nep-cmp New Economics Papers
on Computational Economics
Issue of 2012‒10‒20
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. RunMyCode.org: a novel dissemination and collaboration platform for executing published computational results By Christophe Hurlin; Christophe Pérignon; Victoria Stodden
  2. Portfolio Selection Using Genetic Algorithm By sefiane, slimane; Benbouziane, Mohamed
  3. Lasso-type and Heuristic Strategies in Model Selection and Forecasting By Ivan Savin; Peter Winker
  4. On the initialization of adaptive learning algorithms: A review of methods and a new smoothing-based routine By Michele Berardi; Jaqueson K. Galimberti
  5. Macroeconomic Policy in DSGE and Agent-Based Models By Giorgio Fagiolo; Andrea Roventini
  6. Trade Integration in the CIS: Alternate Options, Economic Effects and Policy Implications for Belarus, Kazakhstan, Russia and Ukraine By Vasily Astrov; Peter Havlik; Olga Pindyuk
  7. DYNAMIC SYSTEM OPTIMAL ROUTING IN MULTIMODAL TRANSIT NETWORK By Tai-Yu Ma; Jean-Patrick Lebacque
  8. Le riforme dell'imposizione diretta sulle imprese italiane By Francesco Crespi; Antonio Di Majo; Maria Grazia Pazienza
  9. Distributed Learning in Hierarchical Networks By Hélène Le Cadre; Bedo Jean-Sébastien
  10. Selectivity, pulse fishing and endogenous lifespan in Beverton-Holt models By Da Rocha, José María; Antelo, Luis T.; Gutiérrez Huerta, María José
  11. Composing Fifth Species Counterpoint Music With Variable Neighborhood Search By Herremans D.; Sörensen K.
  12. A Semi-Markov Modulated Interest Rate Model By Guglielmo D'Amico; Raimondo Manca; Giovanni Salvi
  13. Strong random correlations in networks of heterogeneous agents By Imre Kondor; Istv\'an Csabai; G\'abor Papp; Enys Mones; G\'abor Czimbalmos; M\'at\'e Csaba S\'andor

  1. By: Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959); Victoria Stodden (Columbia University - Columbia University)
    Abstract: We believe computational science as practiced today suffers from a growing credibility gap - it is impossible toreplicate most of the computational results presented at conferences or published in papers today. We argue that this crisis can be addressed by the open availability of the code and data that generated the results, in other words practicing reproducible computational science. In this paper we present a new computational infrastructure called RunMyCode.org that is designed to support published articles by providing a dissemination platform for the code and data that generated the their results. Published articles are given a companion webpage on the RunMyCode.org website from which a visitor can both download the associated code and data, and execute the code in the cloud directly through the RunMyCode.org website. This permits results to be verified through the companion webpage or on a user's local system. RunMyCode.org also permits a user to upload their own data to the companion webpage to check the code by running it on novel datasets. Through the creation of "coder pages" for each contributor to RunMyCode.org, we seek to facilitate social network-like interaction. Descriptive information appears on each coder page, including demographic data and other companion pages to which they made contributions. In this paper we motivate the rationale and functionality of RunMyCode.org and outline a vision of its future.
    Keywords: reproducible research; reproducible computational science; dissemination platform; collaborative networks; cloud computing; executable papers; code sharing; data sharing; open science
    Date: 2012
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-00739233&r=cmp
  2. By: sefiane, slimane; Benbouziane, Mohamed
    Abstract: The selection of optimal portfolios is the central problem of financial investment decisions. Mathematically speaking, portfolio selection refers to the formulation of an objective function that determines the weights of the portfolio invested in each asset as to maximize return and minimize risk. This paper applies the method of genetic algorithm (GA) to obtain an optimal portfolio selection. However, the GA parameters are of great importance in the procedure of convergence of this algorithm towards the optimal solution such as crossover. While, a five stock portfolio example is used in this paper to illustrate the applicability and efficiency of genetic algorithm method, GA method can also be used however for a larger number of portfolio compositions. The results obtained confirm previous research studies about the validity and efficiency of genetic algorithm in selecting optimal portfolios.
