nep-cmp New Economics Papers
on Computational Economics
Issue of 2005‒05‒29
five papers chosen by
Stan Miles
York University

  1. How to Classify a Government? Can a Neural Network do it? By António Caleiro
  2. A Bi-Population Based Genetic Algorithm for the Resource-Constrained Project Scheduling Problem By Debels, Dieter; Vanhoucke, Mario
  3. The Poverty Concentration Implications of Housing Subsidies: A Cellular Automata Thought Experiment By Kevin Jewell
  4. Noname – A new quarterly model for Belgium By Philippe Jeanfils; Koen Burggraeve
  5. The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems By Christopher Carroll

  1. By: António Caleiro (Department of Economics, University of Évora)
    Abstract: An electoral cycle created by governments is a phenomenon that seems to characterise, at least in some particular occasions and/or circumstances, the democratic economies. As it is generally accepted, the short-run electorally-induced fluctuations prejudice the long-run welfare. Since the very first studies on the matter, some authors offered suggestions as to what should be done against this electorally-induced instability. A good alternative to the obvious proposal to increase the electoral period length is to consider that voters abandon a passive and naive behaviour and, instead, are willing to learn about government’s intentions. The electoral cycle literature has developed in two clearly distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. It is our view that an intermediate approach is more appropriate, i.e. one that considers learning voters, which are boundedly rational. In this sense, one may consider neural networks as learning mechanisms used by voters to perform a classification of the incumbent in order to distinguish opportunistic (electorally motivated) from benevolent (non-electorally motivated) behaviour of the government. The paper explores precisely the problem of how to classify a government showing in which, if so, circumstances a neural network, namely a perceptron, can resolve that problem.
    Keywords: Classification, Elections, Government, Neural Networks, Output Persistence, Perceptrons
    JEL: C45 D72 E32
    Date: 2005
    URL: http://d.repec.org/n?u=RePEc:evo:wpecon:9_2005&r=cmp
  2. By: Debels, Dieter; Vanhoucke, Mario
    Abstract: The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward local search scheduling technique. Comparative computational results reveal that this procedure can be considered as today’s best performing RCPSP heuristic. Note
    Date: 2005–05–26
    URL: http://d.repec.org/n?u=RePEc:vlg:vlgwps:2005-8&r=cmp
  3. By: Kevin Jewell
    Abstract: Looking at data from HUD’s low income housing tax credit database from 1987 to 2001, we examine how the US tax credit program has concentrated poverty in neighborhoods by offering advantages to developing low income housing projects in low income census tracts. We then use a simple Cellular Automata model to explore how alternative programs structures could impact economic diversity and poverty concentration. This model suggests that many widely dispersed fixed location affordable housing projects increase local economic diversity over alternative housing allocation rules. If policymakers wish align the Low Income Housing Tax Credit program with the goal of promoting economic diversity in our neighborhoods, they should restructure the bonus to reward to projects in areas without a concentration of subsidized housing.
    Keywords: low income housing tax credit; Residential Location; Simulation; segregation; cellular automata
    JEL: R14 R21 R31
    Date: 2005–05–22
    URL: http://d.repec.org/n?u=RePEc:wpa:wuwpur:0505009&r=cmp
  4. By: Philippe Jeanfils (National Bank of Belgium, Research Department); Koen Burggraeve (National Bank of Belgium, Research Department)
    Abstract: This paper gives an overview of the present version of the quarterly model for the Belgian economy built at the National Bank of Belgium (NBB). This model can provide quantitative input into the policy analysis and projection processes within a framework that has explicit micro-foundations and expectations. This new version is also compatible with the ESA95 national accounts. This model called Noname is relatively compact. The intertemporal optimisation problem of households and firms is subject to polynomial adjustment costs, which yields richer dynamic specifications than the more usual quadratic cost function. Other characteristics are: pricing-to-market and hence flexible mark-ups and incomplete pass-through, a CES production function with an elasticity of substitution between capital and labour below one, time-dependent wage contracting à la Dotsey, King and Wollman. Most of the equations taken individually have acceptable statistical properties and diagnostic simulations suggest that the impulse responses of the model to exogenous shocks are reasonable. Its structure allows simulations to be conducted under the assumption of rational expectations as well as under alternative expectations formations.
    Keywords: Econometric modelling, Pricing-to-market, CES production function, Wage bargaining, Polynomial adjustment costs, Rational expectations.
    JEL: C5 E2 E3 F41
    Date: 2005–05
    URL: http://d.repec.org/n?u=RePEc:nbb:reswpp:200505-2&r=cmp
  5. By: Christopher Carroll
    Abstract: This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided.
    Date: 2005–05
    URL: http://d.repec.org/n?u=RePEc:jhu:papers:520&r=cmp

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