Efficient estimators: the use of neural networks to construct pseudo panels

Marie Cottrell () and Patrice Gaubert
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Marie Cottrell: SAMOS - Statistique Appliquée et MOdélisation Stochastique - UP1 - Université Paris 1 Panthéon-Sorbonne, MATISSE - UMR 8595 - Modélisation Appliquée, Trajectoires Institutionnelles et Stratégies Socio-Économiques - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: Pseudo panels constituted with repeated cross-sections are good substitutes to true panel data. But individuals grouped in a cohort are not the same for successive periods, and it results in a measurement error and inconsistent estimators. The solution is to constitute cohorts of large numbers of individuals but as homogeneous as possible. This paper explains a new way to do this: by using a self-organizing map, whose properties are well suited to achieve these objectives. It is applied to a set of Canadian surveys, in order to estimate income elasticities for 18 consumption functions..

Keywords: Pseudo panels; self-organizing maps (search for similar items in EconPapers)
Date: 2003
Note: View the original document on HAL open archive server: https://hal.science/hal-00122817
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Citations: View citations in EconPapers (3)

Published in 2003, pp.331-339

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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:hal-00122817

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