nep-cmp New Economics Papers
on Computational Economics
Issue of 2007‒01‒14
eleven papers chosen by
Stan Miles
Thompson Rivers University

  1. Computational Intelligence in Exchange-Rate Forecasting By Andreas S. Andreou; George A. Zombanakis
  2. Global Optimization by Differential Evolution and Particle Swarm Methods: Evaluation on Some Benchmark Functions By Mishra, SK
  3. Spatio-Temporal Point Pattern Analysis Using Genetic Algorithms By Yorgos Photis; Yorgos Grekousis
  4. A solving tool for fuzzy quadratic optimal control problems By Silvio Giove; Paolo Bortot
  5. Macroeconomic Impact of Ageing Population in Scotland. A Computable General Equilibrium Analysis. By Katerina Lisenkova; Peter Mcgregor; Nikos Pappas; Kim Swales; Karen Turner; Robert Wright
  6. Economic Impacts of a New Road Network in San-En Region, Japan: A Spatial Computable General Equilibrium Model By Yuzuru Miyata; Hiroyuki Shibusawa; Yasuhiro Hirobata; Akira Ohgai
  7. The regional model for Mediterranean agriculture By Lobianco, Antonello; Roberto, Esposti
  8. Analysis of the impact of decoupling on two Mediterranean regions By Lobianco, Antonello; Roberto, Esposti
  9. SOCSol4L An improved MATLAB package for approximating the solution to a continuous-time stochastic optimal control problem By Azzato, Jeffrey; Krawczyk, Jacek
  10. NISOCSol an algorithm for approximating Markovian equilibria in dynamic games with coupled-constraints By Krawczyk, Jacek; Azzato, Jeffrey
  11. NIRA-3: An improved MATLAB package for finding Nash equilibria in infinite games By Krawczyk, Jacek; Zuccollo, James

  1. By: Andreas S. Andreou (University of Cyprus); George A. Zombanakis (Bank of Greece)
    Abstract: This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.
    Keywords: Exchange - rate forecasting, Neural networks
    JEL: C53
    Date: 2006–11
    URL: http://d.repec.org/n?u=RePEc:bog:wpaper:49&r=cmp
  2. By: Mishra, SK
    Abstract: In this paper we compare the performance of the Differential Evolution (DE) and the Repulsive Particle Swarm (RPS) methods of global optimization. To this end, seventy test functions have been chosen. Among these test functions, some are new while others are well known in the literature; some are unimodal, the others multi-modal; some are small in dimension (no. of variables, x in f(x)), while the others are large in dimension; some are algebraic polynomial equations, while the other are transcendental, etc. FORTRAN programs of DE and RPS have been appended. Among 70 functions, a few have been run for small as well as large dimensions. In total, 73 optimization exercises have been done. DE has succeeded in 63 cases while RPS has succeeded in 55 cases. In almost all cases, DE has converged faster and given much more accurate results. The convergence of RPS is much slower even for lesser stringency on accuracy. Some test functions have been hard for both the methods. These are: Zero-Sum (30D), Perm#1, Perm#2, Power and Bukin functions, Weierstrass, and Michalewicz functions. From what we find, one cannot reach at the definite conclusion that the DE performs better or worse than the RPS. None could assure a supremacy over the other. Each one faltered in some cases; each one succeeded in some others. However, DE is unquestionably faster, more accurate and more frequently successful than the RPS. It may be argued, nevertheless, that alternative choice of adjustable parameters could have yielded better results in either method’s case. The protagonists of either method could suggest that. Our purpose is not to join with the one or the other. We simply want to highlight that in certain cases they both succeed, in certain other case they both fail and each one has some selective preference over some particular type of surfaces. What is needed is to identify such structures and surfaces that suit a particular method most. It is needed that we find out some criteria to classify the problems that suit (or does not suit) a particular method. This classification will highlight the comparative advantages of using a particular method for dealing with a particular class of problems.
