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Jau-er Chen

Personal Details

First Name:Jau-er
Middle Name:
Last Name:Chen
Suffix:
RePEc Short-ID:pch1555
[This author has chosen not to make the email address public]
https://jauerblog.wordpress.com/cv_en

Affiliation

School of Economics
Senshu University

Tokyo, Japan
http://www.senshu-u.ac.jp/sc_grsc/keizai/
RePEc:edi:sesenjp (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2019. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Papers 1909.12592, arXiv.org, revised Feb 2021.

Articles

  1. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
  2. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
  3. Jau-er Chen & Rajarshi Mitra, 2020. "Demographic Shifts and Asset Returns in Japan," Economics Bulletin, AccessEcon, vol. 40(2), pages 1570-1582.
  4. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
  5. Ming‐Hsuan Lee & Tou‐Chin Tsai & Jau‐er Chen & Mon‐Chi Lio, 2019. "Can Information And Communication Technology Improve Stock Market Efficiency? A Cross‐Country Study," Bulletin of Economic Research, Wiley Blackwell, vol. 71(2), pages 113-135, April.
  6. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
  7. Jau-er Chen & Masanori Kashiwagi, 2017. "The Japanese Taylor rule estimated using censored quantile regressions," Empirical Economics, Springer, vol. 52(1), pages 357-371, February.
  8. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2019. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Papers 1909.12592, arXiv.org, revised Feb 2021.

    Cited by:

    1. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    2. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    3. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
    4. Seoyun Hong, 2023. "Censored Quantile Regression with Many Controls," Papers 2303.02784, arXiv.org.

Articles

  1. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    See citations under working paper version above.
  2. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.

    Cited by:

    1. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    2. Zhouwei Wang & Qicheng Zhao & Min Zhu & Tao Pang, 2020. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    3. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.

  3. Ming‐Hsuan Lee & Tou‐Chin Tsai & Jau‐er Chen & Mon‐Chi Lio, 2019. "Can Information And Communication Technology Improve Stock Market Efficiency? A Cross‐Country Study," Bulletin of Economic Research, Wiley Blackwell, vol. 71(2), pages 113-135, April.

    Cited by:

    1. Ebaidalla Mahjoub Ebaidalla & Sana Abusin, 2022. "The Effect of ICT on CO2 Emissions in the GCC Countries: Does Globalization Matter?," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 56-66, November.
    2. Sepehrdoust, Hamid & Ahmadvand, Shokoufeh & Mirzaei, Nesa, 2022. "Impact of information, communication technology and housing industry on financial market development," Technology in Society, Elsevier, vol. 69(C).
    3. Jiexia Ye & Juanjuan Zhao & Kejiang Ye & Chengzhong Xu, 2020. "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction," Papers 2005.04955, arXiv.org, revised Oct 2020.
    4. Asif Khan & Wu Ximei, 2022. "Digital Economy and Environmental Sustainability: Do Information Communication and Technology (ICT) and Economic Complexity Matter?," IJERPH, MDPI, vol. 19(19), pages 1-21, September.
    5. abid, Nabila & Ceci, Federica & Razzaq, Asif, 2023. "Inclusivity of information and communication technology in ecological governance for sustainable resources management in G10 countries," Resources Policy, Elsevier, vol. 81(C).

  4. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.

    Cited by:

    1. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    2. Mogens Fosgerau & Dennis Kristensen, 2021. "Identification of a class of index models: A topological approach," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 121-133.
    3. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
    4. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    5. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
    6. Takayuki Toda & Ayako Wakano & Takahiro Hoshino, 2019. "Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation," Papers 1905.04443, arXiv.org.

  5. Jau-er Chen & Masanori Kashiwagi, 2017. "The Japanese Taylor rule estimated using censored quantile regressions," Empirical Economics, Springer, vol. 52(1), pages 357-371, February.

    Cited by:

    1. Christou Christina & Naraidoo Ruthira & Gupta Rangan, 2020. "Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-17, June.
    2. Tilfani, Oussama & Kristoufek, Ladislav & Ferreira, Paulo & El Boukfaoui, My Youssef, 2022. "Heterogeneity in economic relationships: Scale dependence through the multivariate fractal regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Christis Hassapis, 2019. "Monetary Policy Reaction to Uncertainty in Japan: Evidence from a Quantile-on-Quantile Interest Rate Rule," Working Papers 201929, University of Pretoria, Department of Economics.
    4. Pierre L. Siklos, 2020. "Looking into the Rear-View Mirror: Lessons from Japan for the Eurozone and the U.S?," IMES Discussion Paper Series 20-E-02, Institute for Monetary and Economic Studies, Bank of Japan.

  6. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.

    Cited by:

    1. Christou Christina & Naraidoo Ruthira & Gupta Rangan, 2020. "Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-17, June.
    2. Ando, Tomohiro & Bai, Jushan, 2018. "Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity," MPRA Paper 88765, University Library of Munich, Germany.
    3. Dejan Živkov & Marina Gajic-Glamoclija & Jasmina Duraskovic & Mirela Momcilovic, 2022. "Assessing Permanent and Transitory Volatility Spillover Effect from Oil to Stocks in Baltic and Visegrad Countries," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 70(6), pages 523-542, June.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper 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-BIG: Big Data (1) 2019-10-07. Author is listed
  2. NEP-ECM: Econometrics (1) 2019-10-07. Author is listed

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