Seminario 23/02 presso l'Università di Salerno

[storti@unisa.it]

AVVISO DI SEMINARIO

Il giorno 23 febbraio 2006 alle ore 11.30, presso la Biblioteca del Dipartimento
di Scienze Economiche e Statistiche dell’Università di Salerno, Arie Preminger
(CORE, Université Catholique de Louvain la Neuve) terrà un seminario su

A model selection method for S-estimation

Per ulteriori informazioni è possibile rivolgersi a:
Giuseppe Storti
storti@unisa.it

Informazioni su come raggiungere il Campus sono reperibili al sito:

http://www.unisa.it/Salerno-Territorio/Collegamenti/index.asp





ABSTRACT

A model selection method for S-estimation

Arie Preminger  and Shinichi Sakata

(a) Center of Operations Research and Econometrics Université  Catholique, LLN,
Belgium.
(b) Department of Economics, University of British Columbia, Vancouver, BC,
Canada.


Abstract

In least squares, least absolute deviations estimation, and even generalized
M-estimation, outlying observations sometimes strongly influence the estimation
result, masking an important and interesting relationship existing in the
majority of observations. The S-estimators are a class of estimators that
overcome this difficulty by smoothly downweighting outliers in fitting
regression functions to data.

In this paper, we propose a method of model selection suitable in S-estimation.
The proposed method chooses a model to minimize a criterion named the penalized
S-scale criterion (PSC), which is decreasing in the sample S-scale of fitted
residuals and increasing in the number of parameters. We study the large sample
behavior of the PSC in nonlinear regression with dependent, heterogeneous data,
to establish sets of sufficient conditions for the PSC to consistently select
the model with the best fitting performance in terms of the population S-scale,
and the one with the minimum number of parameters if there are multiple best
performers. Our analysis allows for partial unidentifiability, which is often a
practically important possibility when selecting one among nonlinear regression
models. We offer two examples to demonstrate how our large sample results could
be applied in practice. We also conduct Monte Carlo simulations to verify that
the PSC performs as our large sample theory indicates, and assess the
reliability of the PSC method in comparison with the familiar Akaike and
Schwarz information criteria in  least squares estimation.

Keywords: Robust model selection; partial identification; law of the iterated
logarithm

JEL classification:  C22, C52



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