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A Bayesian Classification Approach To Monetary Aggregation

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  • Serletis, Apostolos

Abstract

In this article we use Bayesian classification and finite mixture models to extract information from the MSI database (maintained by the Federal Reserve Bank of St. Louis) and construct a new set of non-nested monetary aggregates (under the Divisia aggregation procedure) based on statistical similarities and multidimensional structures. We also use recent advances in the fields of applied econometrics, dynamical systems theory, and statistical physics to investigate the relationship between the new money measures and economic activity. The empirical results offer practical evidence in favor of this approach to monetary aggregation.

Suggested Citation

  • Serletis, Apostolos, 2009. "A Bayesian Classification Approach To Monetary Aggregation," Macroeconomic Dynamics, Cambridge University Press, vol. 13(2), pages 200-219, April.
    • Handle: RePEc:cup:macdyn:v:13:y:2009:i:02:p:200-219_08
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    Cited by:

    1. Ali Jadidzadeh & Apostolos Serletis, 2019. "The Demand for Assets and Optimal Monetary Aggregation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(4), pages 929-952, June.

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