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Juan Carlos Escanciano

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 Dec 2023.
    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. 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.
    7. 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.
    8. 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.
    9. 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. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.

    Cited by:

    1. Luis Alvarez & Cristine Pinto, 2023. "A maximal inequality for local empirical processes under weak dependence," Papers 2307.01328, arXiv.org.

  3. Carolina Caetano & Gregorio Caetano & Juan Carlos Escanciano, 2020. "Regression Discontinuity Design with Multivalued Treatments," Papers 2007.00185, arXiv.org.

    Cited by:

    1. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.

  4. Juan Carlos Escanciano & Chuan Goh, 2018. "Quantile-Regression Inference With Adaptive Control of Size," Papers 1807.06977, arXiv.org, revised Sep 2019.

    Cited by:

    1. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.

  5. Juan Carlos Escanciano & Wei Li, 2018. "Optimal Linear Instrumental Variables Approximations," Papers 1805.03275, arXiv.org, revised Feb 2020.

    Cited by:

    1. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    2. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    3. Jiafeng Chen & Daniel L. Chen & Greg Lewis, 2020. "Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models," Papers 2011.06158, arXiv.org, revised Jun 2021.
    4. Escanciano, Juan Carlos, 2023. "Irregular identification of structural models with nonparametric unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 234(1), pages 106-127.

  6. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2018. "Asymptotic distribution-free tests for semiparametric regressions with dependent data," LIDAM Reprints ISBA 2018039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Andrii Babii & Jean-Pierre Florens, 2017. "Are Unobservables Separable?," Papers 1705.01654, arXiv.org, revised Mar 2021.
    2. Giuseppe Cavaliere & Indeewara Perera & Anders Rahbek, 2021. "Specification tests for GARCH processes," Discussion Papers 21-06, University of Copenhagen. Department of Economics.
    3. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    4. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.

  7. Zaichao Du & Juan Carlos Escanciano & Guangwei Zhu, 2017. "Automatic Portmanteau Tests with Applications to Market Risk Management," CAEPR Working Papers 2017-002, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Carlotta Penone & Elisa Giampietri & Samuele Trestini, 2022. "Futures–spot price transmission in EU corn markets," Agribusiness, John Wiley & Sons, Ltd., vol. 38(3), pages 679-709, July.

  8. Juan Carlos Escanciano, 2016. "A Simple and Robust Estimator for Linear Regression Models with Strictly Exogenous Instruments," CAEPR Working Papers 2017-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    2. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    3. Xuexin WANG, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    4. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    5. Wayne Yuan Gao & Rui Wang, 2023. "IV Regressions without Exclusion Restrictions," Papers 2304.00626, arXiv.org, revised Jul 2023.

  9. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey, 2016. "Locally robust semiparametric estimation," CeMMAP working papers CWP31/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    7. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
    8. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Jul 2023.
    9. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org.
    10. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.
    11. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    12. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    13. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    14. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    15. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    16. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    17. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," Papers 1905.10116, arXiv.org, revised Jul 2019.
    18. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    20. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
    21. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    22. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    23. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Jan 2024.
    24. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    25. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Mar 2024.
    27. Greg Lewis & Vasilis Syrgkanis, 2020. "Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation," Papers 2002.07285, arXiv.org, revised Jun 2021.
    28. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Aug 2022.
    29. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    30. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    31. Thomas H. J{o}rgensen, 2020. "Sensitivity to Calibrated Parameters," Papers 2004.12100, arXiv.org, revised Mar 2021.
    32. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    33. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust decision-making under risk and ambiguity," Papers 2104.12573, arXiv.org, revised Oct 2021.
    34. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    35. Cattaneo, Matias D. & Jansson, Michael, 2022. "Average Density Estimators: Efficiency And Bootstrap Consistency," Econometric Theory, Cambridge University Press, vol. 38(6), pages 1140-1174, December.
    36. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
    37. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    38. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    39. Matias D. Cattaneo & Michael Jansson, 2018. "Kernel†Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency," Econometrica, Econometric Society, vol. 86(3), pages 955-995, May.
    40. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    41. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    42. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    43. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    44. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    45. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    46. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    47. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.
    48. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org.
    49. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    50. Bertille Antoine & Xiaolin Sun, 2020. "Partially Linear Models with Endogeneity: a conditional moment based approach," Discussion Papers dp20-06, Department of Economics, Simon Fraser University.
    51. 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.
    52. Rahul Singh, 2021. "Debiased Kernel Methods," Papers 2102.11076, arXiv.org, revised Mar 2021.
    53. Karun Adusumilli & Dita Eckardt, 2019. "Temporal-Difference estimation of dynamic discrete choice models," Papers 1912.09509, arXiv.org, revised Dec 2022.
    54. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    55. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," University of California at San Diego, Economics Working Paper Series qt86c7x315, Department of Economics, UC San Diego.
    56. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," THEMA Working Papers 2024-01, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    57. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised Nov 2023.
    58. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    59. 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.
    60. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    61. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    62. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    63. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    64. Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
    65. Ben Deaner, 2021. "Many Proxy Controls," Papers 2110.03973, arXiv.org.
    66. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    67. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    68. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    69. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    70. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    71. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    72. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    73. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    74. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    75. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    76. Liu, Yanghui & Li, Yehua & Carroll, Raymond J. & Wang, Naisyin, 2022. "Predictive functional linear models with diverging number of semiparametric single-index interactions," Journal of Econometrics, Elsevier, vol. 230(2), pages 221-239.
    77. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Jul 2023.
    78. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2018. "Deep Neural Networks for Estimation and Inference," Papers 1809.09953, arXiv.org, revised Sep 2019.
    79. Yusuke Narita & Shota Yasui & Kohei Yata, 2020. "Debiased Off-Policy Evaluation for Recommendation Systems," Papers 2002.08536, arXiv.org, revised Aug 2021.
    80. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org.
    81. Xinwei Ma & Jingshen Wang, 2018. "Robust Inference Using Inverse Probability Weighting," Papers 1810.11397, arXiv.org, revised May 2019.
    82. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust Decision-Making Under Risk and Ambiguity," ECONtribute Discussion Papers Series 104, University of Bonn and University of Cologne, Germany.
    83. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    84. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    85. Vasilis Syrgkanis & Ruohan Zhan, 2023. "Post-Episodic Reinforcement Learning Inference," Papers 2302.08854, arXiv.org, revised Jul 2023.

  10. Juan Carlos Escanciano & Stefan Hoderlein & Arthur Lewbel & Oliver Linton & Sorawoot Srisuma, 2015. "Nonparametric Euler equation identification and estimation," CeMMAP working papers CWP61/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Cui, Liyuan & Hong, Yongmiao & Li, Yingxing, 2021. "Solving Euler equations via two-stage nonparametric penalized splines," Journal of Econometrics, Elsevier, vol. 222(2), pages 1024-1056.
    2. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    3. Andreas Tryphonides, 2023. "Online Appendix to "Identifying Preferences when Households are Financially Constrained"," Online Appendices 21-242, Review of Economic Dynamics.
    4. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    5. Xiaohong Chen & Victor Chernozhukov & Sokbae (Simon) Lee & Whitney K. Newey, 2011. "Local identification of nonparametric and semiparametric models," CeMMAP working papers 17/11, Institute for Fiscal Studies.
    6. Marcel Fafchamps & Aditya Shrinivas, 2022. "Risk Pooling and Precautionary Saving in Village Economies," NBER Working Papers 30128, National Bureau of Economic Research, Inc.
    7. Giovanni Gallipoli & Brant Abbott, 2017. ""Permanent Income" Inequality," 2017 Meeting Papers 1033, Society for Economic Dynamics.
    8. Victor H. Aguiar & Nail Kashaev, 2018. "Stochastic Revealed Preferences with Measurement Error," Papers 1810.05287, arXiv.org, revised Sep 2020.
    9. Striani, Fabrizio, 2023. "Life-cycle consumption and life insurance: Empirical evidence from Italian Survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    10. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers CWP37/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Eshaghnia, Sadegh S. M. & Heckman, James J. & Landerso, Rasmus & Qureshi, Rafeh, 2022. "Intergenerational Transmission of Family Influence," IZA Discussion Papers 15504, Institute of Labor Economics (IZA).
    12. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness," Papers 2302.05404, arXiv.org.
    13. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," CEPR Discussion Papers 13240, C.E.P.R. Discussion Papers.
    14. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2018. "Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models," NBER Working Papers 25134, National Bureau of Economic Research, Inc.
    15. Andreas Tryphonides, 2020. "Identifying Preferences when Households are Financially Constrained," Papers 2005.02010, arXiv.org, revised Feb 2023.

  11. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Fontanari, Andrea & Cirillo, Pasquale & Oosterlee, Cornelis W., 2018. "From Concentration Profiles to Concentration Maps. New tools for the study of loss distributions," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 13-29.
    2. Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2018. "Forecasting Expected Shortfall: Should we use a Multivariate Model for Stock Market Factors?," Working Papers 18-4, HEC Montreal, Canada Research Chair in Risk Management, revised 25 Jun 2021.
    3. Nick Costanzino & Michael Curran, 2018. "A Simple Traffic Light Approach to Backtesting Expected Shortfall," Risks, MDPI, vol. 6(1), pages 1-7, January.
    4. An Chen & Mitja Stadje & Fangyuan Zhang, 2020. "On the equivalence between Value-at-Risk- and Expected Shortfall-based risk measures in non-concave optimization," Papers 2002.02229, arXiv.org, revised Jun 2022.
    5. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    6. Ophélie Couperier & Jérémy Leymarie, 2020. "Backtesting Expected Shortfall via Multi-Quantile Regression," Working Papers halshs-01909375, HAL.
    7. Kratz, Marie & Lok, Yen H. & McNeil, Alexander J., 2018. "Multinomial VaR backtests: A simple implicit approach to backtesting expected shortfall," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 393-407.
    8. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    9. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    10. Aleš Kresta & Tomáš Tichý & Mehdi Toloo, 2017. "Posouzení modelů odhadu tržního rizika s využitím DEA přístupu [Examination of Market Risk Estimation Models via DEA Approach Modelling]," Politická ekonomie, Prague University of Economics and Business, vol. 2017(2), pages 161-178.
    11. Hamed Tabasi & Vahidreza Yousefi & Jolanta Tamošaitienė & Foroogh Ghasemi, 2019. "Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models," Administrative Sciences, MDPI, vol. 9(2), pages 1-17, May.
    12. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.

  12. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2015. "Testing for Fundamental Vector Moving Average Representations," CAEPR Working Papers 2015-022, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Mario Forni & Luca Gambetti & Luca Sala, 2017. "News, Uncertainty and Economic Fluctuations (No News is Good News)," Center for Economic Research (RECent) 132, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    3. João Vitor Leme & Wallace Casaca & Marilaine Colnago & Maurício Araújo Dias, 2020. "Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models," Energies, MDPI, vol. 13(6), pages 1-20, March.
    4. Christian Gouriéroux & Alain Monfort & Jean-Paul Renne, 2016. "Statistical Inference for Independent Component Analysis: Application to Structural VAR Models," Working Papers 2016-20, Center for Research in Economics and Statistics.
    5. Beaudry, Paul & Fève, Patrick & Guay, Alain & Portier, Franck, 2016. "When is Nonfundamentalness in SVARs A Real Problem?," TSE Working Papers 16-738, Toulouse School of Economics (TSE).
    6. Hamidi Sahneh, Mehdi, 2015. "Are the shocks obtained from SVAR fundamental?," MPRA Paper 65126, University Library of Munich, Germany.
    7. Forni, Mario & Gambetti, Luca & Sala, Luca, 2017. "News, Uncertainty and Economic Fluctuations," CEPR Discussion Papers 12139, C.E.P.R. Discussion Papers.
    8. Gouriéroux, Christian & Zakoian, Jean-Michel, 2016. "Local Explosion Modelling by Noncausal Process," MPRA Paper 71105, University Library of Munich, Germany.
    9. Weifeng Jin, 2023. "Quantile Autoregression-based Non-causality Testing," Papers 2301.02937, arXiv.org.
    10. Fries, Sébastien, 2018. "Conditional moments of noncausal alpha-stable processes and the prediction of bubble crash odds," MPRA Paper 97353, University Library of Munich, Germany, revised Nov 2019.
    11. Gourieroux, Christian & Jasiak, Joann, 2018. "Misspecification of noncausal order in autoregressive processes," Journal of Econometrics, Elsevier, vol. 205(1), pages 226-248.
    12. Junjie Guo & Juan Carlos Escanciano & Jinho Choi, 2017. "Identification and Generalized Band Spectrum Estimation of the New Keynesian Phillips Curve," CAEPR Working Papers 2017-014, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    13. Mario Forni & Luca Gambetti & Luca Sala, 2018. "Fundamentalness, Granger Causality and Aggregation," Center for Economic Research (RECent) 139, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".

