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kunpeng Li

Personal Details

First Name:Kunpeng
Middle Name:
Last Name:Li
Suffix:
RePEc Short-ID:pli545
Terminal Degree:2012 (from RePEc Genealogy)

Affiliation

Capital University of Economics and Business

Beijing, China
http://www.cueb.edu.cn/
RePEc:edi:cuebbcn (more details at EDIRC)

Research output

as
Jump to: Working papers

Working papers

  1. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
  2. Kunpeng Li & Degui Li & Zhongwen Lian & Cheng Hsiao, 2013. "Semiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors," Monash Econometrics and Business Statistics Working Papers 2/13, Monash University, Department of Econometrics and Business Statistics.
  3. Bai, Jushan & Li, Kunpeng, 2012. "Maximum likelihood estimation and inference for approximate factor models of high dimension," MPRA Paper 42099, University Library of Munich, Germany, revised 19 Oct 2012.
  4. Bai, Jushan & Li, Kunpeng, 2010. "Theory and methods of panel data models with interactive effects," MPRA Paper 43441, University Library of Munich, Germany, revised Dec 2012.

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. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.

    Cited by:

    1. Matteo Barigozzi & Matteo Luciani, 2024. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Finance and Economics Discussion Series 2024-086, Board of Governors of the Federal Reserve System (U.S.).
    2. Xiang, Jingjie & Li, Kunpeng & Cui, Guowei, 2018. "A note on the asymptotic properties of least squares estimation in high dimensional constrained factor models," Economics Letters, Elsevier, vol. 171(C), pages 144-148.

  2. Kunpeng Li & Degui Li & Zhongwen Lian & Cheng Hsiao, 2013. "Semiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors," Monash Econometrics and Business Statistics Working Papers 2/13, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Phillips, Peter C.B. & Li, Degui & Gao, Jiti, 2017. "Estimating smooth structural change in cointegration models," Journal of Econometrics, Elsevier, vol. 196(1), pages 180-195.

  3. Bai, Jushan & Li, Kunpeng, 2012. "Maximum likelihood estimation and inference for approximate factor models of high dimension," MPRA Paper 42099, University Library of Munich, Germany, revised 19 Oct 2012.

    Cited by:

