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Quantile regression using metaheuristic algorithms

Quantile regression using metaheuristic algorithms This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms - firefly and accelerated particle swarm optimisation - are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of various complications (e.g., non–differentiability, parameter restrictions, discontinuity, possible multimodality, etc.). Two comparative studies involving an autoregressive model and a conditional scale autoregressive conditional heteroscedasticity model, demonstrate the performance of metaheuristic algorithms relative to existing approaches. In addition, the paper presents an application to consumption behaviour in which the presence of constraints makes existing techniques difficult to implement, but metaheuristic algorithms are straightforward to apply. The findings indicate that marginal propensity to consume is highest in quarter 3 for each of the sample years. However, pre– and post–recession comparisons reveal interesting asymmetries in consumption behaviour. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2013.058498
Publisher site
See Article on Publisher Site

Abstract

This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms - firefly and accelerated particle swarm optimisation - are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of various complications (e.g., non–differentiability, parameter restrictions, discontinuity, possible multimodality, etc.). Two comparative studies involving an autoregressive model and a conditional scale autoregressive conditional heteroscedasticity model, demonstrate the performance of metaheuristic algorithms relative to existing approaches. In addition, the paper presents an application to consumption behaviour in which the presence of constraints makes existing techniques difficult to implement, but metaheuristic algorithms are straightforward to apply. The findings indicate that marginal propensity to consume is highest in quarter 3 for each of the sample years. However, pre– and post–recession comparisons reveal interesting asymmetries in consumption behaviour.

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2013

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