IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v20y2020i3p647-679.html

Smooth varying-coefficient models in Stata

Author

Listed:
  • Fernando Rios-Avila

    (Levy Economics Institute of Bard College)

Abstract

Nonparametric regressions are powerful statistical tools that can be used to model relationships between dependent and independent variables with minimal assumptions on the underlying functional forms. Despite their poten- tial benefits, these models have two weaknesses: The added flexibility creates a curse of dimensionality, and procedures available for model selection, like cross- validation, have a high computational cost in samples with even moderate sizes. An alternative to fully nonparametric models is semiparametric models that com- bine the flexibility of nonparametric regressions with the structure of standard models. In this article, I describe the estimation of a particular type of semipara- metric model known as the smooth varying-coefficient model (Hastie and Tibshi- rani, 1993, Journal of the Royal Statistical Society, Series B 55: 757–796), based on kernel regression methods, using a new set of commands within vc pack. These commands aim to facilitate bandwidth selection and model estimation as well as create visualizations of the results.

Suggested Citation

  • Fernando Rios-Avila, 2020. "Smooth varying-coefficient models in Stata," Stata Journal, StataCorp LLC, vol. 20(3), pages 647-679, September.
    • Handle: RePEc:tsj:stataj:v:20:y:2020:i:3:p:647-679
    DOI: 10.1177/1536867X20953574
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0613/
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/article.html?article=st0613
    File Function: link to article purchase
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1536867X20953574?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
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. David Stadelmann & Tobias Thomas & Nikita Zakharov, 2026. "Too hot to play it cool? Temperature and negative media bias," Public Choice, Springer, vol. 206(3), pages 593-608, March.
    2. Gnimassoun, Blaise & Grekou, Carl & Mignon, Valérie, 2026. "The industrial cost of fixed exchange rate regimes," Journal of Macroeconomics, Elsevier, vol. 87(C).
    3. Bao, Te & Ma, Mengzhong & Wen, Yonggang, 2023. "Herding in the non-fungible token (NFT) market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    4. African Department International Monetary Fund & Western Africa Region World Bank, 2024. "Enhancing Sustainable and Inclusive Growth in the Central African Economic and Monetary Community," World Bank Publications - Reports 38438, The World Bank Group.
    5. Gioia M. Mariani & Eleonora Porreca & Concetta Rondinelli, 2025. "How do households adjust house price expectations in an era of high inflation? Experimental evidence," Questioni di Economia e Finanza (Occasional Papers) 940, Bank of Italy, Economic Research and International Relations Area.
    6. Salisu, Afees A. & Isah, Kazeem & Vinh Vo, Xuan, 2025. "The “effect modifier” of US interest rate in the economic policy uncertainties and economic conditions of fifty (50) US states: A semi-parametric smooth varying-coefficient approach," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    7. Andersson, Fredrik N.G., 2023. "Income inequality and carbon emissions in the United States 1929–2019," Ecological Economics, Elsevier, vol. 204(PA).
    8. Assaf Botzer, 2025. "Publication Trends on the Varying Coefficients Model: Estimating the Actual (Under)Utilization of a Highly Acclaimed Method for Studying Statistical Interactions," Publications, MDPI, vol. 13(2), pages 1-19, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:tsj:stataj:v:20:y:2020:i:3:p:647-679. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum or Lisa Gilmore (email available below). General contact details of provider: http://www.stata-journal.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.