Complutense University Library

Automatic prediction and model selection

Marinucci, Massimiliano (2009) Automatic prediction and model selection. [Thesis]

[img] PDF
Restricted to Repository staff only

3MB
View download statistics for this eprint

==>>> Export to other formats

Abstract

This dissertation is about Automatic Model building and Prediction procedures that are useful to approximate and forecast the expected conditional mean of a stationary target variable. We review the theoretical foundations of model selection and compare the out-of-sample predictive ability of different automatic selection procedures, focusing especially on the the RETINA method proposed by P´erez-Amaral, Gallo & White (2003). A new software implementation of RETINA called RETINA Winpack is proposed. This software piece is designed for immediate use by non-specialist applied researchers. As an important advantage over the original RETINA implementation,it handles extreme observations and allows for distinctive treatment of categorical inputs. Using RETINA Winpack, we present an empirical application to Telecommunications demand using firm-level data. RETINA Winpack is proven to be useful for model specification search among hundred of candidate inputs and for finding suitable approximations that behave well out-of-sample in comparison with alternative linear baseline models. With the aim of increasing the flexibility of the RETINA method in order to deal with non-linearities in the target variable, a new method called RETINET is presented. It generalizes RETINA by expanding the functional approximating capabilities in a way which is similar to Artificial Neural Networks (ANN), by avoiding some of the difficulties related to their practical implementation. As an advantage over traditional ANN, RETINET’s specifications retain, to some extent, analytical interpretability. Based on two different simulation examples the method provides favorable evidence with respect to the out-of-sample forecasting ability provided by both simpler and/or more complex modeling alternatives. RETINET balances between a) Flexibility b) Parsimony c) Reverse engineering ability, and d) Computational speed. The proposed method is inspired by a Specific to General philosophy, going from the simple to the sophisticatedly simple, avoiding unnecessary complexity.


Item Type:Thesis
Additional Information:

Por indicaciones del autor, el texto completo se ha retirado con fecha 18 de Febrero de 2010.
(Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Departamento de Fundamentos del Análisis Económico II (Economía Cuantitativa), leída el 22-01-2008)

Directors:
DirectorsDirector email
Pérez Amaral, Teodosio
White, Halbert
Uncontrolled Keywords:Econometría
Subjects:Social sciences > Economics > Econometrics
ID Code:8103
Deposited On:29 Sep 2008 08:19
Last Modified:05 Sep 2014 12:44

Repository Staff Only: item control page