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Estimating Aggregate Autoregressive Processes When Only Macro Data are Available

Author

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  • Eric JONDEAU

    (University of Lausanne and Swiss Finance Institute)

  • Florian PELGRIN

    (EDHEC Business School and EDHEC Business School)

Abstract

The aggregation of individual random AR(1) models generally leads to an AR(infinity) process. We provide two consistent estimators of aggregate dynamics based on either a parametric regression or a minimum distance approach for use when only macro data are available. Notably, both estimators allow us to recover some moments of the cross-sectional distribution of the autoregressive parameter. Both estimators perform very well in our Monte-Carlo experiment, even with finite samples.

Suggested Citation

  • Eric JONDEAU & Florian PELGRIN, 2014. "Estimating Aggregate Autoregressive Processes When Only Macro Data are Available," Swiss Finance Institute Research Paper Series 14-43, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1443
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    References listed on IDEAS

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    Cited by:

    1. Bernard Candelpergher & Michel Miniconi & Florian Pelgrin, 2015. "Long-memory process and aggregation of AR(1) stochastic processes: A new characterization," Working Papers hal-01166527, HAL.

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    More about this item

    Keywords

    Autoregressive process; Aggregation; Heterogeneity;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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