  13. Juan Carlos Escanciano & Wei Li, 2013. "On the identification of structural linear functionals," CeMMAP working papers CWP48/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Juan Carlos Escanciano & Wei Li, 2018. "Optimal Linear Instrumental Variables Approximations," Papers 1805.03275, arXiv.org, revised Feb 2020.
    2. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.

  14. Juan Carlos Escanciano & Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2013. "Semiparametric Estimation Of Risk-Return Relationships," CAEPR Working Papers 2013-004, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    2. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  15. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    2. Matthew Masten & Alexandre Poirier, 2017. "Inference on breakdown frontiers," CeMMAP working papers 20/17, Institute for Fiscal Studies.
    3. Mourifié, Ismael & Méango, Romuald, 2014. "A note on the identification in two equations probit model with dummy endogenous regressor," Economics Letters, Elsevier, vol. 125(3), pages 360-363.
    4. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    5. Fan, Yanqin & Shi, Xuetao & Tao, Jing, 2023. "Partial identification and inference in moment models with incomplete data," Journal of Econometrics, Elsevier, vol. 235(2), pages 418-443.

  16. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    2. Onder Buberkoku, 2018. "Examining the Value-at-risk Performance of Fractionally Integrated GARCH Models: Evidence from Energy Commodities," International Journal of Economics and Financial Issues, Econjournals, vol. 8(3), pages 36-50.
    3. Yun-Tao Shi & Xiang Xiang & Li Wang & Yuan Zhang & De-Hui Sun, 2018. "Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System," Energies, MDPI, vol. 11(1), pages 1-20, January.
    4. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    5. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    6. Bei, Shuhua & Yang, Aijun & Pei, Haotian & Si, Xiaoli, 2023. "Price Risk Analysis using GARCH Family Models: Evidence from Shanghai Crude Oil Futures Market," Economic Modelling, Elsevier, vol. 125(C).
    7. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    8. Taras Bodnar & Vilhelm Niklasson & Erik Thors'en, 2022. "Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR," Papers 2205.01444, arXiv.org.
    9. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    10. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    11. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    12. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    13. Durán Santomil, Pablo & Otero González, Luís & Martorell Cunill, Onofre & Merigó Lindahl, José M., 2018. "Backtesting an equity risk model under Solvency II," Journal of Business Research, Elsevier, vol. 89(C), pages 216-222.
    14. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.
    15. Ziggel, Daniel & Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2014. "A new set of improved Value-at-Risk backtests," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 29-41.
    16. Ilhami KARAHANOGLU, 2020. "The VaR comparison of the fresh investment toolBITCOIN with other conventional investment tools, gold, stock exchange (BIST100) and foreign currencies (EUR/USD VS TRL)," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 160-181, December.
    17. Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    18. Guillén, Montserrat & Sarabia, José María & Prieto, Faustino, 2013. "Simple risk measure calculations for sums of positive random variables," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 273-280.
    19. Gregor Wei{ss} & Marcus Scheffer, 2012. "Smooth Nonparametric Bernstein Vine Copulas," Papers 1210.2043, arXiv.org.

  17. Delgado, Miguel A. & Escanciano, Juan Carlos, 2011. "Conditional stochastic dominance testing," UC3M Working papers. Economics we1138, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    2. E. Agliardi & M. Pinar & T. Stengos, 2014. "Assessing temporal trends and industry contributions to air and water pollution using stochastic dominance," Working Papers wp981, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    4. Donald W.K. Andrews & Xiaoxia Shi, 2015. "Inference Based on Many Conditional Moment Inequalities," Cowles Foundation Discussion Papers 2010, Cowles Foundation for Research in Economics, Yale University.
    5. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    6. Shih-Kang Chao & Katharina Proksch & Holger Dette & Wolfgang Härdle, 2014. "Confidence Corridors for Multivariate Generalized Quantile Regression," SFB 649 Discussion Papers SFB649DP2014-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Brendan Kline, 2016. "Identification of the Direction of a Causal Effect by Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 176-184, April.

  18. 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. 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.
    3. 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.

  19. Juan Carlos Escanciano & David Jacho-Chavez & Arthur Lewbel, 2010. "Uniform Convergence of Weighted Sums of Non- and Semi-parametric Residuals for Estimation and Testing," Boston College Working Papers in Economics 756, Boston College Department of Economics, revised 31 Jan 2012.

    Cited by:

    1. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," CeMMAP working papers CWP27/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    3. Fabio A. Miessi Sanches & Daniel Silva Junior, Sorawoot Srisuma, 2015. "Minimum Distance Estimation of Search Costs using Price Distribution," Working Papers, Department of Economics 2015_31, University of São Paulo (FEA-USP).
    4. Giulia Bettin & Riccardo Lucchetti & Claudia Pigini, 2016. "State dependence and unobserved heterogeneity in a double hurdle model for remittances: evidence from immigrants to Germany," Mo.Fi.R. Working Papers 127, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    5. Enno Mammen & Christoph Rothe & Melanie Schienle, 2011. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers SFB649DP2011-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone, 2022. "An econometric panel data model of the COVID-19 pandemic," Post-Print hal-03641783, HAL.
    7. Shin Kanaya, 2015. "Uniform Convergence Rates of Kernel-Based Nonparametric Estimators for Continuous Time Diffusion Processes: A Damping Function Approach," CREATES Research Papers 2015-50, Department of Economics and Business Economics, Aarhus University.
    8. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
    9. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Locally Robust Estimation with Generated Regressors," Papers 2301.10643, arXiv.org, revised Nov 2023.
    10. Huang, Liquan & Khalil, Umair & Yıldız, Neşe, 2019. "Identification and estimation of a triangular model with multiple endogenous variables and insufficiently many instrumental variables," Journal of Econometrics, Elsevier, vol. 208(2), pages 346-366.
    11. Gutknecht, Daniel, 2016. "Testing for monotonicity under endogeneity," Journal of Econometrics, Elsevier, vol. 190(1), pages 100-114.
    12. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2015. "Asymptotic distribution-free tests for semiparametric regressions," LIDAM Discussion Papers ISBA 2015001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    14. 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.
    15. Louise Laage, 2020. "A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity," Papers 2003.09367, arXiv.org, revised Nov 2022.
    16. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jul 2019.
    17. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    18. Montoya-Blandón, Santiago & Jacho-Chávez, David T., 2020. "Semiparametric quasi maximum likelihood estimation of the fractional response model," Economics Letters, Elsevier, vol. 186(C).
    19. Feng Yao & Junsen Zhang, 2013. "Efficient Kernel-Based Semiparametric IV Estimation with an Application to Resolving a Puzzle on the Estimates of the Return to Schooling," Working Papers 13-01, Department of Economics, West Virginia University.
    20. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    21. Reza, Sadat & Rilstone, Paul, 2014. "A simple root-N-consistent semiparametric estimator for discrete duration models," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 150-154.
    22. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.
    23. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    24. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    25. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2013. "Maximum score estimation of preference parameters for a binary choice model under uncertainty," CeMMAP working papers CWP14/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Escanciano, J C. & Hoderlein, S. & Lewbel, A. & Linton, O. & Srisuma, S., 2020. "Nonparametric Euler Equation Identi?cation and Estimation," Cambridge Working Papers in Economics 2064, Faculty of Economics, University of Cambridge.
    27. Anastasia Semykina, 2016. "Self-Employment among Women: Do Children Matter More Than We Previously Thought?," Working Papers wp2016_07_02, Department of Economics, Florida State University.
    28. Klein, Roger & Shen, Chan & Vella, Francis, 2015. "Estimation of marginal effects in semiparametric selection models with binary outcomes," Journal of Econometrics, Elsevier, vol. 185(1), pages 82-94.
    29. 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).
    30. Elia Lapenta & Pascal Lavergne, 2022. "Encompassing Tests for Nonparametric Regressions," Papers 2203.06685, arXiv.org, revised Oct 2023.
    31. Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
    32. Juan Carlos Escanciano & Lin Zhu, 2015. "A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 734-762, December.
    33. Roger Klein & Chan Shen & Francis Vella, 2014. "Semiiparametric Selection Models with Binary Outcomes," Departmental Working Papers 201403, Rutgers University, Department of Economics.
    34. Lee, Ying-Ying, 2018. "Efficient propensity score regression estimators of multivalued treatment effects for the treated," Journal of Econometrics, Elsevier, vol. 204(2), pages 207-222.
    35. 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.
    36. Lapenta, Elia & Lavergne, Pascal, 2022. "Encompassing Tests for Nonparametric Regressions," TSE Working Papers 22-1332, Toulouse School of Economics (TSE).
    37. Laurent Delsol & Ingrid Van Keilegom, 2020. "Semiparametric M-estimation with non-smooth criterion functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 577-605, April.
    38. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.
    39. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  20. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.

    Cited by:

    1. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    2. Wang, Luya, 2022. "Adaptive testing using data-driven method selecting smoothing parameters," Economics Letters, Elsevier, vol. 215(C).
    3. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    4. Song Xi Chen & Jiti Gao, 2010. "Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models," School of Economics and Public Policy Working Papers 2010-28, University of Adelaide, School of Economics and Public Policy.
    5. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    7. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    8. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    9. Holger Dette & Stefan Hoderlein & Natalie Neumeyer, 2013. "Testing Multivariate Economic Restrictions Using Quantiles: The Example of Slutsky Negative Semidefiniteness," Boston College Working Papers in Economics 836, Boston College Department of Economics.
    10. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    11. Hong Cheng & Yunqing Wang & Yihong Wang & Tinggan Yang, 2022. "Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 719-748, February.
    12. 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.
    13. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    14. Anthoulla Phella, 2020. "Consistent Specification Test of the Quantile Autoregression," Papers 2010.03898, arXiv.org, revised Jan 2024.
    15. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    16. So, Mike K.P. & Chung, Ray S.W., 2015. "Statistical inference for conditional quantiles in nonlinear time series models," Journal of Econometrics, Elsevier, vol. 189(2), pages 457-472.
    17. Weifeng Jin, 2023. "Quantile Autoregression-based Non-causality Testing," Papers 2301.02937, arXiv.org.
    18. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    19. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    20. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
    21. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    22. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
    23. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.

  21. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.

    Cited by:

    1. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.

  22. Delgado, Miguel A. & Escanciano, Juan Carlos, 2010. "Testing conditional monotonicity in the absence of smoothness," UC3M Working papers. Economics we1017, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Denis Chetverikov, 2012. "Testing regression monotonicity in econometric models," CeMMAP working papers CWP35/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Denis Chetverikov, 2012. "Testing regression monotonicity in econometric models," CeMMAP working papers 35/12, Institute for Fiscal Studies.

  23. Juan Carlos Escanciano & Javier Hualde, 2009. "Persistence In Nonlinear Time Series: A Nonparametric Approach," CAEPR Working Papers 2009-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Linton, O. & Whang, Y-J. & Yen, Y., 2018. "The Lower Regression Function and Testing Expectation Dependence Dominance Hypotheses," Cambridge Working Papers in Economics 1880, Faculty of Economics, University of Cambridge.

  24. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Giuseppe Cavaliere & Indeewara Perera & Anders Rahbek, 2021. "Specification tests for GARCH processes," Discussion Papers 21-06, University of Copenhagen. Department of Economics.
    2. Hafner, C. M. & Linton, O. B. & Wang, L., 2024. "The Effect of Stock Splits on Liquidity in a Dynamic Model," Cambridge Working Papers in Economics 2410, Faculty of Economics, University of Cambridge.
    3. Hafner, C. M. & Linton, O. B. & Wang, L., 2024. "The Effect of Stock Splits on Liquidity in a Dynamic Model," Janeway Institute Working Papers 2404, Faculty of Economics, University of Cambridge.
    4. Perera, Indeewara & Silvapulle, Mervyn J., 2023. "Bootstrap specification tests for dynamic conditional distribution models," Journal of Econometrics, Elsevier, vol. 235(2), pages 949-971.
    5. Hafner, Christian & Linton, Oliver & Wang, Linqi, 2024. "The effect of stock splits on liquidity in a dynamic model," LIDAM Discussion Papers ISBA 2024007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Christian Francq & Olivier Wintenberger & Jean-Michel Zakoïan, 2018. "Goodness-of-fit tests for Log-GARCH and EGARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 27-51, March.
    7. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    8. Alejandra Cabaña & Enrique M. Cabaña & Marco Scavino, 2012. "Weak Convergence of Marked Empirical Processes for Focused Inference on AR(p) vs AR(p + 1) Stationary Time Series," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 793-810, September.
    9. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.