    Keywords: portfolio optimization; objective function; artificial intelligence methods; genetic algorithm
    JEL: G11
    Date: 2012
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:41783&r=cmp
  3. By: Ivan Savin (DFG Research Training Program "The Economics of Innovative Change", Friedrich Schiller University Jena and Max Planck Institute of Economics); Peter Winker (Justus Liebig University Giessen, and Centre for European Economic Research, Mannheim)
    Abstract: Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this performance can be impaired by the only asymptotic consistency of the information criteria. The resulting discrete optimization problems exhibit a high computational complexity. Therefore, a heuristic optimization approach (Genetic Algorithm) is applied. The two strategies are compared by means of a Monte-Carlo simulation study together with an empirical application to leading business cycle indicators in Russia and Germany.
    Keywords: Adaptive Lasso, Elastic net, Forecasting, Genetic algorithms, Heuristic methods, Lasso, Model selection
    JEL: C51 C52 C53 C61 C63
    Date: 2012–10–11
    URL: http://d.repec.org/n?u=RePEc:jrp:jrpwrp:2012-055&r=cmp
  4. By: Michele Berardi; Jaqueson K. Galimberti
    Abstract: We provide a critical review on the methods previously adopted into the literature of learning and expectations in macroeconomics in order to initialize its underlying learning algorithms either for simulation or empirical purposes. We nd that none of these methods is able to pass the sieve of both criteria of coherence to the algorithm long run behavior and of feasibility within the data availability restrictions for macroeconomics. We then propose a smoothing-based initialization routine, and show through simulations that our method meets both those criteria in exchange for a higher computational cost. A simple empirical application is also presented to demonstrate the relevance of initialization for beginning-of-sample inferences.
    Date: 2012
    URL: http://d.repec.org/n?u=RePEc:man:cgbcrp:175&r=cmp
  5. By: Giorgio Fagiolo (Sant'Anna School of Advanced Studies, Pisa); Andrea Roventini (Department of Economics (University of Verona))
    Abstract: The Great Recession seems to be a natural experiment for macroeconomics showing the inadequacy of the predominant theoretical framework — the New Neoclassical Synthesis — grounded on the DSGE model. In this paper, we present a critical discussion of the theoretical, empirical and political-economy pitfalls of the DSGE-based approach to policy analysis. We suggest that a more fruitful research avenue to pursue is to explore alternative theoretical paradigms, which can escape the strong theoretical requirements of neoclassical models (e.g., equilibrium, rationality, representative agent, etc.). We briefly introduce one of the most successful alternative research projects – known in the literature as agent-based computational economics (ACE) – and we present the way it has been applied to policy analysis issues. We then provide a survey of agent-based models addressing macroeconomic policy issues. Finally, we conclude by discussing the methodological status of ACE, as well as the (many) problems it raises.
    Keywords: Economic Policy, Monetary and Fiscal Policies, New Neoclassical Synthesis, New Keynesian Models, DSGE Models, Agent-Based Computational Economics, Agent- Based Models, Great Recession, Crisis
    JEL: B41 B50 E32 E52
    Date: 2012–02
    URL: http://d.repec.org/n?u=RePEc:ver:wpaper:07/2012&r=cmp
  6. By: Vasily Astrov (The Vienna Institute for International Economic Studies, wiiw); Peter Havlik (The Vienna Institute for International Economic Studies, wiiw); Olga Pindyuk (The Vienna Institute for International Economic Studies, wiiw)
    Abstract: A functioning Belarus-Russia-Kazakhstan Customs Union (BRK-CU) would comprise the bulk of the FSU economy and represent a significant step towards an attempted re-integration of the FSU – even more so if Ukraine were also to join. There are still important structural differences in intra-regional compared to extra-regional trade of these countries, regarding exports in particular. The existing specialization patterns and comparative advantages may – apart from purely political considerations – provide some economic rationale for closer trade integration. Our difference-in-difference gravity-based estimates indicate that during the period 1999-2009 liberalization took place primarily in the trade of Belarus, Russia, Kazakhstan and Ukraine with third countries, whereas in their mutual trade barriers in many manufacturing and services sectors actually increased. The BRK-CU largely eliminated the remaining non-tariff barriers in mutual trade and, upon the adoption of a Common External Tariff (CET) in 2010, unified the participating countries’ trade policies vis-à-vis third countries. As a result of CET adoption, the average (un-weighted) level of protection declined by about 2 p.p. in Russia and 1.3 p.p. in Belarus, but increased by around 2.5 p.p. in Kazakhstan. Available estimates of the economic effects of the BRK-CU differ by a wide margin. Our computable general equilibrium (CGE) estimation results suggest that joining the BRK-CU might potentially bring net GDP losses to Ukraine. BRK-CU membership appears to bring net GDP and welfare losses also to Kazakhstan whereas Belarus and Russia benefit in terms of GDP and labour income growth. There seems to be little (economic) justification for Russia prompting Ukraine to join the BRK-CU. Ukraine, on the other hand, is likely to have a significant increase in GDP and real labour income after implementing the DCFTA with the EU.
