IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/12995.html

Bayesian Variable Selection for Nowcasting Economic Time Series

In: Economic Analysis of the Digital Economy

Author

Listed:
  • Steven L. Scott
  • Hal R. Varian

Abstract

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:12995
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c12995.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    2. Concha Artola & Enrique Galán, 2012. "Tracking the future on the web: construction of leading indicators using internet searches," Occasional Papers 1203, Banco de España.
    3. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2009. "How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms," Working Papers in Economics 09/13, University of Canterbury, Department of Economics and Finance.
    4. David C. Wyld, 2010. "ASecond Lifefor organizations?: managing in the new, virtual world," Management Research Review, Emerald Group Publishing Limited, vol. 33(6), pages 529-562, May.
    5. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    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. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    2. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    3. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.
    4. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    5. Dittmann, Heidi & Kuchinke, Björn A., 2016. "Sharing Economy and Regulation," 27th European Regional ITS Conference, Cambridge (UK) 2016 148665, International Telecommunications Society (ITS).
    6. Ormandy, David & Ezratty, Véronique, 2012. "Health and thermal comfort: From WHO guidance to housing strategies," Energy Policy, Elsevier, vol. 49(C), pages 116-121.
    7. Obasan Kehinde A., 2014. "Impact of Human Resources Management on Entrepreneurship Development," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 10(1), pages 83-93, February.
    8. Erin M. Kearns, 2014. "The Study of Torture: Why It Persists, Why Perceptions of It are Malleable, and Why It is Difficult to Eradicate," Laws, MDPI, vol. 4(1), pages 1-15, December.
    9. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    10. Reto Foellmi & Stefan Legge & Lukas Schmid, 2016. "Do Professionals Get It Right? Limited Attention and Risk‐taking Behaviour," Economic Journal, Royal Economic Society, vol. 0(592), pages 724-755, May.
    11. Wenjie Wang & Qingfeng Liu, 2015. "Bootstrap-based Selection for Instrumental Variables Model," Economics Bulletin, AccessEcon, vol. 35(3), pages 1886-1896.
    12. Huang, Kaixing & Wang, Jinxia & Huang, Jikun & Findlay, Christopher, 2018. "The potential benefits of agricultural adaptation to warming in China in the long run," Environment and Development Economics, Cambridge University Press, vol. 23(2), pages 139-160, April.
    13. Russell Davidson & James G. MacKinnon, 2015. "Bootstrap Tests for Overidentification in Linear Regression Models," Econometrics, MDPI, vol. 3(4), pages 1-39, December.
    14. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    15. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    16. Fakhri J. Hasanov & Muhammad Javid & Frederick L. Joutz, 2022. "Saudi Non-Oil Exports before and after COVID-19: Historical Impacts of Determinants and Scenario Analysis," Sustainability, MDPI, vol. 14(4), pages 1-38, February.
    17. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    18. Bloemraad, Irene & Voss, Kim & Silva, Fabiana, 2014. "Framing the Immigrant Movement as about Rights, Family, or Economics: Which Appeals Resonate and for Whom?," Institute for Research on Labor and Employment, Working Paper Series qt3b32w33p, Institute of Industrial Relations, UC Berkeley.
    19. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    20. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:nbr:nberch:12995. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    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.