  25. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Teresa Ledwina & Grzegorz Wyłupek, 2012. "Nonparametric tests for stochastic ordering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 730-756, December.
    2. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    3. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    4. Hsu, Shih-Hsun & Kuan, Chung-Ming, 2014. "Constructing smooth tests without estimating the eigenpairs of the limiting process," Journal of Econometrics, Elsevier, vol. 178(P1), pages 71-79.
    5. Junjie Guo & Juan Carlos Escanciano & Jinho Choi, 2017. "Identification and Generalized Band Spectrum Estimation of the New Keynesian Phillips Curve," CAEPR Working Papers 2017-014, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. Huang, Henry H. & Wang, Kent & Wang, Zhanglong, 2016. "A test of efficiency for the S&P 500 index option market using the generalized spectrum method," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 52-70.

  26. Juan Carlos Escanciano & Kyungchul Song, 2007. "Asymptotically Optimal Tests for Single-Index Restrictions with a Focus on Average Partial Effects," PIER Working Paper Archive 07-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    Cited by:

    1. Kyungchul Song, 2007. "Testing Conditional Independence via Rosenblatt Transforms," PIER Working Paper Archive 07-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  27. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," CAEPR Working Papers 2007-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, revised Sep 2008.

    Cited by:

    1. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    2. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    3. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    4. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    5. Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2008. "Backtesting Value-at-Risk: A GMM Duration-Based-Test," Post-Print halshs-00364796, HAL.
    6. Kimera Naradh & Retius Chifurira & Knowledge Chinhamu, 2022. "Analysis of stock exchange risk and currency in South African Financial Markets using stable parameter estimation," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 11(1), pages 120-131, January.
    7. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    8. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    9. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    10. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    11. Sylvain Benoît & Christophe Hurlin & Christophe Pérignon, 2015. "Implied Risk Exposures," Post-Print hal-01485613, HAL.
    12. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.
    13. Denisa Banulescu & Christophe Hurlin & Jeremy Leymarie & O. Scaillet, 2019. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Swiss Finance Institute Research Paper Series 19-48, Swiss Finance Institute.
    14. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    15. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    16. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    17. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    18. Sander Barendse & Erik Kole & Dick van Dijk, 2023. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 528-568.
    19. Lönnbark, Carl, 2013. "On the role of the estimation error in prediction of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 847-853.
    20. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    21. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    22. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    23. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    24. 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.
    25. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    26. Michael B. Gordy & Alexander J. McNeil, 2017. "Spectral backtests of forecast distributions with application to risk management," Papers 1708.01489, arXiv.org, revised Jul 2019.
    27. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    28. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
    29. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    30. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    31. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    32. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Working Papers halshs-00671658, HAL.
    33. Zaichao Du & Juan Carlos Escanciano & Guangwei Zhu, 2017. "Automatic Portmanteau Tests with Applications to Market Risk Management," CAEPR Working Papers 2017-002, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    34. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    35. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    36. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    37. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    38. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    39. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    40. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    41. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.
    42. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    43. Bec, Frédérique & Gollier, Christian, 2014. "Cyclicality and term structure of Value-at-Risk within a threshold autoregression setup," IDEI Working Papers 835, Institut d'Économie Industrielle (IDEI), Toulouse.
    44. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    45. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    46. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    47. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    48. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    49. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.

  28. Juan Carlos Escanciano, 2007. "Joint and Marginal Diagnostic Tests for Conditional Mean and Variance Specifications," CAEPR Working Papers 2007-009, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    Cited by:

    1. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    2. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    3. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    4. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.

  29. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    2. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    3. Yacouba Boubacar Maïnassara & Youssef Esstafa & Bruno Saussereau, 2021. "Estimating FARIMA models with uncorrelated but non-independent error terms," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 549-608, October.
    4. Zhou, Xing-cai & Lin, Jin-guan, 2012. "A wavelet estimator in a nonparametric regression model with repeated measurements under martingale difference error’s structure," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1914-1922.

  30. Juan Carlos Escanciano, 2006. "Joint Diagnostic Tests for Conditional Mean and Variance Specifications," Faculty Working Papers 02/06, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Song Xi Chen & Jiti Gao, 2010. "Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models," School of Economics and Public Policy Working Papers 2010-28, University of Adelaide, School of Economics and Public Policy.

  31. Juan Carlos Escanciano, 2005. "A Consistent Diagnostic Test for Regression Models Using Projections," Faculty Working Papers 09/05, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Manuel A. Domínguez & Ignacio N. Lobato, 2020. "Specification testing with estimated variables," Econometric Reviews, Taylor & Francis Journals, vol. 39(5), pages 476-494, May.
    3. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    4. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    5. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    6. Juan Carlos Escanciano, 2004. "Model Checks Using Residual Marked Empirical Processes," Faculty Working Papers 13/04, School of Economics and Business Administration, University of Navarra.
    7. Yasser Al Zaim & Mohammad Reza Faridrohani, 2021. "Bayesian random projection-based signal detection for Gaussian scale space random fields," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 503-532, September.
    8. Kloodt, Nick & Neumeyer, Natalie, 2020. "Specification tests in semiparametric transformation models — A multiplier bootstrap approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    9. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    10. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    11. Jun Zhang & Junpeng Zhu & Yan Zhou & Xia Cui & Tao Lu, 2020. "Multiplicative regression models with distortion measurement errors," Statistical Papers, Springer, vol. 61(5), pages 2031-2057, October.
    12. Juan Carlos Escanciano & Wei Li, 2018. "Optimal Linear Instrumental Variables Approximations," Papers 1805.03275, arXiv.org, revised Feb 2020.
    13. Zhihua Sun & Dongshan Luo & Xiaohua Zhou & Qingzhao Zhang, 2021. "Comparative studies on the adequacy check of parametric measurement error models with auxiliary variable," Statistical Papers, Springer, vol. 62(4), pages 1723-1751, August.
    14. 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.
    15. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    16. Eduardo García‐Portugués & Javier Álvarez‐Liébana & Gonzalo Álvarez‐Pérez & Wenceslao González‐Manteiga, 2021. "A goodness‐of‐fit test for the functional linear model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 502-528, June.
    17. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    18. Pascal Lavergne & Valentin Patilea, 2008. "One for All and All for One:Regression Checks With Many Regressors," Discussion Papers dp08-06, Department of Economics, Simon Fraser University.
    19. Cui Rui & Li Yuhao, 2024. "Goodness-of-Fit for Conditional Distributions: An Approach Using Principal Component Analysis and Component Selection," Papers 2403.10352, arXiv.org.
    20. Xu, Kai & Zhou, Yeqing, 2021. "Projection-averaging-based cumulative covariance and its use in goodness-of-fit testing for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    21. Li, Lingzhu & Chiu, Sung Nok & Zhu, Lixing, 2019. "Model checking for regressions: An approach bridging between local smoothing and global smoothing methods," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 64-82.
    22. 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.
    23. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
    24. Wang, Xuexin, 2015. "A Note on Consistent Conditional Moment Tests," MPRA Paper 69005, University Library of Munich, Germany.
    25. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.
    26. Jun Zhang & Xia Cui & Heng Peng, 2020. "Estimation and hypothesis test for partial linear single-index multiplicative models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 699-740, June.
    27. Hongtu Zhu & Joseph G. Ibrahim & Ming-Hui Chen, 2015. "Diagnostic measures for the Cox regression model with missing covariates," Biometrika, Biometrika Trust, vol. 102(4), pages 907-923.
    28. Miguel A. Delgado & Juan Carlos Escanciano, 2013. "Conditional Stochastic Dominance Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 16-28, January.
    29. Hongtu Zhu & Joseph G. Ibrahim & Xiaoyan Shi, 2009. "Diagnostic Measures for Generalized Linear Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 686-712, December.
    30. 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.
    31. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    32. Juan Carlos Escanciano & Kyungchul Song, 2007. "Asymptotically Optimal Tests for Single-Index Restrictions with a Focus on Average Partial Effects," PIER Working Paper Archive 07-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    33. Tao Chen & Gautam Tripathi, 2014. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," DEM Discussion Paper Series 14-04, Department of Economics at the University of Luxembourg.
    34. Andrea Vaona, 2008. "The sensitivity of nonparametric misspecification tests to disturbance autocorrelation," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0803, USI Università della Svizzera italiana.
    35. Jun Zhang, 2021. "Model checking for multiplicative linear regression models with mixed estimators," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 364-403, August.
    36. Song, Kyungchul, 2010. "Testing semiparametric conditional moment restrictions using conditional martingale transforms," Journal of Econometrics, Elsevier, vol. 154(1), pages 74-84, January.
    37. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    38. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    39. Pascal Lavergne & Valentin Patilea, 2006. "Breaking the Curse of Dimensionality in Nonparametric Testing," Working Papers 2006-24, Center for Research in Economics and Statistics.
    40. Elia Lapenta & Pascal Lavergne, 2022. "Encompassing Tests for Nonparametric Regressions," Papers 2203.06685, arXiv.org, revised Oct 2023.
    41. Liu, Jicai & Si, Yuefeng & Niu, Yong & Zhang, Riquan, 2022. "Projection quantile correlation and its use in high-dimensional grouped variable screening," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    42. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    43. Juan Carlos Escanciano & Joel Robert Terschuur, 2022. "Machine Learning Inference on Inequality of Opportunity," Papers 2206.05235, arXiv.org, revised Oct 2023.
    44. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.
    45. Chen, Feifei & Jiang, Qing & Feng, Zhenghui & Zhu, Lixing, 2020. "Model checks for functional linear regression models based on projected empirical processes," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    46. Juan Carlos Escanciano, 2010. "The Integrated Instrumental Variables Estimator: Exploiting Nonlinearities for Identification of Linear Models," CAEPR Working Papers 2010-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    47. Zhang, Jun & Feng, Zhenghui & Zhou, Bu, 2014. "A revisit to correlation analysis for distortion measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 116-129.
    48. Sun, Zhihua & Chen, Feifei & Zhou, Xiaohua & Zhang, Qingzhao, 2017. "Improved model checking methods for parametric models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 147-161.
    49. Escanciano, Juan Carlos & Song, Kyungchul, 2010. "Testing single-index restrictions with a focus on average derivatives," Journal of Econometrics, Elsevier, vol. 156(2), pages 377-391, June.
    50. Lapenta, Elia & Lavergne, Pascal, 2022. "Encompassing Tests for Nonparametric Regressions," TSE Working Papers 22-1332, Toulouse School of Economics (TSE).
    51. Junmin Liu & Deli Zhu & Luoyao Yu & Xuehu Zhu, 2023. "Specification testing of partially linear single-index models: a groupwise dimension reduction-based adaptive-to-model approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 232-262, March.
    52. James Davidson & Andreea G. Halunga, 2013. "Consistent Model Specification Testing," Discussion Papers 1312, University of Exeter, Department of Economics.
    53. Cuizhen Niu & Lixing Zhu, 2018. "A robust adaptive-to-model enhancement test for parametric single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1013-1045, October.