    Keywords: foreign trade, integration, Customs Union, gravity and CGE modelling, Belarus, Kazakhstan, Russia, Ukraine
    JEL: C5 F1 F5 P3
    Date: 2012–09
    URL: http://d.repec.org/n?u=RePEc:wii:rpaper:rr:381&r=cmp
  7. By: Tai-Yu Ma (LET - Laboratoire d'économie des transports - CNRS : UMR5593 - Université Lumière - Lyon II - Ecole Nationale des Travaux Publics de l'Etat); Jean-Patrick Lebacque (IFSTTAR/GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée - IFSTTAR - Université Paris XII - Paris Est Créteil Val-de-Marne)
    Abstract: The system optimal routing problem has been widely studied for road network while it is less considered for public transit system. Traditional shortest-path-based multimodal itinerary guidance systems may deteriorate the system performance when the assigned lines become congested. For this issue, we formulate the dynamic system optimal routing model for multimodal transit system. The transit system is represented by a multilevel graph to explicitly simulate passenger flow and transit system operations. A solution algorithm based on the cross entropy method is proposed, and its performance is compared with the method of successive averages in static and dynamic cases. Numerical study on a simple multimodal transit network provides the basis for comparing the system optimal routing and user optimal routing under different congestion levels.
    Keywords: system optimal routing, multimodal, transit,
    Date: 2012–07–26
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00740347&r=cmp
  8. By: Francesco Crespi; Antonio Di Majo; Maria Grazia Pazienza (0164)
    Abstract: The aim of this essay is to analyse the evolution of the tax design for corporate taxation in Italy with specific reference to most recent reforms. A micro-simulation model applied to a large sample of Italian companies is built to estimate the effects of the introduction of the ACE in 2012. The analysis shows that the frequent attempts to modify the design of corporate taxation generated a high instability in this specific sector of the tax system rather than relevant effects in terms of economic growth. This result suggests that future tax changes should be more directly linked to capital accumulation by firms.
    Keywords: Tax Design, Corporate Taxation
    JEL: H25 H32 H87
    Date: 2012–10
    URL: http://d.repec.org/n?u=RePEc:rtr:wpaper:0164&r=cmp
  9. By: Hélène Le Cadre (LIMA - CEA, LIST, Laboratory of Information, Models and Learning - CEA : SACLAY); Bedo Jean-Sébastien (Orange/France-Télécom - Telecom Orange)
    Abstract: In this article, we propose distributed learning based approaches to study the evolution of a decentralized hierarchical system, an illustration of which is the smart grid. Smart grid management requires the control of non-renewable energy production and the inegration of renewable energies which might be highly unpredictable. Indeed, their production levels rely on uncontrolable factors such as sunshine, wind strength, etc. First, we derive optimal control strategies on the non-renewable energy productions and compare competitive learning algorithms to forecast the energy needs of the end users. Second, we introduce an online learning algorithm based on regret minimization enabling the agents to forecast the production of renewable energies. Additionally, we define organizations of the market promoting collaborative learning which generate higher performance for the whole smart grid than full competition.
