bravo, Francesco, 2002. "Blockwise empirical Cressie-Read test statistics for [alpha]-mixing processes," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 319-325, July.

    Cited by:

    1. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    2. Francesco Bravo, 2016. "Local Information Theoretic Methods for smooth Coefficients Dynamic Panel Data Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(5), pages 690-708, September.

  21. Bravo, Francesco, 1999. "A Correction Factor For Unit Root Test Statistics," Econometric Theory, Cambridge University Press, vol. 15(2), pages 218-227, April.

    Cited by:

    1. Canepa, Alessandra, 2020. "Improvement on the LR Test Statistic on the Cointegrating Relations in VAR Models: Bootstrap Methods and Applications," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202007, University of Turin.
    2. Pere, Pekka, 2000. "Adjusted estimates and Wald statistics for the AR(1) model with constant," Journal of Econometrics, Elsevier, vol. 98(2), pages 335-363, October.
    3. Noud P.A. van Giersbergen, 2013. "Bartlett correction in the stable second‐order autoregressive model with intercept and trend," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 482-498, November.
    4. Chambers, Marcus J. & Kyriacou, Maria, 2013. "Jackknife estimation with a unit root," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1677-1682.
    5. Canepa Alessandra, 2022. "Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors," Journal of Time Series Econometrics, De Gruyter, vol. 14(1), pages 51-85, January.

Chapters

  1. Francesco Bravo & Juan Carlos Escanciano & Taisuke Otsu, 2012. "A Simple Test for Identification in GMM under Conditional Moment Restrictions," Advances in Econometrics, in: Essays in Honor of Jerry Hausman, pages 455-477, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

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NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 8 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 (8) 2000-02-14 2000-08-02 2000-08-07 2004-08-09 2005-12-01 2008-09-13 2011-04-02 2012-03-28. Author is listed
  2. NEP-ETS: Econometric Time Series (1) 2005-12-01

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