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Joint Forecast Combination of Macroeconomic Aggregates and Their Components

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  • Cobb, Marcus P A

Abstract

This paper presents a framework that extends forecast combination to include an aggregate and its components in the same process. This is done with the objective of increasing aggregate forecasting accuracy by using relevant disaggregate information and increasing disaggregate forecasting accuracy by providing a binding context for the component’s forecasts. The method relies on acknowledging that underlying a composite index is a well defined structure and its outcome is a fully consistent forecasting scenario. This is particularly relevant for people that are interested in certain components or that have to provide support for a particular aggregate assessment. In an empirical application with GDP data from France, Germany and the United Kingdom we find that the outcome of the combination method shows equal aggregate accuracy to that of equivalent traditional combination methods and a disaggregate accuracy similar or better to that of the best single models.

Suggested Citation

  • Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:76556
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    References listed on IDEAS

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    Cited by:

    1. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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