    Keywords: Algorithmic Game Theory; Coalition; Distributed Learning; Regret
    Date: 2012–10–09
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-00740905&r=cmp
  10. By: Da Rocha, José María; Antelo, Luis T.; Gutiérrez Huerta, María José
    Abstract: Optimal management in a multi-cohort Beverton-Holt model with any number of age classes and imperfect selectivity is equivalent to finding the optimal fish lifespan by chosen fallow cycles. Optimal policy differs in two main ways from the optimal lifespan rule with perfect selectivity. First, weight gain is valued in terms of the whole population structure. Second, the cost of waiting is the interest rate adjusted for the increase in the pulse length. This point is especially relevant for assessing the role of selectivity. Imperfect selectivity reduces the optimal lifespan and the optimal pulse length. We illustrate our theoretical findings with a numerical example. Results obtained using global numerical methods select the optimal pulse length predicted by the optimal lifespan rule.
    Keywords: optimisation in age-structured models, pulse fishing
    JEL: O1
    Date: 2012
    URL: http://d.repec.org/n?u=RePEc:ehu:dfaeii:8768&r=cmp
  11. By: Herremans D.; Sörensen K.
    Abstract: In this paper, a variable neighborhood search (VNS) algorithm is developed and analyzed that can generate fth species counterpoint fragments. The existing species counterpoint rules are quantied and form the basis of the objective function used by the algorithm. The VNS developed in this research is a local search metaheuristic that starts from a randomly generated fragment and gradually improves this solution by changing one or two notes at a time. An in-depth statistical analysis reveals the signicance as well as the optimal settings of the parameters of the VNS. The algorithm has been implemented in a user-friendly software environment called Optimuse. Optimuse allows a user to input basic characteristics such as length, key and mode. Based on this input, a fth species counterpoint fragment is generated that can be edited and played back immediately. This work is the expansion of a previous paper by the authors in which rst species counterpoint music is composed by a similar VNS algorithm.
    Date: 2012–09
    URL: http://d.repec.org/n?u=RePEc:ant:wpaper:2012020&r=cmp
  12. By: Guglielmo D'Amico; Raimondo Manca; Giovanni Salvi
    Abstract: In this paper we propose a semi-Markov modulated model of interest rates. We assume that the switching process is a semi-Markov process with finite state space E and the modulated process is a diffusive process. We derive recursive equations for the higher order moments of the discount factor and we describe a Monte Carlo al- gorithm to execute simulations. The results are specialized to classical models as those by Vasicek, Hull and White and CIR with a semi-Markov modulation.
    Date: 2012–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1210.3164&r=cmp
  13. By: Imre Kondor; Istv\'an Csabai; G\'abor Papp; Enys Mones; G\'abor Czimbalmos; M\'at\'e Csaba S\'andor
    Abstract: Correlations and other collective phenomena in a schematic model of heterogeneous binary agents (individual spin-glass samples) are considered on the complete graph and also on 2d and 3d regular lattices. The system's stochastic dynamics is studied by numerical simulations. The dynamics is so slow that one can meaningfully speak of quasi-equilibrium states. Performing measurements of correlations in such a quasi-equilibrium state we find that they are random both as to their sign and absolute value, but on average they fall off very slowly with distance in all instances that we have studied. This means that the system is essentially non-local, small changes at one end may have a strong impact at the other. Correlations and other local quantities are extremely sensitive to the boundary conditions all across the system, although this sensitivity disappears upon averaging over the samples or partially averaging over the agents. The strong, random correlations tend to organize a large fraction of the agents into strongly correlated clusters that act together. If we think about this model as a distant metaphor of economic agents or bank networks, the systemic risk implications of this tendency are clear: any impact on even a single strongly correlated agent will spread, in an unforeseeable manner, to the whole system via the strong random correlations.
    Date: 2012–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1210.3324&r=cmp

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