IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v20y2020i3p647-679.html
   My bibliography  Save this article

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 LP, 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:

    References listed on IDEAS

    as
    1. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253, January.
    2. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    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. 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).
    2. Andersson, Fredrik N.G., 2023. "Income inequality and carbon emissions in the United States 1929–2019," Ecological Economics, Elsevier, vol. 204(PA).

    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. David H. Bernstein & Christopher F. Parmeter, 2017. "Returns to Scale in Electricity Generation: Revisited and Replicated," Working Papers 2017-08, University of Miami, Department of Economics.
    2. Herwartz, H. & Xu, F., 2010. "A functional coefficient model view of the Feldstein-Horioka puzzle," Journal of International Money and Finance, Elsevier, vol. 29(1), pages 37-54, February.
    3. Almeida, Alexandre N. & Bravo-Ureta, Boris E., 2019. "Agricultural productivity, shadow wages and off-farm labor decisions in Nicaragua," Economic Systems, Elsevier, vol. 43(1), pages 99-110.
    4. Battisti, Michele & Gatto, Massimo Del & Parmeter, Christopher F., 2022. "Skill-biased technical change and labor market inefficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    5. Delgado, Michael S. & McCloud, Nadine & Kumbhakar, Subal C., 2014. "A generalized empirical model of corruption, foreign direct investment, and growth," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 298-316.
    6. Töpfer, Marina, 2017. "Detailed RIF decomposition with selection: The gender pay gap in Italy," Hohenheim Discussion Papers in Business, Economics and Social Sciences 26-2017, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    7. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    8. Wong, Heung & Ip, Wai-cheung & Zhang, Riquan, 2008. "Varying-coefficient single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1458-1476, January.
    9. Huang, Bai & Lee, Tae-Hwy & Ullah, Aman, 2020. "Combined estimation of semiparametric panel data models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 30-45.
    10. E. Zacharias & T. Stengos, 2006. "Intertemporal pricing and price discrimination: a semiparametric hedonic analysis of the personal computer market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 371-386.
    11. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    12. Cai, Zongwu, 2003. "Nonparametric estimation equations for time series data," Statistics & Probability Letters, Elsevier, vol. 62(4), pages 379-390, May.
    13. Wang, Qihua & Zhang, Riquan, 2009. "Statistical estimation in varying coefficient models with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2389-2405, November.
    14. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
    15. Linton, Oliver & Xiao, Zhijie, 2019. "Efficient estimation of nonparametric regression in the presence of dynamic heteroskedasticity," Journal of Econometrics, Elsevier, vol. 213(2), pages 608-631.
    16. Daniel J. Henderson & Andrew Houtenville & Le Wang, 2017. "The Distribution of Returns to Education for People with Disabilities," Journal of Labor Research, Springer, vol. 38(3), pages 261-282, September.
    17. Faten Ben Bouheni & Hassan Obeid & Elena Margarint, 2022. "Nonperforming loan of European Islamic banks over the economic cycle," Annals of Operations Research, Springer, vol. 313(2), pages 773-808, June.
    18. Hassani, Hossein & Yeganegi, Mohammad Reza & Gupta, Rangan, 2019. "Does inequality really matter in forecasting real housing returns of the United Kingdom?," International Economics, Elsevier, vol. 159(C), pages 18-25.
    19. Hasan Engin Duran & Andrzej Cieślik, 2021. "The distribution of city sizes in Turkey: A failure of Zipf’s law due to concavity," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(5), pages 1702-1719, October.
    20. Cai Zongwu & Chen Linna & Fang Ying, 2012. "A New Forecasting Model for USD/CNY Exchange Rate," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-20, September.

    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.

    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: 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.