IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v12y2019i4p180-d292636.html
   My bibliography  Save this article

Nonparametric Econometric Methods and Applications

Author

Listed:
  • Thanasis Stengos

    (Department of Economics and Finance, University of Guelph, Guelph, ON N1G 2W1, Canada)

Abstract

An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The present Special Issue collects a number of new contributions, both theoretical and empirical that cover a wide spectrum of areas such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth as well as statistical theory and methodology.

Suggested Citation

  • Thanasis Stengos, 2019. "Nonparametric Econometric Methods and Applications," JRFM, MDPI, vol. 12(4), pages 1-3, November.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:4:p:180-:d:292636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/12/4/180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/12/4/180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yiguo Sun & Ximing Wu, 2018. "Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study," JRFM, MDPI, vol. 11(2), pages 1-20, June.
    2. Burak Alparslan Eroğlu & Barış Soybilgen, 2018. "On the Performance of Wavelet Based Unit Root Tests," JRFM, MDPI, vol. 11(3), pages 1-22, August.
    3. Pantelis Kalaitzidakis & Theofanis P. Mamuneas & Thanasis Stengos, 2018. "Greenhouse Emissions and Productivity Growth," JRFM, MDPI, vol. 11(3), pages 1-14, July.
    4. Chaoyi Chen & Yiguo Sun, 2018. "Monte Carlo Comparison for Nonparametric Threshold Estimators," JRFM, MDPI, vol. 11(3), pages 1-15, August.
    5. Mark J. Jensen & John M. Maheu, 2018. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-29, September.
    6. Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.
    7. Nickolaos G. Tzeremes, 2018. "Financial Development and Countries’ Production Efficiency: A Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-13, August.
    8. Sadat Reza & Paul Rilstone, 2019. "Smoothed Maximum Score Estimation of Discrete Duration Models," JRFM, MDPI, vol. 12(2), pages 1-16, April.
    9. Mustafa Koroglu, 2019. "Growth and Debt: An Endogenous Smooth Coefficient Approach," JRFM, MDPI, vol. 12(1), pages 1-22, February.
    10. Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    2. Liu, Haiyun & Islam, Mollah Aminul & Khan, Muhammad Asif & Hossain, Md Ismail & Pervaiz, Khansa, 2020. "Does financial deepening attract foreign direct investment? Fresh evidence from panel threshold analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    3. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).
    4. Frank, Johannes, 2023. "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics 03/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    6. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    7. Mst. Shapna Akter & Hossain Shahriar & Reaz Chowdhury & M. R. C. Mahdy, 2022. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach," Future Internet, MDPI, vol. 14(9), pages 1-23, August.
    8. Florian Bourgey & Emmanuel Gobet & Ying Jiao, 2022. "Bridging socioeconomic pathways of CO2 emission and credit risk," Working Papers hal-03458299, HAL.
    9. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    10. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    11. Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
    12. Athanasios P. Fassas & Nikolas Hourvouliades, 2019. "VIX Futures as a Market Timing Indicator," JRFM, MDPI, vol. 12(3), pages 1-9, July.
    13. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    14. Miriam Hägele & Jaakko Lehtomaa, 2021. "Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    15. João Leitão & Joaquim Ferreira, 2021. "Dynamic Effects of Material Production and Environmental Sustainability on Economic Vitality Indicators: A Panel VAR Approach," JRFM, MDPI, vol. 14(2), pages 1-20, February.
    16. Alexandra Soberon & Irene D’Hers, 2020. "The Environmental Kuznets Curve: A Semiparametric Approach with Cross-Sectional Dependence," JRFM, MDPI, vol. 13(11), pages 1-23, November.
    17. Anh The Vo & Duc Hong Vo & Quan Thai-Thuong Le, 2019. "CO 2 Emissions, Energy Consumption, and Economic Growth: New Evidence in the ASEAN Countries," JRFM, MDPI, vol. 12(3), pages 1-20, September.
    18. Anh-Tu Nguyen & Shih-Hao Lu & Phuc Thanh Thien Nguyen, 2021. "Validating and Forecasting Carbon Emissions in the Framework of the Environmental Kuznets Curve: The Case of Vietnam," Energies, MDPI, vol. 14(11), pages 1-38, May.
    19. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    20. Rigas, Nikos & Kounetas, Konstantinos, 2021. "The Role of temperature, Precipitation and CO2 emissions on Countries’ Economic Growth and Productivity," MPRA Paper 104727, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:12:y:2019:i:4:p:180-:d:292636. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.