  32. Juan Carlos Escanciano, 2005. "Goodness-of-fit Tests for Linear and Non-linear Time Series Models," Faculty Working Papers 02/05, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    3. Wang, Qiying & Wu, Dongsheng & Zhu, Ke, 2018. "Model checks for nonlinear cointegrating regression," Journal of Econometrics, Elsevier, vol. 207(2), pages 261-284.
    4. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    5. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    6. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    7. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    8. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.
    9. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    10. Li, Haiqi & Zhong, Wanling & Park, Sung Y., 2016. "Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations," Economic Modelling, Elsevier, vol. 52(PB), pages 661-671.
    11. Escanciano, J. Carlos, 2006. "A Consistent Diagnostic Test For Regression Models Using Projections," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1030-1051, December.
    12. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    13. Domínguez, Manuel A. & Lobato, Ignacio N., 2006. "A consistent specification test for models defined by conditional moment restrictions," UC3M Working papers. Economics we064111, Universidad Carlos III de Madrid. Departamento de Economía.
    14. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    15. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    16. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    17. Ke Zhu, 2018. "Statistical inference for autoregressive models under heteroscedasticity of unknown form," Papers 1804.02348, arXiv.org, revised Aug 2018.
    18. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    19. Pascal Lavergne & Valentin Patilea, 2008. "One for All and All for One:Regression Checks With Many Regressors," Discussion Papers dp08-06, Department of Economics, Simon Fraser University.
    20. Manuel Landajo & María Presno, 2013. "Nonparametric pseudo-Lagrange multiplier stationarity testing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 125-147, February.
    21. Kasparis, Ioannis, 2010. "The Bierens test for certain nonstationary models," Journal of Econometrics, Elsevier, vol. 158(2), pages 221-230, October.
    22. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    23. Donghang Luo & Ke Zhu & Huan Gong & Dong Li, 2020. "Testing error distribution by kernelized Stein discrepancy in multivariate time series models," Papers 2008.00747, arXiv.org.
    24. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    25. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    26. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.
    27. Hamidi Sahneh, Mehdi, 2015. "Are the shocks obtained from SVAR fundamental?," MPRA Paper 65126, University Library of Munich, Germany.
    28. Landajo, Manuel & Presno, María José, 2010. "Nonparametric pseudo-Lagrange multiplier stationarity testing," MPRA Paper 25659, University Library of Munich, Germany.
    29. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    30. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    31. Manuel Vega-Gordillo & José Luis à lvarez-Arce, 2005. "Heterogeneity In Economic Freedom: Free Clusters Or Free Countries," Faculty Working Papers 08/05, School of Economics and Business Administration, University of Navarra.
    32. Junjie Guo & Juan Carlos Escanciano & Jinho Choi, 2017. "Identification and Generalized Band Spectrum Estimation of the New Keynesian Phillips Curve," CAEPR Working Papers 2017-014, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    33. Andisheh Saliminezhad & Pejman Bahramian, 2021. "The role of financial stress in the economic activity: Fresh evidence from a Granger‐causality in quantiles analysis for the UK and Germany," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1670-1680, April.
    34. Jiménez-Gamero, M.D. & Alba-Fernández, M.V., 2021. "A test for the geometric distribution based on linear regression of order statistics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 103-123.
    35. Colling, Benjamin & Van Keilegom, Ingrid, 2017. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 10-30.
    36. Manuel Landajo & María José Presno, 2010. "Stationarity testing under nonlinear models. Some asymptotic results," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 392-405, September.
    37. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.
    38. Manuel Dominguez & Ignacio Lobato, 2010. "Consistent Inference in Models Defined by COnditional Moment Restrictions: an Alternative to GMM," Working Papers 1005, Centro de Investigacion Economica, ITAM.
    39. Juan Carlos Escanciano, 2005. "On the Asymptotic Power Properties of Specification Tests for Dynamic Parametric Regressions," Faculty Working Papers 07/05, School of Economics and Business Administration, University of Navarra.
    40. Juan Carlos Escanciano, 2006. "Joint Diagnostic Tests for Conditional Mean and Variance Specifications," Faculty Working Papers 02/06, School of Economics and Business Administration, University of Navarra.
    41. Cuizhen Niu & Lixing Zhu, 2018. "A robust adaptive-to-model enhancement test for parametric single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1013-1045, October.

  33. Juan Carlos Escanciano, 2005. "On the Asymptotic Power Properties of Specification Tests for Dynamic Parametric Regressions," Faculty Working Papers 07/05, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.

  34. Juan Carlos Escanciano, 2004. "Model Checks Using Residual Marked Empirical Processes," Faculty Working Papers 13/04, School of Economics and Business Administration, University of Navarra.

    Cited by:

    1. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    2. Juan Carlos Escanciano, 2005. "Goodness-of-fit Tests for Linear and Non-linear Time Series Models," Faculty Working Papers 02/05, School of Economics and Business Administration, University of Navarra.
    3. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.
    4. Nedeljković, Milan & Urošević, Branko, 2012. "Determinants of the Dinar-Euro Nominal Exchange Rate," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 121-141, September.
    5. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    6. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    7. Anne Leucht & Michael Neumann, 2013. "Degenerate $$U$$ - and $$V$$ -statistics under ergodicity: asymptotics, bootstrap and applications in statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(2), pages 349-386, April.
    8. Vanessa Berenguer Rico & Bent Nielsen, 2017. "Marked and Weighted Empirical Processes of Residuals with Applications to Robust Regressions," Economics Series Working Papers 841, University of Oxford, Department of Economics.
    9. James Davidson & Andreea G. Halunga, 2013. "Consistent Model Specification Testing," Discussion Papers 1312, University of Exeter, Department of Economics.
    10. Juan Carlos Escanciano, 2005. "On the Asymptotic Power Properties of Specification Tests for Dynamic Parametric Regressions," Faculty Working Papers 07/05, School of Economics and Business Administration, University of Navarra.
    11. Juan Carlos Escanciano, 2006. "Joint Diagnostic Tests for Conditional Mean and Variance Specifications," Faculty Working Papers 02/06, School of Economics and Business Administration, University of Navarra.

  35. Escanciano, Juan Carlos & Velasco, Carlos, 2003. "Generalized spectral tests for the martingale difference hypothesis," DES - Working Papers. Statistics and Econometrics. WS ws035312, Universidad Carlos III de Madrid. Departamento de Estadística.

    Cited by:

    1. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2011. "Small sample properties of alternative tests for martingale difference hypothesis," Economics Letters, Elsevier, vol. 110(2), pages 151-154, February.
    2. Mengya Liu & Fukan Zhu & Ke Zhu, 2020. "Multi-frequency-band tests for white noise under heteroskedasticity," Papers 2004.09161, arXiv.org.
    3. Wang, Qiying & Wu, Dongsheng & Zhu, Ke, 2018. "Model checks for nonlinear cointegrating regression," Journal of Econometrics, Elsevier, vol. 207(2), pages 261-284.
    4. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    5. Juan Carlos Escanciano, 2005. "Goodness-of-fit Tests for Linear and Non-linear Time Series Models," Faculty Working Papers 02/05, School of Economics and Business Administration, University of Navarra.
    6. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    7. Juan Carlos Escanciano, 2004. "Model Checks Using Residual Marked Empirical Processes," Faculty Working Papers 13/04, School of Economics and Business Administration, University of Navarra.
    8. Amélie Charles & Olivier Darné & Jae H. Kim & Etienne Redor, 2016. "Stock Exchange Mergers and Market," Post-Print hal-01238707, HAL.
    9. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    10. Zheng, Tingguo & Xiao, Han & Chen, Rong, 2015. "Generalized ARMA models with martingale difference errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 492-506.
    11. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    12. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    13. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2011. "Testing the martingale difference hypothesis in CO2 emission allowances," Economic Modelling, Elsevier, vol. 28(1-2), pages 27-35, January.
    14. Shao, Xiaofeng, 2011. "A bootstrap-assisted spectral test of white noise under unknown dependence," Journal of Econometrics, Elsevier, vol. 162(2), pages 213-224, June.
    15. Juan Carlos Escanciano & Carlos Velasco, 2006. "Testing the Martingale Difference Hypothesis Using Integrated Regression Functions," Faculty Working Papers 06/06, School of Economics and Business Administration, University of Navarra.
    16. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    17. Lazăr, Dorina & Todea, Alexandru & Filip, Diana, 2012. "Martingale difference hypothesis and financial crisis: Empirical evidence from European emerging foreign exchange markets," Economic Systems, Elsevier, vol. 36(3), pages 338-350.
    18. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    19. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    20. Amélie Charles & Olivier Darné & Jae H. Kim, 2010. "Exchange-Rate Return Predictability and the Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates," Working Papers hal-00547722, HAL.
    21. Li, Haiqi & Zhong, Wanling & Park, Sung Y., 2016. "Generalized cross-spectral test for nonlinear Granger causality with applications to money–output and price–volume relations," Economic Modelling, Elsevier, vol. 52(PB), pages 661-671.
    22. Gourieroux, Christian & Jasiak, Joann, 2019. "Robust analysis of the martingale hypothesis," Econometrics and Statistics, Elsevier, vol. 9(C), pages 17-41.
    23. Noda, Akihiko, 2016. "A test of the adaptive market hypothesis using a time-varying AR model in Japan," Finance Research Letters, Elsevier, vol. 17(C), pages 66-71.
    24. Gyamfi NE & Kyei KA & Gill R, 2016. "African Stock Markets and Return Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 8(5), pages 91-99.
    25. Chen, Bin & Song, Zhaogang, 2013. "Testing whether the underlying continuous-time process follows a diffusion: An infinitesimal operator-based approach," Journal of Econometrics, Elsevier, vol. 173(1), pages 83-107.
    26. Escanciano, J. Carlos, 2007. "Weak convergence of non-stationary multivariate marked processes with applications to martingale testing," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1321-1336, August.
    27. Verheyden, Tim & De Moor, Lieven & Van den Bossche, Filip, 2015. "Towards a new framework on efficient markets," Research in International Business and Finance, Elsevier, vol. 34(C), pages 294-308.
    28. Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    29. McPherson, Matthew Q. & Palardy, Joseph, 2007. "Are international stock returns predictable?: An examination of linear and non-linear predictability using generalized spectral tests," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(5), pages 452-464, December.
    30. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate Variance Ratio Statistics," Cambridge Working Papers in Economics 1459, Faculty of Economics, University of Cambridge.
    31. Herman J. Bierens & Li Wang, 2017. "Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 103-135, March.
    32. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    33. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    34. Alexandru Todea & Dorina Lazar, 2012. "Global Crisis and Relative Efficiency: Empirical Evidence from Central and Eastern European Stock Markets," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 4(1), pages 045-053, June.
    35. Peter C.B. Phillips & Sainan Jin, 2013. "Testing the Martingale Hypothesis," Cowles Foundation Discussion Papers 1912, Cowles Foundation for Research in Economics, Yale University.
    36. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.
    37. Amélie Charles & Olivier Darné & Jae H. Kim & Etienne Redor, 2014. "Stock Exchange Mergers and Market Efficiency," Working Papers hal-00940105, HAL.
    38. Mirzaee Ghazani, Majid & Khalili Araghi, Mansour, 2014. "Evaluation of the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the Tehran stock exchange," Research in International Business and Finance, Elsevier, vol. 32(C), pages 50-59.
    39. 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.
    40. Yuzhakov, Vladimir (Южаков, Владимир) & Boykov, Vladimir (Бойков, Владимир) & Dobrolyubova, Elena (Добролюбова, Елена) & Pokida, Andrey (Покида, Андрей) & Zybunovskaya, Natalia (Зыбуновская, Наталья), 2014. "On the way to the service state: satisfaction of the recipients of public services [На Пути К Сервисному Государству: Удовлетворенность Получателей Государственных Услуг]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, pages 116-142.
    41. Hong Cheng & Yunqing Wang & Yihong Wang & Tinggan Yang, 2022. "Inferring Causal Interactions in Financial Markets Using Conditional Granger Causality Based on Quantile Regression," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 719-748, February.
    42. Dzung Phan Tran Trung & Hung Pham Quang, 2019. "Adaptive Market Hypothesis: Evidence from the Vietnamese Stock Market," JRFM, MDPI, vol. 12(2), pages 1-16, May.
    43. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2014. "Multivariate variance ratio statistics," CeMMAP working papers 29/14, Institute for Fiscal Studies.
    44. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    45. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    46. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    47. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    48. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    49. Hamidi Sahneh, Mehdi, 2015. "Are the shocks obtained from SVAR fundamental?," MPRA Paper 65126, University Library of Munich, Germany.
    50. Kralik Lóránd István, 2018. "Conditional Correlation on CEE Stock Markets," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 130-136, December.
    51. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
    52. Moon, Seongman & Velasco, Carlos, 2013. "Tests for m-dependence based on sample splitting methods," Journal of Econometrics, Elsevier, vol. 173(2), pages 143-159.
    53. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    54. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    55. Kenichi Hirayama & Akihiko Noda, 2019. "Measuring the Time-Varying Market Efficiency in the Prewar Japanese Stock Market, 1924-1943," Papers 1911.04059, arXiv.org, revised Dec 2022.
    56. Diniz-Maganini, Natalia & Rasheed, Abdul A. & Sheng, Hsia Hua, 2023. "Price efficiency of the foreign exchange rates of BRICS countries: A comparative analysis," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(1).
    57. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    58. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2015. "An investigation into multivariate variance ratio statistics and their application to stock market predictability," CeMMAP working papers 13/15, Institute for Fiscal Studies.
    59. Shahid, Muhammad Naeem & Azmi, Wajahat & Ali, Mohsin & Islam, Muhammad Umar & Rizvi, Syed Aun R., 2023. "Uncovering risk transmission between socially responsible investments, alternative energy investments and the implied volatility of major commodities," Energy Economics, Elsevier, vol. 120(C).
    60. Raffaella Giacomini & Dimitris N. Politis & Halbert White, 2012. "A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators," CeMMAP working papers CWP11/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    61. Jinho Choi & Juan Carlos Escanciano & Junjie Guo, 2022. "Generalized band spectrum estimation with an application to the New Keynesian Phillips curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1055-1078, August.
    62. Victor Dragotă & Elena Ţilică, 2014. "Market efficiency of the Post Communist East European stock markets," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 307-337, June.
    63. Huai-Long Shi & Zhi-Qiang Jiang & Wei-Xing Zhou, 2016. "Time-varying return predictability in the Chinese stock market," Papers 1611.04090, arXiv.org.
    64. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.
    65. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    66. Pınar Evrim Mandacı & F. Dilvin Taskın & Zeliha Can Ergun, 2019. "Adaptive Market Hypothesis," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 84-101.
    67. Ghazani, Majid Mirzaee & Ebrahimi, Seyed Babak, 2019. "Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices," Finance Research Letters, Elsevier, vol. 30(C), pages 60-68.
    68. Dilip Kumar & Srinivasan Maheswaran, 2014. "Are major global stock markets efficient? An application of the martingale difference hypothesis with wild bootstrap," American Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 3(2/3/4), pages 217-233.
    69. Seok Young Hong & Oliver Linton & Hui Jun Zhang, 2015. "An investigation into multivariate variance ratio statistics and their application to stock market predictability," CeMMAP working papers CWP13/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    70. Majid Mirzaee Ghazani & Mohammad Ali Jafari, 2021. "Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    71. Junjie Guo & Juan Carlos Escanciano & Jinho Choi, 2017. "Identification and Generalized Band Spectrum Estimation of the New Keynesian Phillips Curve," CAEPR Working Papers 2017-014, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    72. Muhammad Naeem Shahid, 2022. "COVID-19 and adaptive behavior of returns: evidence from commodity markets," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    73. Andisheh Saliminezhad & Pejman Bahramian, 2021. "The role of financial stress in the economic activity: Fresh evidence from a Granger‐causality in quantiles analysis for the UK and Germany," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1670-1680, April.
    74. Xiaojun Song & Haoyu Wei, 2021. "Nonparametric Tests of Conditional Independence for Time Series," Papers 2110.04847, arXiv.org.
    75. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary University of London, School of Economics and Finance.
    76. Todea, Alexandru & Pleşoianu, Anita, 2013. "The influence of foreign portfolio investment on informational efficiency: Empirical evidence from Central and Eastern European stock markets," Economic Modelling, Elsevier, vol. 33(C), pages 34-41.
    77. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    78. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    79. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    80. Assaf, Ata & Kristoufek, Ladislav & Demir, Ender & Kumar Mitra, Subrata, 2021. "Market efficiency in the art markets using a combination of long memory, fractal dimension, and approximate entropy measures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    81. Okorie, David Iheke & Lin, Boqiang, 2021. "Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    82. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.
    83. Chang, Jinyuan & Jiang, Qing & Shao, Xiaofeng, 2023. "Testing the martingale difference hypothesis in high dimension," Journal of Econometrics, Elsevier, vol. 235(2), pages 972-1000.
    84. Juan Carlos Escanciano, 2005. "On the Asymptotic Power Properties of Specification Tests for Dynamic Parametric Regressions," Faculty Working Papers 07/05, School of Economics and Business Administration, University of Navarra.
    85. Victor Dragota & Dragos Stefan Oprea, 2014. "Informational Efficiency Tests on the Romanian Stock Market: A Review of the Literature," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(1), pages 015-028, June.
    86. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    87. Juan Carlos Escanciano, 2006. "Joint Diagnostic Tests for Conditional Mean and Variance Specifications," Faculty Working Papers 02/06, School of Economics and Business Administration, University of Navarra.
    88. Afees A. Salisu & Taofeek O. Ayinde, 2016. "Testing the Martingale Difference Hypothesis (MDH) with Structural Breaks: Evidence from Foreign Exchanges of Nigeria and South Africa," Journal of African Business, Taylor & Francis Journals, vol. 17(3), pages 342-359, September.
    89. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

