nep-cmp New Economics Papers
on Computational Economics
Issue of 2007‒02‒10
four papers chosen by
Stan Miles
Thompson Rivers University

  1. A Data-Driven Optimization Heuristic for Downside Risk Minimization By Manfred Gilli; Evis Këllezi; Hilda Hysi
  2. Heuristics for the single machine scheduling problem with quadratic earliness and tardiness penalties By Jorge M. S. Valente; Rui A. F. S. Alves
  3. A micro-meso-macro perspective on the methodology of evolutionary economics: integrating history, simulation and econometrics By Prof John Foster
  4. USING THE EU-SILC FOR POLICY SIMULATION: PROSPECTS, SOME LIMITATIONS AND SUGGESTIONS By Francesco Figari; Levy H; Sutherland H

  1. By: Manfred Gilli (University of Geneva); Evis Këllezi (Mirabaud & cie); Hilda Hysi (University of Geneva - Department of Econometrics)
    Abstract: In practical portfolio choice models risk is often defined as VaR, expected short-fall, maximum loss, Omega function, etc. and is computed from simulated future scenarios of the portfolio value. It is well known that the minimization of these functions can not, in general, be performed with standard methods. We present a multi-purpose data-driven optimization heuristic capable to deal efficiently with a variety of risk functions and practical constraints on the positions in the portfolio. The efficiency and robustness of the heuristic is illustrated by solving a collection of real world portfolio optimization problems using different risk functions such as VaR, expected shortfall, maximum loss and Omega function with the same algorithm.
    Keywords: Portfolio optimization, Heuristic optimization, Threshold accepting, Downside risk
    JEL: C61 C63 G11 G32
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2&r=cmp
  2. By: Jorge M. S. Valente (LIACC/NIAAD, Faculdade de Economia, Universidade do Porto, Portugal); Rui A. F. S. Alves (Faculdade de Economia, Universidade do Porto, Portugal)
    Abstract: In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose several dispatching heuristics, and analyse their performance on a wide range of instances. The heuristics include simple and widely used scheduling rules, as well as adaptations of those rules to a quadratic objective function. We also propose heuristic procedures that specifically address both the earliness and the tardiness penalties, as well as the quadratic cost function. Several improvement procedures were also analysed. These procedures are applied as an improvement step, once the heuristics have generated a schedule. The computational experiments show that the best results are provided by the heuristics that explicitly consider both early and tardy costs, and the quadratic objective function. Therefore, it is indeed important to specifically address the quadratic feature of the cost function, instead of simply using procedures originally developed for a linear objective function. The heuristics are quite fast, and are capable of quickly solving even very large instances. The use of an improvement step is recommended, since it usually improves the solution quality with little additional computational effort.
    Keywords: scheduling, single machine, early/tardy, quadratic penalties, dispatching rules
    Date: 2007–02
    URL: http://d.repec.org/n?u=RePEc:por:fepwps:236&r=cmp
  3. By: Prof John Foster (School of Economics, The University of Queensland)
    Abstract: Applied economics has long been dominated by multiple regression techniques. In this regard, econometrics has tended to have a narrower focus than, for example, psychometrics in psychology. Over the last two decades, the simulation and calibration approach to modeling has become more popular as an alternative to traditional econometric strategies. However, in contrast to the well-developed methodologies that now exist in econometrics, simulation/calibration remains exploratory and provisional, both as an explanatory and as a predictive modelling technique although clear progress has recently been made in this regard (see Brenner and Werker (2006)). In this paper, we suggest an approach that can usefully integrate both of these modelling strategies into a coherent evolutionary economic methodology.
    Date: 2007
    URL: http://d.repec.org/n?u=RePEc:qld:uq2004:343&r=cmp
  4. By: Francesco Figari (Institute for Social & Economic Research); Levy H (Institute for Social & Economic Research); Sutherland H (Institute for Social & Economic Research)
    Abstract: We explore the prospects for using the EU-SILC as the underlying micro-database for policy simulation across the EU. In particular we consider the issues to be addressed, and the advantages arising, from building a database from the EUSILC for the EU tax-benefit model, EUROMOD. In order to identify the issues and illustrate their importance a trial database for Spain is constructed. It is used within EUROMOD to calculate some selected social indicators as well as indicators of work incentives and the effects of fiscal drag in Spain between 2003 and 2006. We conclude that, although transforming the EU-SILC into a database for EUROMOD would require a significant amount of effort, this is likely to be worthwhile because of the consequential improvements in comparability across countries, efficiency in developing and maintaining the model for many countries and simplification of access arrangements. We therefore offer some suggestions for how to improve the User Database for this purpose.
    Keywords: EU-SILC, European Union; Microsimulation
    JEL: C81 C88 I32
    Date: 2007–01
    URL: http://d.repec.org/n?u=RePEc:ese:emodwp:em1/07&r=cmp

This nep-cmp issue is ©2007 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.