    Keywords: : Global optimization; Stochastic search; Repulsive particle swarm; Differential Evolution; Clustering algorithm; Simulated annealing; Genetic algorithm; Tabu search; Ant Colony algorithm; Monte Carlo method; Box algorithm; Nelder-Mead; Nonlinear programming; FORTRAN computer program; local optima; Benchmark; test functions
    JEL: C61 C63
    Date: 2006–10–05
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1005&r=cmp
  3. By: Yorgos Photis; Yorgos Grekousis
    Abstract: The effectiveness of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely, and efficient manner upon an event’s occurrence. A typical methodology to deal with such a task is through the application of an appropriate location - allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated to a specific spatial reference unit, appears to be spatially structured or semi – structured. Aiming to exploit the above incentive, the spatial tracing and analysis of emergency incidents is achieved through the utilisation of Artificial Intelligence. More specifically, in the proposed approach, each location problem is dealt with at two interacting levels. Firstly, spatio-temporal point pattern of demand is analysed over time by a new genetic algorithm. The proposed genetic algorithm interrelates sequential events formulating moving objects and as a result, every demand point pattern is correlated both to previous and following events. Secondly, the approach provides the ability to predict, by means of an artificial neural network, how the pattern of demand will evolve and thus the location of supplying centres and/or vehicles can be optimally defined. The proposed neural network is also optimised through genetic algorithms. The approach is applied to Athens Metropolitan Area and the data come from Fire Department’s records for the years 2003-2004.
    Date: 2006–08
    URL: http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa06p910&r=cmp
  4. By: Silvio Giove (Department of Applied Mathematics, University of Venice); Paolo Bortot (Department of Applied Mathematics, University of Venice)
    Abstract: In this paper we propose an iterative method to solve an optimal control problem, with fuzzy target and constraints. The algorithm is developed in such a way as to satisfy the target function and the constraints. The algorithm can be applied only if a method exists to solve a crisp parametric sub-problem obtained by the original one. This is the case for a quadratic-linear target function with linear constraints, for which some well established solvable methods exist for the crisp associated sub-problem. A numerical test confirmed the good convergence properties.
    Keywords: fuzzy, mathematical programming
    JEL: C6
    Date: 2006–11
    URL: http://d.repec.org/n?u=RePEc:vnm:wpaper:148&r=cmp
  5. By: Katerina Lisenkova; Peter Mcgregor; Nikos Pappas; Kim Swales; Karen Turner; Robert Wright
    Abstract: This paper combines a multi-period economic Computable General Equilibrium (CGE) modelling framework with a demographic model to analyse the macroeconomic impact of the projected demographic trends in Scotland. Demographic trends are defined by the existing fertility-mortality rates and the level of annual net-migration. We employ a combination of a demographic and a CGE simulation to track the impact of changes in demographic structure upon macroeconomic variables under different scenarios for annual migration. We find that positive net migration can cancel the expected negative impact upon the labour market of other demographic changes. (Pressure on wages, falling employment). However, the required size of the annual net-migration is far higher than the current trends. The policy implication suggested by the results is that active policies are needed to attract migrants. We nevertheless report results when varying fertility and mortality assumptions. The impact of varying those assumptions is rather small.
    Date: 2006–08
    URL: http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa06p432&r=cmp
  6. By: Yuzuru Miyata; Hiroyuki Shibusawa; Yasuhiro Hirobata; Akira Ohgai
    Abstract: In this paper, we aim to evaluate impacts of a new road network on the regional economy in San-en, Japan. San-en area is a boundary region between Aichi and Shizuoka Prefectures where the industrial sector is concentrated. The regional economy in San-en strongly depends on the regional transportation networks. Recently, a new road construction is planned in the region. The efficiency of road investment is expected for the regional economy. We construct a spatial computable general equilibrium model to evaluate the border economy. The spatial economic impacts of a new road construction are analyzed by the numerical simulation under several scenarios.