Articles

  1. Escanciano, Juan Carlos & Li, Wei, 2021. "Optimal Linear Instrumental Variables Approximations," Journal of Econometrics, Elsevier, vol. 221(1), pages 223-246.
    See citations under working paper version above.
  2. Escanciano, Juan Carlos & Hoderlein, Stefan & Lewbel, Arthur & Linton, Oliver & Srisuma, Sorawoot, 2021. "Nonparametric Euler Equation Identification And Estimation," Econometric Theory, Cambridge University Press, vol. 37(5), pages 851-891, October.
    See citations under working paper version above.
  3. Caetano, Carolina & Escanciano, Juan Carlos, 2021. "Identifying Multiple Marginal Effects With A Single Instrument," Econometric Theory, Cambridge University Press, vol. 37(3), pages 464-494, June.

    Cited by:

    1. Carolina Caetano & Gregorio Caetano & Juan Carlos Escanciano, 2023. "Regression discontinuity design with multivalued treatments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 840-856, September.
    2. Michael Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023. "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," Working Papers 23-01, Center for Economic Studies, U.S. Census Bureau.

  4. J. C. Escanciano & S. C. Goh, 2019. "Quantile-Regression Inference With Adaptive Control of Size," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1382-1393, July.
    See citations under working paper version above.
  5. Juan Carlos Escanciano, 2018. "A simple and robust estimator for linear regression models with strictly exogenous instruments," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 36-54, February.
    See citations under working paper version above.
  6. Guangwei Zhu & Zaichao Du & Juan Carlos Escanciano, 2017. "Automatic portmanteau tests with applications to market risk management," Stata Journal, StataCorp LP, vol. 17(4), pages 901-915, December.
    See citations under working paper version above.
  7. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    See citations under working paper version above.
  8. Juan Carlos Escanciano & Juan Carlos Pardo-Fernández & Ingrid Van Keilegom, 2017. "Semiparametric Estimation of Risk–Return Relationships," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 40-52, January.
    See citations under working paper version above.
  9. Juan Carlos Escanciano & David Jacho‐Chávez & Arthur Lewbel, 2016. "Identification and estimation of semiparametric two‐step models," Quantitative Economics, Econometric Society, vol. 7(2), pages 561-589, July.

    Cited by:

    1. Arthur Lewbel, 2016. "Identification and Estimation Using Heteroscedasticity Without Instruments: The Binary Endogenous Regressor Case," Boston College Working Papers in Economics 927, Boston College Department of Economics.
    2. Enno Mammen & Christoph Rothe & Melanie Schienle, 2011. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers SFB649DP2011-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    3. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    4. 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.
    5. Yingying Dong & Arthur Lewbel, 2012. "A Simple Estimator for Binary Choice Models With Endogenous Regressors," Boston College Working Papers in Economics 807, Boston College Department of Economics.
    6. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    7. Alyssa Carlson, 2021. "Relaxing Conditional Independence in an Endogenous Binary Response Model," Working Papers 2113, Department of Economics, University of Missouri.
    8. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2013. "Maximum score estimation of preference parameters for a binary choice model under uncertainty," CeMMAP working papers CWP14/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Klein, Roger & Shen, Chan & Vella, Francis, 2015. "Estimation of marginal effects in semiparametric selection models with binary outcomes," Journal of Econometrics, Elsevier, vol. 185(1), pages 82-94.
    10. Bo E. Honoré & Luojia Hu, 2020. "Selection Without Exclusion," Econometrica, Econometric Society, vol. 88(3), pages 1007-1029, May.
    11. Samuele Centorrino & Jean-Pierre Florens, 2014. "Nonparametric Instrumental Variable Estimation of Binary Response Models," Department of Economics Working Papers 14-07, Stony Brook University, Department of Economics.
    12. Zhewen Pan & Zhengxin Wang & Junsen Zhang & Yahong Zhou, 2024. "Marginal treatment effects in the absence of instrumental variables," Papers 2401.17595, arXiv.org.
    13. Patricia Moreno-Mencía & Juan M. Rodríguez-Poo & David Cantarero-Prieto, 2021. "A Multi-step Process Approach for Estimating Public Sector Wages. The Spanish Expe¬rience," Hacienda Pública Española / Review of Public Economics, IEF, vol. 237(2), pages 33-56, June.
    14. Songnian Chen & Shakeeb Khan & Xun Tang, 2020. "Dummy Endogenous Variables in Weakly Separable Multiple Index Models without Monotonicity," Boston College Working Papers in Economics 996, Boston College Department of Economics.
    15. Songnian Chen & Shakeeb Khan & Xun Tang, 2020. "Identification and Estimation of Weakly Separable Models Without Monotonicity," Papers 2003.04337, arXiv.org, revised Apr 2020.
    16. Myrna, Olena, 2023. "Competition in online land lease auctions in Ukraine: Reduced-form estimation," Land Use Policy, Elsevier, vol. 125(C).
    17. 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.
    18. 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.
    19. Geneviève Vallée, 2018. "How Long Does It Take You to Pay? A Duration Study of Canadian Retail Transaction Payment Times," Staff Working Papers 18-46, Bank of Canada.
    20. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers CWP55/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Laurent Delsol & Ingrid Van Keilegom, 2020. "Semiparametric M-estimation with non-smooth criterion functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(2), pages 577-605, April.

  10. Juan Carlos Escanciano & Lin Zhu, 2015. "A Simple Data-Driven Estimator for the Semiparametric Sample Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 734-762, December.

    Cited by:

    1. Juan Carlos Escanciano, 2020. "Uniform Rates for Kernel Estimators of Weakly Dependent Data," Papers 2005.09951, arXiv.org.
    2. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.

  11. 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.

    Cited by:

    1. Kilani Ghoudi & Bruno Rémillard, 2018. "Serial independence tests for innovations of conditional mean and variance models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 3-26, March.

  12. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.

    Cited by:

    1. Jorge E. Galán, 2020. "The benefits are at the tail: uncovering the impact of macroprudential policy on growth-at-risk," Working Papers 2007, Banco de España.
    2. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    3. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    4. Christoph Breunig, 2016. "Specification Testing in Nonparametric Instrumental Quantile Regression," SFB 649 Discussion Papers SFB649DP2016-032, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    6. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    7. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
    8. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    9. Jorge E. Galán & María Rodríguez Moreno, 2020. "At-risk measures and financial stability," Revista de Estabilidad Financiera, Banco de España, issue NOV.
    10. Russell Davidson & Victoria Zinde-Walsh, 2017. "Advances in specification testing," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1595-1631, December.
    11. Gijbels, Irène & Omelka, Marek & Veraverbeke, Noël, 2021. "Omnibus test for covariate effects in conditional copula models," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    12. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    13. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    14. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    15. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  13. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    See citations under working paper version above.
  14. Miguel A. Delgado & Juan Carlos Escanciano, 2013. "Conditional Stochastic Dominance Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 16-28, January.
    See citations under working paper version above.
  15. Juan Carlos Escanciano & Ignacio N. Lobato & Lin Zhu, 2013. "Automatic Specification Testing for Vector Autoregressions and Multivariate Nonlinear Time Series Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 426-437, October.

    Cited by:

    1. Pedro H. C. Sant’Anna, 2017. "Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 349-358, July.
    2. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    3. Grivas, Charisios, 2021. "An Automatic Portmanteau Test For Nonlinear Dependence," MPRA Paper 114312, University Library of Munich, Germany, revised 22 Aug 2022.
    4. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    5. Li, Muyi & Zhang, Yanfen, 2022. "Bootstrapping multivariate portmanteau tests for vector autoregressive models with weak assumptions on errors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    6. Cheng Yu & Dong Li & Feiyu Jiang & Ke Zhu, 2023. "Matrix GARCH Model: Inference and Application," Papers 2306.05169, arXiv.org.

  16. Delgado, Miguel A. & Escanciano, Juan Carlos, 2012. "Distribution-free tests of stochastic monotonicity," Journal of Econometrics, Elsevier, vol. 170(1), pages 68-75.

