Title: Quantile regression using metaheuristic algorithms

Authors: Mohammad Arshad Rahman

Addresses: Department of Humanities and Social Sciences, Indian Institute of Technology Kanpur, Kanpur 208016, India

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.

Keywords: quantile regression; firefly algorithm; particle swarm optimisation; accelerated PSO; conditional scale ARCH model; consumption behaviour; economic recession; metaheuristics; nonlinear models; linear models; econometrics; autoregressive models; modelling; constraints.

DOI: 10.1504/IJCEE.2013.058498

International Journal of Computational Economics and Econometrics, 2013 Vol.3 No.3/4, pp.205 - 233

Received: 22 Feb 2013
Accepted: 03 Oct 2013

Published online: 31 Dec 2013 *

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