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Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?

Author

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  • Jing Zeng

    (Department of Economics, University of Konstanz, Germany)

Abstract

Including disaggregate variables or using information extracted from the disaggregate variables into a forecasting model for an economic aggregate may improve the forecasting accuracy. In this paper we suggest to use the boosting method to select the disaggregate variables which are most helpful in predicting an aggregate of interest. We conduct a simulation study to investigate the variable selection ability of this method. To assess the forecasting performance a recursive pseudo-out-of-sample forecasting experiment for six key Euro area macroeconomic variables is conducted. The results suggest that using boosting to select relevant predictors is a feasible and competitive approach in forecasting an aggregate.

Suggested Citation

  • Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
  • Handle: RePEc:knz:dpteco:1420
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    File URL: http://www.uni-konstanz.de/FuF/wiwi/workingpaperseries/WP_20_JingZeng_2014.pdf
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    References listed on IDEAS

    as
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    Cited by:

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    More about this item

    Keywords

    aggregation; macroeconomic forecasting; componentwise boosting; factor analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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