    Cited by:

    1. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    2. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    3. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers CWP09/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Hoderlein, Stefan & Su, Liangjun & White, Halbert & Yang, Thomas Tao, 2016. "Testing for monotonicity in unobservables under unconfoundedness," Journal of Econometrics, Elsevier, vol. 193(1), pages 183-202.
    5. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.
    6. Gutknecht, Daniel, 2016. "Testing for monotonicity under endogeneity," Journal of Econometrics, Elsevier, vol. 190(1), pages 100-114.
    7. Denis Chetverikov & Daniel Wilhelm, 2016. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers 48/16, Institute for Fiscal Studies.
    8. Denis Chetverikov & Daniel Wilhelm, 2017. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers CWP14/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    10. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    11. José Romeo & Nelson Tanaka & Antonio Pedroso-de-Lima & Victor Salinas-Torres, 2013. "Large sample properties for a class of copulas in bivariate survival analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(8), pages 997-1015, November.
    12. Lee, Sokbae & Song, Kyungchul & Whang, Yoon-Jae, 2013. "Testing functional inequalities," Journal of Econometrics, Elsevier, vol. 172(1), pages 14-32.
    13. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    14. Liu, Nianqing & Vuong, Quang & Xu, Haiqing, 2017. "Rationalization and identification of binary games with correlated types," Journal of Econometrics, Elsevier, vol. 201(2), pages 249-268.
    15. Daniel Gutknecht, 2013. "Testing for Monotonicity under Endogeneity An Application to the Reservation Wage Function," Economics Series Working Papers 673, University of Oxford, Department of Economics.
    16. Miguel A. Delgado & Juan Carlos Escanciano, 2013. "Conditional Stochastic Dominance Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 16-28, January.
    17. Berghaus, Betina & Bücher, Axel, 2014. "Nonparametric tests for tail monotonicity," Journal of Econometrics, Elsevier, vol. 180(2), pages 117-126.
    18. Sokbae (Simon) Lee & Kyungchui (Kevin) Song & Yoon-Jae Whang, 2014. "Testing for a general class of functional inequalities," CeMMAP working papers 09/14, Institute for Fiscal Studies.
    19. Denis Chetverikov & Daniel Wilhelm, 2015. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers 39/15, Institute for Fiscal Studies.
    20. Gutknecht, Daniel, 2012. "Do Reservation Wages Decline Monotonically? A Novel Statistical Test," Economic Research Papers 270635, University of Warwick - Department of Economics.
    21. Colubi, Ana & Domínguez-Menchero, J. Santos & González-Rodríguez, Gil, 2014. "Testing constancy in monotone response models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 45-56.
    22. Toru Kitagawa & Martin Nybom & Jan Stuhler, 2018. "Measurement error and rank correlations," CeMMAP working papers CWP28/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Seo, Juwon, 2018. "Tests of stochastic monotonicity with improved power," Journal of Econometrics, Elsevier, vol. 207(1), pages 53-70.

  17. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    See citations under working paper version above.
  18. J. Carlos Escanciano & Jose Olmo, 2011. "Robust Backtesting Tests for Value-at-risk Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 132-161, Winter.

    Cited by:

    1. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    2. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    3. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    4. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    5. Igor L. Kheifets, 2015. "Specification tests for nonlinear dynamic models," Econometrics Journal, Royal Economic Society, vol. 18(1), pages 67-94, February.
    6. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    7. Sander Barendse & Erik Kole & Dick van Dijk, 2023. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 528-568.
    8. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    9. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    10. Olivier de Bandt & Jean-Cyprien Héam & Claire Labonne & Santiago Tavolaro, 2015. "La mesure du risque systémique après la crise financière," Revue économique, Presses de Sciences-Po, vol. 66(3), pages 481-500.
    11. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    12. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    13. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, January.
    14. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    15. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    16. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    17. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    18. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    19. Zulu, Thulani & Manguzvane, Mathias Mandla & Bonga-Bonga, Lumengo, 2023. "Assessing the contribution of South African Insurance Firms to Systemic Risk," MPRA Paper 116815, University Library of Munich, Germany.
    20. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    21. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    22. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    23. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    24. Bogdan Wlodarczyk, 2017. "Zmiennosc cen na globalnym rynku surowcow a ryzyko banku," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 15(66), pages 107-124.

  19. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    See citations under working paper version above.
  20. Escanciano, Juan Carlos & Song, Kyungchul, 2010. "Testing single-index restrictions with a focus on average derivatives," Journal of Econometrics, Elsevier, vol. 156(2), pages 377-391, June.

    Cited by:

    1. Florios, Kostas, 2018. "A hyperplanes intersection simulated annealing algorithm for maximum score estimation," Econometrics and Statistics, Elsevier, vol. 8(C), pages 37-55.
    2. Escanciano, Juan Carlos & Jacho-Chávez, David T. & Lewbel, Arthur, 2014. "Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 426-443.
    3. Strzalkowska-Kominiak, Ewa & Cao, Ricardo, 2013. "Maximum likelihood estimation for conditional distribution single-index models under censoring," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 74-98.
    4. Xu, Kai & Zhou, Yeqing, 2021. "Projection-averaging-based cumulative covariance and its use in goodness-of-fit testing for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    5. Kyungchul Song, 2009. "Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling," PIER Working Paper Archive 09-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Kyungchul Song, 2009. "Bootstrapping Semiparametric Models with Single-Index Nuisance Parameters, Second Version," PIER Working Paper Archive 10-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 02 Aug 2010.
    7. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.
    8. Ewa Strzalkowska-Kominiak & Ricardo Cao, 2014. "Beran-based approach for single-index models under censoring," Computational Statistics, Springer, vol. 29(5), pages 1243-1261, October.
    9. Kyungchul Song, 2009. "Two-Step Extremum Estimation with Estimated Single-Indices," PIER Working Paper Archive 09-012, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  21. Escanciano, Juan Carlos & Mayoral, Silvia, 2010. "Data-driven smooth tests for the martingale difference hypothesis," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1983-1998, August.
    See citations under working paper version above.
  22. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.

    Cited by:

    1. Jiménez-Gamero, M.D. & Alba-Fernández, M.V. & Jodrá, P. & Barranco-Chamorro, I., 2015. "An approximation to the null distribution of a class of Cramér–von Mises statistics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 118(C), pages 258-272.
    2. Vortelinos, Dimitrios I., 2014. "Non-parametric analysis of equity arbitrage," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 199-216.
    3. Chu, Ba & Jacho-Chávez, David T., 2012. "k-NEAREST NEIGHBOR ESTIMATION OF INVERSE-DENSITY-WEIGHTED EXPECTATIONS WITH DEPENDENT DATA," Econometric Theory, Cambridge University Press, vol. 28(4), pages 769-803, August.
    4. Liangjun Su & Stefan Hoderlein & Halbert White, 2013. "Testing Monotonicity in Unobservables with Panel Data," Boston College Working Papers in Economics 892, Boston College Department of Economics, revised 01 Feb 2016.
    5. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.

  23. Escanciano, J. Carlos, 2010. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," Econometric Theory, Cambridge University Press, vol. 26(3), pages 744-773, June.
    See citations under working paper version above.
  24. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    See citations under working paper version above.
  25. Escanciano, J. Carlos, 2009. "On The Lack Of Power Of Omnibus Specification Tests," Econometric Theory, Cambridge University Press, vol. 25(1), pages 162-194, February.

    Cited by:

    1. Pedro H. C. Sant’Anna, 2017. "Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 349-358, July.
    2. Dennis Kristensen, 2010. "Semi-Nonparametric Estimation and Misspecification Testing of Diffusion Models," CREATES Research Papers 2010-43, Department of Economics and Business Economics, Aarhus University.
    3. Amengual, Dante & Carrasco, Marine & Sentana, Enrique, 2020. "Testing distributional assumptions using a continuum of moments," Journal of Econometrics, Elsevier, vol. 218(2), pages 655-689.
    4. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    5. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    6. Simos G. Meintanis & Bojana Milošević & Marko Obradović, 2020. "Goodness-of-fit tests in conditional duration models," Statistical Papers, Springer, vol. 61(1), pages 123-140, February.
    7. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    8. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    9. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    10. Sangyeol Lee & Simos G. Meintanis & Minyoung Jo, 2019. "Inferential procedures based on the integrated empirical characteristic function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 357-386, September.
    11. Gijbels, Irène & Omelka, Marek & Veraverbeke, Noël, 2021. "Omnibus test for covariate effects in conditional copula models," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    12. Hsu, Shih-Hsun & Kuan, Chung-Ming, 2014. "Constructing smooth tests without estimating the eigenpairs of the limiting process," Journal of Econometrics, Elsevier, vol. 178(P1), pages 71-79.
    13. Zu, Yang & Boswijk, H. Peter, 2017. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Journal of Empirical Finance, Elsevier, vol. 41(C), pages 53-75.
    14. Zu, Y., 2015. "Consistent nonparametric specification tests for stochastic volatility models based on the return distribution," Working Papers 15/02, Department of Economics, City University London.
    15. Feiyu Jiang & Emmanuel Selorm Tsyawo, 2022. "A Consistent ICM-based $\chi^2$ Specification Test," Papers 2208.13370, arXiv.org.
    16. Escanciano, Juan Carlos & Song, Kyungchul, 2010. "Testing single-index restrictions with a focus on average derivatives," Journal of Econometrics, Elsevier, vol. 156(2), pages 377-391, June.
    17. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.

  26. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.

    Cited by:

