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Lina Lu

Personal Details

First Name:Lina
Middle Name:
Last Name:Lu
Suffix:
RePEc Short-ID:plu263
[This author has chosen not to make the email address public]
http://www.columbia.edu/~ll2582/

Affiliation

Federal Reserve Bank of Boston

Boston, Massachusetts (United States)
http://www.bos.frb.org/
RePEc:edi:frbbous (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Tomohiro Ando & Jushan Bai & Lina Lu & Cindy M. Vojtech, 2024. "Scenario-based Quantile Connectedness of the U.S. Interbank Liquidity Risk Network," Supervisory Research and Analysis Working Papers SRA 24-02, Federal Reserve Bank of Boston.
  2. Nicola Cetorelli & Mattia Landoni & Lina Lu, 2023. "Non-Bank Financial Institutions and Banks’ Fire-Sale Vulnerabilities," Staff Reports 1057, Federal Reserve Bank of New York.
  3. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Supervisory Research and Analysis Working Papers RPA 19-2, Federal Reserve Bank of Boston.
  4. 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.
  5. Lina Lu, 2017. "Simultaneous Spatial Panel Data Models with Common Shocks," Supervisory Research and Analysis Working Papers RPA 17-3, Federal Reserve Bank of Boston.
  6. Bai, Jushan & Li, Kunpeng & Lu, Lina, 2014. "Estimation and inference of FAVAR models," MPRA Paper 60960, University Library of Munich, Germany.
  7. 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.

Articles

  1. Li, Kunpeng & Li, Qi & Lu, Lina, 2018. "Quasi maximum likelihood analysis of high dimensional constrained factor models," Journal of Econometrics, Elsevier, vol. 206(2), pages 574-612.
  2. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Finance and Economics Discussion Series 2019-048, Board of Governors of the Federal Reserve System (U.S.).

    Mentioned in:

    1. Negative Nominal Interest Rates: A Primer
      by Steve Cecchetti and Kim Schoenholtz in Money, Banking and Financial Markets on 2019-12-02 13:10:33

Working papers

  1. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Supervisory Research and Analysis Working Papers RPA 19-2, Federal Reserve Bank of Boston.

    Cited by:

    1. Campbell, John Y. & Sigalov, Roman, 2022. "Portfolio choice with sustainable spending: A model of reaching for yield," Journal of Financial Economics, Elsevier, vol. 143(1), pages 188-206.
    2. Jansen, Kristy, 2021. "Essays on institutional investors, portfolio choice, and asset prices," Other publications TiSEM fd998408-d282-4e0f-b542-4, Tilburg University, School of Economics and Management.
    3. Aleksandar Andonov & Roman Kräussl & Joshua Rauh & Stijn Van Nieuwerburgh, 2021. "Institutional Investors and Infrastructure Investing [Pension fund asset allocation and liability discount rates]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3880-3934.

  2. 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.

  3. Lina Lu, 2017. "Simultaneous Spatial Panel Data Models with Common Shocks," Supervisory Research and Analysis Working Papers RPA 17-3, Federal Reserve Bank of Boston.

    Cited by:

    1. Henok Fasil Telila, 2024. "Frontier markets sovereign risk: New evidence from spatial econometric models," French Stata Users' Group Meetings 2024 10, Stata Users Group.
    2. Ando, Tomohiro & Li, Kunpeng & Lu, Lina, 2023. "A spatial panel quantile model with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 232(1), pages 191-213.
    3. AMBA OYON, Claude Marius & Mbratana, Taoufiki, 2017. "Simultaneous equation models with spatially autocorrelated error components," MPRA Paper 82395, University Library of Munich, Germany.
    4. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    5. Marius C. O. Amba, 2021. "Simultaneous Equations with Three Way Error Components," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(3), pages 583-596, September.
    6. Chen, Jia & Shin, Yongcheol & Zheng, Chaowen, 2022. "Estimation and inference in heterogeneous spatial panels with a multifactor error structure," Journal of Econometrics, Elsevier, vol. 229(1), pages 55-79.

  4. Bai, Jushan & Li, Kunpeng & Lu, Lina, 2014. "Estimation and inference of FAVAR models," MPRA Paper 60960, University Library of Munich, Germany.