    1. Matteo Barigozzi & Marc Hallin, 2024. "The Dynamic, the Static, and the Weak factor models and the analysis of high-dimensional time series," Papers 2407.10653, arXiv.org.
    2. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Jun 2024.
    3. Shaoxin Wang & Hu Yang & Chaoli Yao, 2019. "On the penalized maximum likelihood estimation of high-dimensional approximate factor model," Computational Statistics, Springer, vol. 34(2), pages 819-846, June.
    4. Luke Hartigan & Michelle Wright, 2021. "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers rdp2021-03, Reserve Bank of Australia.
    5. Linton, O. B. & Tang, H. & Wu, J., 2022. "A Structural Dynamic Factor Model for Daily Global Stock Market Returns," Cambridge Working Papers in Economics 2237, Faculty of Economics, University of Cambridge.
    6. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
    7. Matteo Barigozzi & Matteo Luciani, 2024. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Finance and Economics Discussion Series 2024-086, Board of Governors of the Federal Reserve System (U.S.).
    8. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    9. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
    10. Yi‐Chiuan Wang & Jyh‐Lin Wu, 2015. "Fundamentals and Exchange Rate Prediction Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(8), pages 1651-1671, December.
    11. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    12. Kunpeng Li & Qi Li & Lina Lu, 2018. "Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models," Supervisory Research and Analysis Working Papers RPA 18-2, Federal Reserve Bank of Boston.
    13. Matteo Luciani, 2020. "Common and Idiosyncratic Inflation," Finance and Economics Discussion Series 2020-024, Board of Governors of the Federal Reserve System (U.S.).
    14. Matteo Barigozzi & Marc Hallin & Matteo Luciani & Paolo Zaffaroni, 2021. "Inferential Theory for Generalized Dynamic Factor Models," Working Papers ECARES 2021-20, ULB -- Universite Libre de Bruxelles.
    15. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics Discussion Papers 2020-5, Kiel Institute for the World Economy (IfW Kiel).
    16. Luke Hartigan & Michelle Wright, 2023. "Monitoring Financial Conditions and Downside Risk to Economic Activity in Australia," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 253-287, June.
    17. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Working Papers ECARES 2023-15, ULB -- Universite Libre de Bruxelles.
    18. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    19. Filipiak, Katarzyna & Klein, Daniel, 2017. "Estimation of parameters under a generalized growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 73-86.
    20. Hörmann, Siegfried & Jammoul, Fatima, 2022. "Consistently recovering the signal from noisy functional data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    21. Gabriele Fiorentini & Enrique Sentana, 2013. "Dynamic Specification Tests for Dynamic Factor Models," Working Papers wp2013_1306, CEMFI.
    22. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    23. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers rdp2024-04, Reserve Bank of Australia.
    24. Li, Kunpeng & Lu, Lina, 2014. "Efficient estimation of heterogeneous coefficients in panel data models with common shock," MPRA Paper 59312, University Library of Munich, Germany.
    25. Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
    26. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.
    27. Hou, Lei & Li, Kunpeng & Li, Qi & Ouyang, Min, 2021. "Revisiting the location of FDI in China: A panel data approach with heterogeneous shocks," Journal of Econometrics, Elsevier, vol. 221(2), pages 483-509.
    28. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.
    29. Franco Peracchi & Claudio Rossetti, 2022. "A nonlinear dynamic factor model of health and medical treatment," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1046-1066, June.
    30. Poncela, Pilar, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    31. Bai, Jushan, 2013. "Likelihood approach to dynamic panel models with interactive effects," MPRA Paper 50267, University Library of Munich, Germany.
    32. Matteo Barigozzi, 2022. "On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis," Papers 2211.01921, arXiv.org, revised Jul 2023.
    33. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    34. Poncela Blanco, Maria Pilar & Fresoli, Diego Eduardo, 2022. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 36251, Universidad Carlos III de Madrid. Departamento de Estadística.
    35. Joakim Westerlund, 2020. "A cross‐section average‐based principal components approach for fixed‐T panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 776-785, September.
    36. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    37. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
    38. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2023. "Latent Factor Analysis in Short Panels," Papers 2306.14004, arXiv.org, revised May 2024.
    39. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    40. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.
    41. Christian Brownlees & Gu{dh}mundur Stef'an Gu{dh}mundsson & Yaping Wang, 2024. "Performance of Empirical Risk Minimization For Principal Component Regression," Papers 2409.03606, arXiv.org, revised Sep 2024.
    42. Mikkelsen, Jakob Guldbæk & Hillebrand, Eric & Urga, Giovanni, 2019. "Consistent estimation of time-varying loadings in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 208(2), pages 535-562.
    43. Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    44. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    45. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    46. Giorgio Calzolari & Roxana Halbleib & Christian Mucher, 2023. "Sequential Estimation of Multivariate Factor Stochastic Volatility Models," Papers 2302.07052, arXiv.org.
    47. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    48. Alonso, Andrés M. & Galeano, Pedro & Peña, Daniel, 2020. "A robust procedure to build dynamic factor models with cluster structure," Journal of Econometrics, Elsevier, vol. 216(1), pages 35-52.
    49. Christian Brownlees & Geert Mesters, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona School of Economics.
    50. Martin Solberger & Erik Spånberg, 2020. "Estimating a Dynamic Factor Model in EViews Using the Kalman Filter and Smoother," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 875-900, March.
    51. Monika Bours & Ansgar Steland, 2021. "Large‐sample approximations and change testing for high‐dimensional covariance matrices of multivariate linear time series and factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 610-654, June.
    52. Bai, Jushan & Li, Kunpeng & Lu, Lina, 2014. "Estimation and inference of FAVAR models," MPRA Paper 60960, University Library of Munich, Germany.
    53. Zhe Sun & Yundong Tu, 2024. "Factors in Fashion: Factor Analysis towards the Mode," Papers 2409.19287, arXiv.org.
    54. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    55. Linton, O. B. & Tang, H. & Wu, J., 2022. "A Structural Dynamic Factor Model for Daily Global Stock Market Returns," Janeway Institute Working Papers camjip:2214, Faculty of Economics, University of Cambridge.
    56. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  4. Bai, Jushan & Li, Kunpeng, 2010. "Theory and methods of panel data models with interactive effects," MPRA Paper 43441, University Library of Munich, Germany, revised Dec 2012.

    Cited by:

    1. Arteaga-Molina, Luis A. & Rodríguez-Poo, Juan M., 2019. "Empirical likelihood based inference for a categorical varying-coefficient panel data model with fixed effects," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 110-124.
    2. Xuan, Liang & Jiti, Gao & xiaodong, Gong, 2021. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," MPRA Paper 108497, University Library of Munich, Germany, revised 30 May 2021.
    3. Li, Kathleen T. & Bell, David R., 2017. "Estimation of average treatment effects with panel data: Asymptotic theory and implementation," Journal of Econometrics, Elsevier, vol. 197(1), pages 65-75.
    4. Jia Chen, 2018. "Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models," Discussion Papers 18/15, Department of Economics, University of York.
    5. Archer Gong Zhang & Jiahua Chen, 2023. "Optimal Estimation under a Semiparametric Density Ratio Model," Papers 2309.09103, arXiv.org.
    6. Degui Li & Junhui Qian & Liangjun Su, 2016. "Panel Data Models With Interactive Fixed Effects and Multiple Structural Breaks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1804-1819, October.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ETS: Econometric Time Series (3) 2012-10-27 2013-02-16 2019-02-04
  2. NEP-ECM: Econometrics (2) 2012-10-27 2013-02-16
  3. NEP-ORE: Operations Research (1) 2019-02-04

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