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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.
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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. David C. Wyld, 2010. "ASecond Life for organizations?: managing in the new, virtual world," Management Research Review, Emerald Group Publishing, vol. 33(6), pages 529-562, May.
    4. Concha Artola & Enrique Galán, 2012. "Tracking the future on the web: construction of leading indicators using internet searches," Occasional Papers 1203, Banco de España;Occasional Papers Homepage.
    5. 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.
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    Citations

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    Cited by:

    1. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    2. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    3. repec:eee:touman:v:46:y:2015:i:c:p:386-397 is not listed on IDEAS
    4. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    5. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

    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

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