    Cited by:

    1. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
    2. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    3. Alexander Chudik & M. Hashem Pesaran & Kamiar Mohaddes, 2019. "Identifying global and national output and fiscal policy shocks using a GVAR," CAMA Working Papers 2019-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Zongwu Cai & Xiyuan Liu, 2021. "Solving the Price Puzzle Via A Functional Coefficient Factor-Augmented VAR Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202106, University of Kansas, Department of Economics, revised Jan 2021.
    5. Jiahe Lin & George Michailidis, 2019. "Approximate Factor Models with Strongly Correlated Idiosyncratic Errors," Papers 1912.04123, arXiv.org.
    6. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    8. Xiang Gao & Wen Kong & Zhijun Hu, 2022. "The Effects of National Fundamental Factors on Regional House Prices: A Factor-Augmented VAR Analysis," JRFM, MDPI, vol. 15(7), pages 1-19, July.
    9. Ashoka Mody & Milan Nedeljkovic, 2018. "Central Bank Policies and Financial Markets: Lessons from the Euro Crisis," Working Papers 253, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Herrala, Risto & Orlandi, Fabrice, 2020. "Win-win? Assessing the global impact of the Chinese economy," BOFIT Discussion Papers 4/2020, Bank of Finland Institute for Emerging Economies (BOFIT).
    11. Yemba, Boniface & Kitenge, Erick & Tang, Biyan & Gaekwad, Neepa B., 2024. "Monetary policy in China: A Factor Augmented VAR approach," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 975-1008.
    12. Yohei Yamamoto & Naoko Hara, 2022. "Identifying factor‐augmented vector autoregression models via changes in shock variances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 722-745, June.
    13. 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.
    14. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    15. Maurizio Daniele & Julie Schnaitmann, 2019. "A Regularized Factor-augmented Vector Autoregressive Model," Papers 1912.06049, arXiv.org.
    16. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," ISER Discussion Paper 1053r, Institute of Social and Economic Research, Osaka University, revised Mar 2020.
    17. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    18. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    19. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    20. Herwartz, Helmut & Rohloff, Hannes, 2018. "Less bang for the buck? Assessing the role of inflation uncertainty for U.S. monetary policy transmission in a data rich environment," University of Göttingen Working Papers in Economics 358, University of Goettingen, Department of Economics.
    21. Ashoka Mody & Milan Nedeljkovic, 2018. "Central Bank Policies and Financial Markets: Lessons from the Euro Crisis," CESifo Working Paper Series 7400, CESifo.
    22. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2017. "Structural Factor Analysis of Interest Rate Pass Through In Four Large Euro Area Economies," Working Papers in Economics 17/07, University of Canterbury, Department of Economics and Finance.
    23. Christian Brownlees & Geert Mesters, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona School of Economics.
    24. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    25. Jiahe Lin & George Michailidis, 2019. "Regularized Estimation of High-dimensional Factor-Augmented Vector Autoregressive (FAVAR) Models," Papers 1912.04146, arXiv.org, revised May 2020.
    26. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Estimation of Grouped Time-Varying Network Vector Autoregression Models," Papers 2303.10117, arXiv.org, revised Mar 2024.
    27. Martin Hodula & Martin Macháček & Aleš Melecký, 2020. "Placing the Czech Shadow Banking Sector under the Light," Prague Economic Papers, Prague University of Economics and Business, vol. 2020(1), pages 3-28.
    28. Antoine A. Djogbenou, 2024. "Identifying oil price shocks with global, developed, and emerging latent real economy activity factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 128-149, January.

  5. 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.

    Cited by:

    1. Su, Liangjun & Ju, Gaosheng, 2018. "Identifying latent grouped patterns in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 554-573.
    2. Çiçekçi, Cumhur & Gaygısız, Esma, 2023. "Procyclicality of fiscal policy in oil-rich countries: Roles of resource funds and institutional quality," Resources Policy, Elsevier, vol. 85(PB).

Articles

  1. Li, Kunpeng & Li, Qi & Lu, Lina, 2018. "Quasi maximum likelihood analysis of high dimensional constrained factor models," Journal of Econometrics, Elsevier, vol. 206(2), pages 574-612.
    See citations under working paper version above.
  2. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    See citations under working paper version above.Sorry, no citations of articles recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 9 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (5) 2014-11-22 2015-01-19 2017-01-08 2017-11-12 2024-07-08. Author is listed
  2. NEP-ORE: Operations Research (3) 2017-01-08 2017-11-12 2019-02-04
  3. NEP-ETS: Econometric Time Series (2) 2015-01-19 2019-02-04
  4. NEP-NET: Network Economics (2) 2023-05-29 2024-07-08
  5. NEP-DES: Economic Design (1) 2023-05-29
  6. NEP-FMK: Financial Markets (1) 2019-07-22
  7. NEP-GEO: Economic Geography (1) 2017-11-12
  8. NEP-MAC: Macroeconomics (1) 2019-07-22
  9. NEP-MON: Monetary Economics (1) 2024-07-08
  10. NEP-PUB: Public Finance (1) 2019-07-22
  11. NEP-RMG: Risk Management (1) 2024-07-08
  12. NEP-URE: Urban and Real Estate Economics (1) 2017-11-12

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