    1. Dominique Guégan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Documents de travail du Centre d'Economie de la Sorbonne 18014, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Ozkan, Oktay, 2021. "Impact of COVID-19 on stock market efficiency: Evidence from developed countries," Research in International Business and Finance, Elsevier, vol. 58(C).
    3. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2011. "Small sample properties of alternative tests for martingale difference hypothesis," Economics Letters, Elsevier, vol. 110(2), pages 151-154, February.
    4. Mengya Liu & Fukan Zhu & Ke Zhu, 2020. "Multi-frequency-band tests for white noise under heteroskedasticity," Papers 2004.09161, arXiv.org.
    5. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    6. Teresa Ledwina & Grzegorz Wyłupek, 2012. "Nonparametric tests for stochastic ordering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 730-756, December.
    7. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    8. Lyócsa, Štefan & Molnár, Peter, 2020. "Stock market oscillations during the corona crash: The role of fear and uncertainty," Finance Research Letters, Elsevier, vol. 36(C).
    9. Omid Sabbaghi & Navid Sabbaghi, 2017. "The Chicago Climate Exchange and market efficiency: an empirical analysis," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(4), pages 711-734, October.
    10. Claude Diebolt & Mohamed Chikhi, 2021. "Testing The Weak Form Efficiency Of The French Etf Market With Lstar-Anlstgarch Approach Using A Semiparametric Estimation," Working Papers 09-21, Association Française de Cliométrie (AFC).
    11. Ke Zhu, 2016. "Bootstrapping the portmanteau tests in weak auto-regressive moving average models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 463-485, March.
    12. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    13. Shao, Xiaofeng, 2011. "A bootstrap-assisted spectral test of white noise under unknown dependence," Journal of Econometrics, Elsevier, vol. 162(2), pages 213-224, June.
    14. Amélie Charles & Olivier Darné & Jae H Kim, 2017. "Adaptive Markets Hypothesis for Islamic Stock Portfolios: Evidence from Dow Jones Size and Sector-Indices," Post-Print hal-01526483, HAL.
    15. Amélie Charles & Olivier Darné, 2012. "Trends and random walks in macroeconomic time series: A reappraisal," Post-Print hal-00956937, HAL.
    16. Jentsch, Carsten & Subba Rao, Suhasini, 2015. "A test for second order stationarity of a multivariate time series," Journal of Econometrics, Elsevier, vol. 185(1), pages 124-161.
    17. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    18. Li, Linyuan & Duchesne, Pierre & Liou, Chu Pheuil, 2021. "On diagnostic checking in ARMA models with conditionally heteroscedastic martingale difference using wavelet methods," Econometrics and Statistics, Elsevier, vol. 19(C), pages 169-187.
    19. Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
    20. Amélie Charles & Olivier Darné & Jae H. Kim, 2010. "Exchange-Rate Return Predictability and the Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates," Working Papers hal-00547722, HAL.
    21. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    22. Gourieroux, Christian & Jasiak, Joann, 2019. "Robust analysis of the martingale hypothesis," Econometrics and Statistics, Elsevier, vol. 9(C), pages 17-41.
    23. Noda, Akihiko, 2016. "A test of the adaptive market hypothesis using a time-varying AR model in Japan," Finance Research Letters, Elsevier, vol. 17(C), pages 66-71.
    24. Pedro H. C. Sant’Anna, 2017. "Testing for Uncorrelated Residuals in Dynamic Count Models With an Application to Corporate Bankruptcy," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 349-358, July.
    25. Adrian Wai‐Kong Cheung & Jen‐Je Su & Astrophel Kim Choo, 2012. "Are exchange rates serially correlated? New evidence from the Euro FX markets," Review of Financial Economics, John Wiley & Sons, vol. 21(1), pages 14-20, January.
    26. Zacharias Psaradakis & Marián Vávra, 2015. "Portmanteau Tests for Linearity of Stationary Time Series," Birkbeck Working Papers in Economics and Finance 1514, Birkbeck, Department of Economics, Mathematics & Statistics.
    27. Ali Almail & Fahad Almudhaf, 2017. "Adaptive Market Hypothesis: Evidence from three centuries of UK data," Economics and Business Letters, Oviedo University Press, vol. 6(2), pages 48-53.
    28. Julia S. Mehlitz & Benjamin R. Auer, 2021. "Time‐varying dynamics of expected shortfall in commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 895-925, June.
    29. Stefano Martinazzi & Daniele Regoli & Andrea Flori, 2020. "A Tale of Two Layers: The Mutual Relationship between Bitcoin and Lightning Network," Risks, MDPI, vol. 8(4), pages 1-18, December.
    30. Verheyden, Tim & De Moor, Lieven & Van den Bossche, Filip, 2015. "Towards a new framework on efficient markets," Research in International Business and Finance, Elsevier, vol. 34(C), pages 294-308.
    31. Amélie Charles & Olivier Darné & Jae H Kim, 2017. "Adaptive markets hypothesis for Islamic stock indices: Evidence from Dow Jones size and sector-indices," Post-Print hal-01579718, HAL.
    32. Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    33. Kräussl, Roman & Tugnetti, Alessandro, 2023. "Non-fungible tokens (NFTs): A review of pricing determinants, applications and opportunities," CFS Working Paper Series 693, Center for Financial Studies (CFS).
    34. Marius Cristian Frunza & Dominique Guégan, 2018. "Is the Bitcoin Rush Over?," Working Papers 2018:10, Department of Economics, University of Venice "Ca' Foscari".
    35. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    36. Peter C.B. Phillips & Sainan Jin, 2013. "Testing the Martingale Hypothesis," Cowles Foundation Discussion Papers 1912, Cowles Foundation for Research in Economics, Yale University.
    37. Kuck, Konstantin & Maderitsch, Robert, 2019. "Intra-day dynamics of exchange rates: New evidence from quantile regression," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 247-257.
    38. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.
    39. Horpestad, Jone B. & Lyócsa, Štefan & Molnár, Peter & Olsen, Torbjørn B., 2019. "Asymmetric volatility in equity markets around the world," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 540-554.
    40. Lyócsa, Štefan & Molnár, Peter, 2018. "Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds," Energy, Elsevier, vol. 155(C), pages 462-473.
    41. Mirzaee Ghazani, Majid & Khalili Araghi, Mansour, 2014. "Evaluation of the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the Tehran stock exchange," Research in International Business and Finance, Elsevier, vol. 32(C), pages 50-59.
    42. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.
    43. David Harris & Hsein Kew, 2014. "Portmanteau Autocorrelation Tests Under Q-Dependence And Heteroskedasticity," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 203-217, May.
    44. Lyócsa, Štefan & Plíhal, Tomáš, 2022. "Russia’s ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Finance Research Letters, Elsevier, vol. 48(C).
    45. Alfredo García-Hiernaux, 2009. "Diagnostic checking using subspace methods," Documentos de Trabajo del ICAE 2009-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    46. Dowling, Michael, 2022. "Fertile LAND: Pricing non-fungible tokens," Finance Research Letters, Elsevier, vol. 44(C).
    47. Juan Carlos Escanciano & Silvia Mayoral, 2007. "Data-Driven Smooth Tests for the Martingale Difference Hypothesis," Faculty Working Papers 01/07, School of Economics and Business Administration, University of Navarra.
    48. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2022. "Data-driven portmanteau tests for time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 675-698, September.
    49. Zhu, Ke & Li, Wai Keung, 2015. "A bootstrapped spectral test for adequacy in weak ARMA models," Journal of Econometrics, Elsevier, vol. 187(1), pages 113-130.
    50. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    51. Zhu, Hegui & Ge, Jiangxia & Qi, Wentao & Zhang, Xiangde & Lu, Xiaoxiong, 2022. "Dynamic analysis and image encryption application of a sinusoidal-polynomial composite chaotic system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 198(C), pages 188-210.
    52. Karasiński Jacek, 2023. "The adaptive market hypothesis and the return predictability in the cryptocurrency markets," Economics and Business Review, Sciendo, vol. 9(1), pages 94-118, April.
    53. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    54. Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš & Molnár, Peter, 2020. "Fear of the coronavirus and the stock markets," Finance Research Letters, Elsevier, vol. 36(C).
    55. Hill, Jonathan B. & Motegi, Kaiji, 2019. "Testing the white noise hypothesis of stock returns," Economic Modelling, Elsevier, vol. 76(C), pages 231-242.
    56. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    57. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    58. Feiyu Jiang & Dong Li & Ke Zhu, 2019. "Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model," Papers 1907.04147, arXiv.org, revised Oct 2020.
    59. Xiao, Han & Wu, Wei Biao, 2019. "Portmanteau Test and Simultaneous Inference for Serial Covariances," IRTG 1792 Discussion Papers 2019-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    60. Kenichi Hirayama & Akihiko Noda, 2019. "Measuring the Time-Varying Market Efficiency in the Prewar Japanese Stock Market, 1924-1943," Papers 1911.04059, arXiv.org, revised Dec 2022.
    61. Lyócsa, Štefan & Baumöhl, Eduard & Výrost, Tomáš, 2022. "YOLO trading: Riding with the herd during the GameStop episode," Finance Research Letters, Elsevier, vol. 46(PA).
    62. Štefan Lyócsa & Roman Horváth, 2018. "Stock Market Contagion: a New Approach," Open Economies Review, Springer, vol. 29(3), pages 547-577, July.
    63. Guay, Alain & Guerre, Emmanuel & Lazarová, Štěpána, 2013. "Robust adaptive rate-optimal testing for the white noise hypothesis," Journal of Econometrics, Elsevier, vol. 176(2), pages 134-145.
    64. Eckhard Platen & Renata Rendek, 2019. "Dynamics of a Well-Diversified Equity Index," Research Paper Series 398, Quantitative Finance Research Centre, University of Technology, Sydney.
    65. Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
    66. Zaichao Du & Juan Carlos Escanciano & Guangwei Zhu, 2017. "Automatic Portmanteau Tests with Applications to Market Risk Management," CAEPR Working Papers 2017-002, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    67. Amélie Charles & Olivier Darné & Jae H. Kim, 2015. "Will precious metals shine ? A market efficiency perspective," Post-Print hal-01238706, HAL.
    68. Dominique Guegan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Post-Print halshs-01822992, HAL.
    69. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada," UC3M Working papers. Economics 27484, Universidad Carlos III de Madrid. Departamento de Economía.
    70. Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
    71. Joseph P. Romano & Marius A. Tirlea, 2022. "Permutation testing for dependence in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 781-807, September.
    72. Amélie Charles & Olivier Darné & Jae H. Kim, 2014. "Precious metals shine? A market efficiency perspective," Working Papers hal-01010516, HAL.
    73. Characiejus, Vaidotas & Rice, Gregory, 2020. "A general white noise test based on kernel lag-window estimates of the spectral density operator," Econometrics and Statistics, Elsevier, vol. 13(C), pages 175-196.
    74. Lyócsa, Štefan & Molnár, Peter & Plíhal, Tomáš, 2019. "Central bank announcements and realized volatility of stock markets in G7 countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 58(C), pages 117-135.
    75. Do, Linh Phuong Catherine & Lyócsa, Štefan & Molnár, Peter, 2021. "Residual electricity demand: An empirical investigation," Applied Energy, Elsevier, vol. 283(C).
    76. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    77. Biswabhusan Bhuyan & Subhamitra Patra & Ranjan Kumar Bhuian, 2020. "Market Adaptability and Evolving Predictability of Stock Returns: An Evidence from India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(4), pages 605-619, December.
    78. Dominique Guegan & Marius Cristian Frunza, 2018. "Is the Bitcoin Rush Over?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01822992, HAL.
    79. Hsu, Shih-Hsun & Kuan, Chung-Ming, 2014. "Constructing smooth tests without estimating the eigenpairs of the limiting process," Journal of Econometrics, Elsevier, vol. 178(P1), pages 71-79.
    80. Andrew Harvey & Stephen Thiele, 2014. "Testing against Changing Correlation," Cambridge Working Papers in Economics 1439, Faculty of Economics, University of Cambridge.
    81. Anselmi, Giulio & Petrella, Giovanni, 2023. "Non-fungible token artworks: More crypto than art?," Finance Research Letters, Elsevier, vol. 51(C).
    82. Pınar Evrim Mandacı & F. Dilvin Taskın & Zeliha Can Ergun, 2019. "Adaptive Market Hypothesis," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 84-101.
    83. Chevapatrakul, Thanaset & Mascia, Danilo V., 2019. "Detecting overreaction in the Bitcoin market: A quantile autoregression approach," Finance Research Letters, Elsevier, vol. 30(C), pages 371-377.
    84. Sashikanta Khuntia & J. K. Pattanayak, 2020. "Evolving Efficiency of Exchange Rate Movement: An Evidence from Indian Foreign Exchange Market," Global Business Review, International Management Institute, vol. 21(4), pages 956-969, August.
    85. Maderitsch, R., 2015. "Information transmission between stock markets in Hong Kong, Europe and the US: New evidence on time- and state-dependence," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 13-36.
    86. Ayala Astrid & Blazsek Szabolcs & Escribano Alvaro, 2023. "Anticipating extreme losses using score-driven shape filters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 449-484, September.
    87. Marian Vavra, 2012. "Robustness of Power Properties of Non-linearity Tests," Birkbeck Working Papers in Economics and Finance 1205, Birkbeck, Department of Economics, Mathematics & Statistics.
    88. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
    89. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2017. "Dynamic conditional score models with time-varying location, scale and shape parameters," UC3M Working papers. Economics 25043, Universidad Carlos III de Madrid. Departamento de Economía.
    90. Nasri, Bouchra R., 2022. "Tests of serial dependence for multivariate time series with arbitrary distributions," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    91. Georgios Bampinas & Panagiotis Konstantinou & Theodore Panagiotidis, 2021. "Reassessing the inflation uncertainty‐inflation relationship in the tails," Bulletin of Economic Research, Wiley Blackwell, vol. 73(4), pages 508-534, October.
    92. Lyócsa, Štefan & Molnár, Peter, 2017. "The effect of non-trading days on volatility forecasts in equity markets," Finance Research Letters, Elsevier, vol. 23(C), pages 39-49.
    93. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    94. Gençay, Ramazan & Signori, Daniele, 2015. "Multi-scale tests for serial correlation," Journal of Econometrics, Elsevier, vol. 184(1), pages 62-80.
    95. Roy, Archi & Soni, Anchal & Deb, Soudeep, 2023. "A wavelet-based methodology to compare the impact of pandemic versus Russia–Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets," Energy Economics, Elsevier, vol. 124(C).
    96. Todea, Alexandru & Pleşoianu, Anita, 2013. "The influence of foreign portfolio investment on informational efficiency: Empirical evidence from Central and Eastern European stock markets," Economic Modelling, Elsevier, vol. 33(C), pages 34-41.
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    98. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
    99. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    100. Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš, 2021. "Stock market volatility forecasting: Do we need high-frequency data?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1092-1110.
    101. v{S}tefan Ly'ocsa & Tom'av{s} Pl'ihal, 2022. "Russia's Ruble during the onset of the Russian invasion of Ukraine in early 2022: The role of implied volatility and attention," Papers 2205.09179, arXiv.org.
    102. Kian-Ping Lim & Weiwei Luo & Jae H. Kim, 2013. "Are US stock index returns predictable? Evidence from automatic autocorrelation-based tests," Applied Economics, Taylor & Francis Journals, vol. 45(8), pages 953-962, March.
    103. Geoffrey M. Ngene & Catherine Anitha Manohar & Ivan F. Julio, 2020. "Overreaction in the REITs Market: New Evidence from Quantile Autoregression Approach," JRFM, MDPI, vol. 13(11), pages 1-28, November.
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    105. Chang, Jinyuan & Yao, Qiwei & Zhou, Wen, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," LSE Research Online Documents on Economics 68531, London School of Economics and Political Science, LSE Library.
    106. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    107. Ayala, Astrid & Blazsek, Szabolcs & Escribano, Álvaro, 2019. "Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk," UC3M Working papers. Economics 28638, Universidad Carlos III de Madrid. Departamento de Economía.
    108. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  27. Escanciano, Juan Carlos, 2009. "Quasi-Maximum Likelihood Estimation Of Semi-Strong Garch Models," Econometric Theory, Cambridge University Press, vol. 25(2), pages 561-570, April.

