Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability

Philippe de Peretti () and William Barnett
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Philippe de Peretti: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

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Abstract: In aggregation theory, the admissibility condition for clustering components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper introduces a new class of weak separability tests, which is seminonparametric. Such tests are both based on a necessary and sufficient condition and are fully stochastic, allowing to take into account measurement error. Simulations show that the tests perform well, even for large measurement errors.

Keywords: weak separability; seminonparametric test (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (7)

Published in Macroeconomic Dynamics, 2009, 13 (S2), pp.217-334. ⟨10.1017/S1365100509090300⟩

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Related works:
Journal Article: ADMISSIBLE CLUSTERING OF AGGREGATOR COMPONENTS: A NECESSARY AND SUFFICIENT STOCHASTIC SEMINONPARAMETRIC TEST FOR WEAK SEPARABILITY (2009)
Working Paper: Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability (2009)
Working Paper: Admissible Clustering of Aggregator Components: A Necessary and Sufficient Stochastic Semi-Nonparametric Test for Weak Separability (2009)
Working Paper: Admissible clustering of aggregator components: a necessary and sufficient stochastic semi-nonparametric test for weak separability (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-00646786

DOI: 10.1017/S1365100509090300

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