IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2306.05593.html
   My bibliography  Save this paper

Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy

Author

Listed:
  • Jiti Gao
  • Fei Liu
  • Bin Peng
  • Yanrong Yang

Abstract

In this paper, we investigate a semiparametric regression model under the context of treatment effects via a localized neural network (LNN) approach. Due to a vast number of parameters involved, we reduce the number of effective parameters by (i) exploring the use of identification restrictions; and (ii) adopting a variable selection method based on the group-LASSO technique. Subsequently, we derive the corresponding estimation theory and propose a dependent wild bootstrap procedure to construct valid inferences accounting for the dependence of data. Finally, we validate our theoretical findings through extensive numerical studies. In an empirical study, we revisit the impacts of a tightening monetary policy action on a variety of economic variables, including short-/long-term interest rate, inflation, unemployment rate, industrial price and equity return via the newly proposed framework using a monthly dataset of the US.

Suggested Citation

  • Jiti Gao & Fei Liu & Bin Peng & Yanrong Yang, 2023. "Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy," Papers 2306.05593, arXiv.org, revised Jul 2024.
    • Handle: RePEc:arx:papers:2306.05593
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2306.05593
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kline Patrick & Santos Andres, 2012. "A Score Based Approach to Wild Bootstrap Inference," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 23-41, August.
    2. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    3. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    4. El-Sharif, Idris & Brown, Dick & Burton, Bruce & Nixon, Bill & Russell, Alex, 2005. "Evidence on the nature and extent of the relationship between oil prices and equity values in the UK," Energy Economics, Elsevier, vol. 27(6), pages 819-830, November.
    5. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    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. Jiti Gao & Bin Peng & Yayi Yan, 2023. "A Localised Neural network with Dependent Data: Estimation and Inference," Monash Econometrics and Business Statistics Working Papers 15/23, Monash University, Department of Econometrics and Business Statistics.
    2. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
    3. Lyócsa, Štefan & Todorova, Neda, 2021. "What drives volatility of the U.S. oil and gas firms?," Energy Economics, Elsevier, vol. 100(C).
    4. Casas Villalba, Maria Isabel & Mao, Xiuping & Lopes Moreira da Veiga, María Helena, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Guohua Feng & Jiti Gao & Fei Liu & Bin Peng, 2023. "Estimation and Inference for Three-Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers 20/23, Monash University, Department of Econometrics and Business Statistics.
    6. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    7. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    8. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    9. Veith, Stefan & Werner, Jörg R. & Zimmermann, Jochen, 2009. "Capital market response to emission rights returns: Evidence from the European power sector," Energy Economics, Elsevier, vol. 31(4), pages 605-613, July.
    10. Federica Alfani & Vasco Molini & Giacomo Pallante & Alessandro PalmaGran, 2024. "Job displacement and reallocation failure. Evidence from climate shocks in Morocco," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 51(1), pages 1-31.
    11. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    12. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    13. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2022. "Spillovers and diversification benefits between oil futures and ASEAN stock markets," Resources Policy, Elsevier, vol. 79(C).
    14. Lee, Hwang Hee & Hyun, Jung-Soon, 2019. "The asymmetric effect of equity volatility on credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 125-136.
    15. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    16. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    17. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    18. Federico Belotti & Edoardo Di Porto & Gianluca Santoni, 2021. "The effect of local taxes on firm performance: Evidence from geo‐referenced data," Journal of Regional Science, Wiley Blackwell, vol. 61(2), pages 492-510, March.
    19. Martins-Filho, Carlos & Yao, Feng & Torero, Maximo, 2018. "Nonparametric Estimation Of Conditional Value-At-Risk And Expected Shortfall Based On Extreme Value Theory," Econometric Theory, Cambridge University Press, vol. 34(1), pages 23-67, February.
    20. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2306.05593. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.