    Cited by:

    1. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.
    2. Heejoon Han & Dennis Kristensen, 2012. "Asymptotic Theory for the QMLE in GARCH-X Models with Stationary and Non-Stationary Covariates," CREATES Research Papers 2012-25, Department of Economics and Business Economics, Aarhus University.
    3. Mika Meitz & Pentti Saikkonen, 2008. "Parameter estimation in nonlinear AR-GARCH models," Economics Series Working Papers 396, University of Oxford, Department of Economics.
    4. Todd, Prono, 2009. "Simple, Skewness-Based GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 30994, University Library of Munich, Germany, revised 30 Jul 2011.
    5. Christan Francq & Jean-Michel Zakoian, 2012. "Optimal Predictions of Powers of Conditionally Heteroskedastic Processes," Working Papers 2012-17, Center for Research in Economics and Statistics.
    6. Cerovecki, Clément & Francq, Christian & Hörmann, Siegfried & Zakoïan, Jean-Michel, 2019. "Functional GARCH models: The quasi-likelihood approach and its applications," Journal of Econometrics, Elsevier, vol. 209(2), pages 353-375.
    7. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    8. Sucarrat, Genaro, 2020. "garchx: Flexible and Robust GARCH-X Modelling," MPRA Paper 100301, University Library of Munich, Germany.
    9. M. Jiménez Gamero, 2014. "On the empirical characteristic function process of the residuals in GARCH models and applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 409-432, June.
    10. Todd, Prono, 2010. "Simple GMM Estimation of the Semi-Strong GARCH(1,1) Model," MPRA Paper 20034, University Library of Munich, Germany.
    11. Conrad, Christian & Schienle, Melanie, 2019. "Testing for an omitted multiplicative long-term component in GARCH models," Working Paper Series in Economics 121, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    12. Francq, Christian & Thieu, Le Quyen, 2015. "Qml inference for volatility models with covariates," MPRA Paper 63198, University Library of Munich, Germany.
    13. Christian Francq & Jean-Michel Zakoïan, 2016. "Estimating multivariate volatility models equation by equation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 613-635, June.
    14. Tingguo Zheng & Han Xiao & Rong Chen, 2021. "Generalized Autoregressive Moving Average Models with GARCH Errors," Papers 2105.05532, arXiv.org.
    15. Abdelouahab Bibi, 2021. "Asymptotic properties of QMLE for seasonal threshold GARCH model with periodic coefficients," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 477-514, June.
    16. Bibi, Abdelouahab & Ghezal, Ahmed, 2017. "Asymptotic properties of QMLE for periodic asymmetric strong and semi-strong GARCH models," MPRA Paper 81126, University Library of Munich, Germany.

  28. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March.

    Cited by:

    1. Chen, Min & Zhu, Ke, 2013. "Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations," MPRA Paper 50487, University Library of Munich, Germany.
    2. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    3. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    4. Christensen, Bent Jesper & Dahl, Christian M. & Iglesias, Emma M., 2012. "Semiparametric inference in a GARCH-in-mean model," Journal of Econometrics, Elsevier, vol. 167(2), pages 458-472.
    5. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    6. Giuseppe Cavaliere & Indeewara Perera & Anders Rahbek, 2021. "Specification tests for GARCH processes," Discussion Papers 21-06, University of Copenhagen. Department of Economics.
    7. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    8. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
    9. Leucht, Anne & Neumann, Michael H. & Kreiss, Jens-Peter, 2013. "A model specification test for GARCH(1,1) processes," Working Papers 13-11, University of Mannheim, Department of Economics.
    10. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    11. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    12. Li, Dong & Ling, Shiqing & Zhu, Ke, 2016. "ZD-GARCH model: a new way to study heteroscedasticity," MPRA Paper 68621, University Library of Munich, Germany.
    13. Carlos Velasco & Xuexin Wang, 2015. "A Joint Portmanteau Test For Conditional Mean And Variance Time-Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 39-60, January.
    14. Li, Dong & Zhang, Xingfa & Zhu, Ke & Ling, Shiqing, 2018. "The ZD-GARCH model: A new way to study heteroscedasticity," Journal of Econometrics, Elsevier, vol. 202(1), pages 1-17.
    15. Anne Leucht & Jens-Peter Kreiss & Michael H. Neumann, 2015. "A Model Specification Test For GARCH(1,1) Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1167-1193, December.
    16. M. Dolores Jiménez-Gamero & Sangyeol Lee & Simos G. Meintanis, 2020. "Goodness-of-fit tests for parametric specifications of conditionally heteroscedastic models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 682-703, September.

  29. Escanciano, J. Carlos, 2007. "Weak convergence of non-stationary multivariate marked processes with applications to martingale testing," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1321-1336, August.

    Cited by:

    1. Wang, Qiying & Wu, Dongsheng & Zhu, Ke, 2018. "Model checks for nonlinear cointegrating regression," Journal of Econometrics, Elsevier, vol. 207(2), pages 261-284.
    2. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Ferger, Dietmar, 2009. "Argmax-stable marked empirical processes," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1203-1206, May.
    4. Zaichao Du, 2016. "Nonparametric bootstrap tests for independence of generalized errors," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 55-83, February.
    5. Yoichi Nishiyama, 2009. "Goodness‐of‐fit test for a nonlinear time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 674-681, November.
    6. Peter C.B. Phillips & Sainan Jin, 2013. "Testing the Martingale Hypothesis," Cowles Foundation Discussion Papers 1912, Cowles Foundation for Research in Economics, Yale University.
    7. 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.
    8. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    9. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
    10. Alejandra Cabaña & Enrique M. Cabaña & Marco Scavino, 2012. "Weak Convergence of Marked Empirical Processes for Focused Inference on AR(p) vs AR(p + 1) Stationary Time Series," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 793-810, September.
    11. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.

  30. Delgado, Miguel A. & Carlos Escanciano, J., 2007. "Nonparametric tests for conditional symmetry in dynamic models," Journal of Econometrics, Elsevier, vol. 141(2), pages 652-682, December.

    Cited by:

    1. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    2. Tao Chen & Gautam Tripathi, 2013. "Testing conditional symmetry without smoothing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 273-313, June.
    3. Philippe Lambert & Sébastien Laurent, 2008. "Testing Conditional Dynamics in Asymmetry. A Residual-Based Approach," Working Papers ECARES 2008_009, ULB -- Universite Libre de Bruxelles.
    4. J. Carlos Escanciano & Carlos Velasco, 2010. "Specification tests of parametric dynamic conditional quantiles," Post-Print hal-00732534, HAL.
    5. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," CAEPR Working Papers 2012-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    6. 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.
    7. Zacharias Psaradakis & Marián Vávra, 2015. "A Quantile-based Test for Symmetry of Weakly Dependent Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(4), pages 587-598, July.
    8. Delgado, Miguel A. & Song, Xiaojun, 2018. "Nonparametric tests for conditional symmetry," Journal of Econometrics, Elsevier, vol. 206(2), pages 447-471.
    9. Martínez Ibáñez, Oscar & Olmo, José, 2008. "A nonlinear threshold model for the dependence of extremes of stationary sequences," Working Papers 2072/5361, Universitat Rovira i Virgili, Department of Economics.
    10. Masayuki Hirukawa & Mari Sakudo, 2016. "Testing Symmetry of Unknown Densities via Smoothing with the Generalized Gamma Kernels," Econometrics, MDPI, vol. 4(2), pages 1-27, June.
    11. J. Carlos Escanciano, 2009. "Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models," CAEPR Working Papers 2009-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    12. Francq, Christian & Jiménez Gamero, Maria Dolores & Meintanis, Simos, 2015. "Tests for sphericity in multivariate garch models," MPRA Paper 67411, University Library of Munich, Germany.
    13. Tao Chen & Gautam Tripathi, 2014. "A simple consistent test of conditional symmetry in symmetrically trimmed tobit models," DEM Discussion Paper Series 14-04, Department of Economics at the University of Luxembourg.
    14. Igor Kheifets & Carlos Velasco, 2013. "New Goodness-of-fit Diagnostics for Conditional Discrete Response Models," Cowles Foundation Discussion Papers 1924, Cowles Foundation for Research in Economics, Yale University.
    15. Lambert, Philippe & Laurent, Sébastien & Veredas, David, 2012. "Testing conditional asymmetry: A residual-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1229-1247.
    16. Niu, Cuizhen & Guo, Xu & Li, Yong & Zhu, Lixing, 2018. "Pairwise distance-based tests for conditional symmetry," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 145-162.
    17. Simos Meintanis, 2013. "Comments on: An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 432-436, September.
    18. Cristina Danciulescu, 2010. "Backtesting Value-at-Risk Models: A Multivariate Approach," CAEPR Working Papers 2010-004, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    19. Marcelo Fernandes & Eduardo Mendes & Olivier Scaillet, 2015. "Testing for symmetry and conditional symmetry using asymmetric kernels," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 649-671, August.
    20. Joseph Ngatchou-Wandji & Michel Harel, 2013. "A Cramér-von Mises test for symmetry of the error distribution in asymptotically stationary stochastic models," Statistical Inference for Stochastic Processes, Springer, vol. 16(3), pages 207-236, October.
    21. Xiaojun Song & Haoyu Wei, 2021. "Nonparametric Tests of Conditional Independence for Time Series," Papers 2110.04847, arXiv.org.
    22. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.

  31. Escanciano, J. Carlos, 2006. "Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 531-541, June.
    See citations under working paper version above.
  32. Escanciano, J. Carlos, 2006. "A Consistent Diagnostic Test For Regression Models Using Projections," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1030-1051, December.
    See citations under working paper version above.
  33. Escanciano, J. Carlos & Velasco, Carlos, 2006. "Testing the martingale difference hypothesis using integrated regression functions," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2278-2294, December.
    See citations under working paper version above.
  34. Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
    See citations under working paper version above.

Software components

    Sorry, no citations of software components recorded.

Chapters

  1. J. Carlos Escanciano & Ignacio N. Lobato, 2009. "Testing the Martingale Hypothesis," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 20, pages 972-1003, Palgrave Macmillan.

    Cited by:

    1. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2011. "Small sample properties of alternative tests for martingale difference hypothesis," Economics Letters, Elsevier, vol. 110(2), pages 151-154, February.
    2. Cesar Rufino, 2013. "Random walks in the different sectoral submarkets of the Philippine Stock Exchange amid modernization," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 50(1), pages 57-82, June.
    3. Charles, Amélie & Darné, Olivier & Fouilloux, Jessica, 2011. "Testing the martingale difference hypothesis in CO2 emission allowances," Economic Modelling, Elsevier, vol. 28(1-2), pages 27-35, January.
    4. Lazăr, Dorina & Todea, Alexandru & Filip, Diana, 2012. "Martingale difference hypothesis and financial crisis: Empirical evidence from European emerging foreign exchange markets," Economic Systems, Elsevier, vol. 36(3), pages 338-350.
    5. Amélie Charles & Olivier Darné & Jae H. Kim, 2010. "Exchange-Rate Return Predictability and the Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates," Working Papers hal-00547722, HAL.
    6. Gourieroux, Christian & Jasiak, Joann, 2019. "Robust analysis of the martingale hypothesis," Econometrics and Statistics, Elsevier, vol. 9(C), pages 17-41.
    7. Kuck, Konstantin & Maderitsch, Robert, 2019. "Intra-day dynamics of exchange rates: New evidence from quantile regression," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 247-257.
    8. Dzung Phan Tran Trung & Hung Pham Quang, 2019. "Adaptive Market Hypothesis: Evidence from the Vietnamese Stock Market," JRFM, MDPI, vol. 12(2), pages 1-16, May.
    9. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    10. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    11. Afees A. Salisu & Taofeek O. Ayinde, 2018. "Testing for spillovers in naira exchange rates: The role of electioneering & global financial crisis," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 18(4), pages 341-348, December.
    12. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    13. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    14. Adeyeye Patrick Olufemi & Aluko Olufemi Adewale & Migiro Stephen Oseko, 2017. "Efficiency of Foreign Exchange Markets in Sub-Saharan Africa in the Presence of Structural Break: A Linear and Non-Linear Testing Approach," Journal of Economics and Behavioral Studies, AMH International, vol. 9(4), pages 122-131.
    15. Eva Regnier, 2018. "Probability Forecasts Made at Multiple Lead Times," Management Science, INFORMS, vol. 64(5), pages 2407-2426, May.
    16. Friedrich Geiecke & Mark Trede, 2010. "A Direct Test of Rational Bubbles," CQE Working Papers 1310, Center for Quantitative Economics (CQE), University of Muenster.
    17. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    18. Chang, Jinyuan & Jiang, Qing & Shao, Xiaofeng, 2023. "Testing the martingale difference hypothesis in high dimension," Journal of Econometrics, Elsevier, vol. 235(2), pages 972-1000.
    19. Afees A. Salisu & Taofeek O. Ayinde, 2016. "Testing the Martingale Difference Hypothesis (MDH) with Structural Breaks: Evidence from Foreign Exchanges of Nigeria and South Africa," Journal of African Business, Taylor & Francis Journals, vol. 17(3), pages 342-359, September.

Books

    Sorry, no citations of books recorded.
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