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Estimating aggregate autoregressive processes when only macro data are available

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  • Jondeau, Eric
  • Pelgrin, Florian

Abstract

The aggregation of individual random AR(1) models generally leads to an AR(∞) 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

  • Jondeau, Eric & Pelgrin, Florian, 2014. "Estimating aggregate autoregressive processes when only macro data are available," Economics Letters, Elsevier, vol. 124(3), pages 341-347.
    • Handle: RePEc:eee:ecolet:v:124:y:2014:i:3:p:341-347
    DOI: 10.1016/j.econlet.2014.06.012
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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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