    Date: 2006–08
    URL: http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa06p810&r=cmp
  7. By: Lobianco, Antonello; Roberto, Esposti
    Abstract: AgriPoliS is a multi-agent mixed integer linear programming (MIP) model, spatially explicit, developed in C++ language and suitable for long-term sim- ulations of agricultural policies. Beyond the mixed integer programming core, the model main feature is the interaction among a set of heterogeneous farm- ers and between them and the environment in which they operate. In this paper we describe an extension of the model allowing AgriPoliS to deal with typical characters of the Mediterranean agriculture. In particular AgriPoliS was extended to allow a generic number of products and soil types, included perennial crops and products with quality differentiation. Furthermore, it can explicitly take into account irrigation.
    Keywords: Mediterranean Agriculture; Common Agricultural Policy; Agent-based Models.
    JEL: Q12 Q18
    Date: 2006–01
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1181&r=cmp
  8. By: Lobianco, Antonello; Roberto, Esposti
    Abstract: AgriPoliS is a multi-agent mixed integer linear programming (MIP) model, spatially explicit, developed in C++ language and suitable for long-term simulations of agricultural policies. Once extended to deal with typical characters of the Mediterranean agriculture, AgriPoliS is used in this paper to describe the implementation of alternative policy cenarios and to apply them to two regions located in Central and South Italy. Results suggest that the effects of decoupling policies in the Mediterranean agriculture, as implemented in the 2003 reform, are often dominated by effects of structural trends and only a "bond scheme" would substantially change the regional farm structures. In no scenario we observe remarkable agricultural land abandonment.
    Keywords: Mediterranean Agriculture; Common Agricultural Policy; Multi-Agent Model
    JEL: Q12 C61 Q18
    Date: 2006–09
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1182&r=cmp
  9. By: Azzato, Jeffrey; Krawczyk, Jacek
    Abstract: Computing the solution to a stochastic optimal control problem is difficult. A method of approximating a solution to a given stochastic optimal control problem using Markov chains was developed in [1]. This paper describes a suite of MATLAB functions implementing this method of approximating a solution to a given continuous stochastic optimal control problem.
    Keywords: Computational techniques; Economic software; Computational methods in stochastic optimal control; Computational economics; Approximating Markov decision chains
    JEL: C87 C63
    Date: 2006
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1179&r=cmp
  10. By: Krawczyk, Jacek; Azzato, Jeffrey
    Abstract: In this report, we outline a method for approximating a Markovian (or feedback-Nash) equilibrium of a dynamic game, possibly subject to coupled-constraints. We treat such a game as a "multiple" optimal control problem. A method for approximating a solution to a given optimal control problem via backward induction on Markov chains was developed in Krawczyk (2006). A Markovian equilibrium may be obtained numerically by adapting this backward induction approach to a stage Nikaido-Isoda function (described in Krawczyk & Zuccollo (2006)).
    Keywords: Computational techniques; Noncooperative games; Econometric software; Taxation; Water; Climate; Dynamic programming; Dynamic games; Applications of game theory; Environmental economics; Computational economics; Nikaido-Isoda function; Approximating Markov decision chains
    JEL: C87 C63 Q25 C72 E62
    Date: 2006
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1195&r=cmp
  11. By: Krawczyk, Jacek; Zuccollo, James
    Abstract: A powerful method for computing Nash equilibria in constrained, multi-player games is created when the relaxation algorithm and the Nikaido-Isoda function are used together in a suite of MATLAB routines. This paper updates the MATLAB suite described in \cite{Berridge97} by adapting them to MATLAB 7. The suite is now capable of solving both static and open-loop dynamic games. An example solving a coupled constraints game using the suite is provided.
    Keywords: Nikaido-Isoda function; Coupled constraints
    JEL: C63
    Date: 2006–12
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:1119&r=cmp

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