IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v186y2020ics0165176519304410.html
   My bibliography  Save this article

Wasserstein Index Generation Model: Automatic generation of time-series index with application to Economic Policy Uncertainty

Author

Listed:
  • Xie, Fangzhou

Abstract

I propose a novel method, the Wasserstein Index Generation model (WIG), to generate a public sentiment index automatically. To test the model’s effectiveness, an application to generate Economic Policy Uncertainty (EPU) index is showcased.

Suggested Citation

  • Xie, Fangzhou, 2020. "Wasserstein Index Generation Model: Automatic generation of time-series index with application to Economic Policy Uncertainty," Economics Letters, Elsevier, vol. 186(C).
  • Handle: RePEc:eee:ecolet:v:186:y:2020:i:c:s0165176519304410
    DOI: 10.1016/j.econlet.2019.108874
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176519304410
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2019.108874?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Castelnuovo, Efrem & Tran, Trung Duc, 2017. "Google It Up! A Google Trends-based Uncertainty index for the United States and Australia," Economics Letters, Elsevier, vol. 161(C), pages 149-153.
    2. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    3. Ghirelli, Corinna & Pérez, Javier J. & Urtasun, Alberto, 2019. "A new economic policy uncertainty index for Spain," Economics Letters, Elsevier, vol. 182(C), pages 64-67.
    4. Saltzman, Bennett & Yung, Julieta, 2018. "A machine learning approach to identifying different types of uncertainty," Economics Letters, Elsevier, vol. 171(C), pages 58-62.
    5. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ivana Lolić & Petar Sorić & Marija Logarušić, 2022. "Economic Policy Uncertainty Index Meets Ensemble Learning," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 401-437, August.
    2. Fangzhou Xie, 2020. "Pruned Wasserstein Index Generation Model and wigpy Package," Papers 2004.00999, arXiv.org, revised Jul 2020.
    3. Bhanu Pratap & Nalin Priyaranjan, 2023. "Macroeconomic effects of uncertainty: a Google trends-based analysis for India," Empirical Economics, Springer, vol. 65(4), pages 1599-1625, October.
    4. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    5. Viktoriia Naboka-Krell, 2023. "Construction and Analysis of Uncertainty Indices based on Multilingual Text Representations," MAGKS Papers on Economics 202310, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

    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. Fangzhou Xie, 2020. "Pruned Wasserstein Index Generation Model and wigpy Package," Papers 2004.00999, arXiv.org, revised Jul 2020.
    2. Catalina Bolancé & Carlos Alberto Acuña & Salvador Torra, 2022. "Non-Normal Market Losses and Spatial Dependence Using Uncertainty Indices," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    3. Donadelli, Michael & Gufler, Ivan & Pellizzari, Paolo, 2020. "The macro and asset pricing implications of rising Italian uncertainty: Evidence from a novel news-based macroeconomic policy uncertainty index," Economics Letters, Elsevier, vol. 197(C).
    4. Corinna Ghirelli & María Gil & Javier J. Pérez & Alberto Urtasun, 2021. "Measuring economic and economic policy uncertainty and their macroeconomic effects: the case of Spain," Empirical Economics, Springer, vol. 60(2), pages 869-892, February.
    5. Bhanu Pratap & Nalin Priyaranjan, 2023. "Macroeconomic effects of uncertainty: a Google trends-based analysis for India," Empirical Economics, Springer, vol. 65(4), pages 1599-1625, October.
    6. Kyoto Yono & Hiroki Sakaji & Hiroyasu Matsushima & Takashi Shimada & Kiyoshi Izumi, 2020. "Construction of Macroeconomic Uncertainty Indices for Financial Market Analysis Using a Supervised Topic Model," JRFM, MDPI, vol. 13(4), pages 1-18, April.
    7. Nikolay Hristov & Markus Roth, 2019. "Uncertainty Shocks and Financial Crisis Indicators," CESifo Working Paper Series 7839, CESifo.
    8. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    9. Himounet, Nicolas, 2022. "Searching the nature of uncertainty: Macroeconomic and financial risks VS geopolitical and pandemic risks," International Economics, Elsevier, vol. 170(C), pages 1-31.
    10. Mueller, Hannes & Garcia-Uribe, Sandra & Sanz, Carlos, 2020. "Economic Uncertainty and Divisive Politics: Evidence from the "dos Españas"," CEPR Discussion Papers 15479, C.E.P.R. Discussion Papers.
    11. Gabriel Caldas Montes & Victor Maia, 2023. "The reaction of disagreements in inflation expectations to fiscal sentiment obtained from information in official communiqués," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 828-859, October.
    12. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    13. Johannes Zahner, 2020. "Above, but close to two percent. Evidence on the ECB’s inflation target using text mining," MAGKS Papers on Economics 202046, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    14. Hansen, Stephen & McMahon, Michael & Tong, Matthew, 2019. "The long-run information effect of central bank communication," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 185-202.
    15. Baranowski, Paweł & Doryń, Wirginia & Łyziak, Tomasz & Stanisławska, Ewa, 2021. "Words and deeds in managing expectations: Empirical evidence from an inflation targeting economy," Economic Modelling, Elsevier, vol. 95(C), pages 49-67.
    16. Tosapol Apaitan & Pongsak Luangaram & Pym Manopimoke, 2022. "Uncertainty in an emerging market economy: evidence from Thailand," Empirical Economics, Springer, vol. 62(3), pages 933-989, March.
    17. Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2021. "A global economic policy uncertainty index from principal component analysis," Finance Research Letters, Elsevier, vol. 40(C).
    18. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    19. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    20. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.

    More about this item

    Keywords

    Economic Policy Uncertainty Index (EPU); Wasserstein Dictionary Learning (WDL); Singular Value Decomposition (SVD); Wasserstein Index Generation Model (WIG);
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

    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:eee:ecolet:v:186:y:2020:i:c:s0165176519304410. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.