nep-cmp New Economics Papers
on Computational Economics
Issue of 2008‒07‒30
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Evolutionary Finance By Igor V. Evstigneev; Thorsten Hens; Klaus Reiner Schenk-Hoppé
  2. Optimised Search Heuristic Combining Valid Inequalities and Tabu Search By Susana Fernandes; Helena Ramalhinho-Lourenço
  3. Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis By Birur, Dileep; Hertel, Thomas; Tyner, Wally
  4. Impact Analysis of Regional Knowledge Subsidy: a CGE Approach By Giorgio Garau; Patrizio Lecca
  5. (The Evolution of) Post-Secondary Education: A Computational Model and Experiments By Andreas Ortmann; Sergey Slobodyan

  1. By: Igor V. Evstigneev (Economic Studies, University of Manchester); Thorsten Hens (Swiss Banking Institute, University of Zurich); Klaus Reiner Schenk-Hoppé (Leeds University Business School and School of Mathematics, University of Leeds)
    Abstract: Evolutionary finance studies the dynamic interaction of investment strategies in financial markets. This market interaction generates a stochastic wealth dynamics on a heterogenous population of traders through the fluctuation of asset prices and their random payoffs. Asset prices are endogenously determined through short-term market clearing. Investors' portfolio choices are characterized by investment strategies which provide a descriptive model of decision behavior. The mathematical framework of these models is given by random dynamical systems. This chapter surveys the recent progress made by the authors in the theory and applications of evolutionary finance models. An introduction to and the motivation of the modeling approach is followed by a theoretical part which presents results on the market selection (and co-existence) of investment strategies, discusses the relation to the Kelly rule and implications for asset pricing theory, and introduces a continuous-time mathematical finance version. Applications are concerned with simulation studies of the market dynamics, empirical estimation of asset prices and their dynamics, and the evolution of investment strategies using genetic programming.
    Keywords: Evolutionary Finance, Wealth Dynamics, Market Interaction
    JEL: G11 C61 C62
    Date: 2008–05
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp0814&r=cmp
  2. By: Susana Fernandes; Helena Ramalhinho-Lourenço
    Abstract: This paper presents an Optimised Search Heuristic that combines a tabu search method with the verification of violated valid inequalities. The solution delivered by the tabu search is partially destroyed by a randomised greedy procedure, and then the valid inequalities are used to guide the reconstruction of a complete solution. An application of the new method to the Job-Shop Scheduling problem is presented.
    Keywords: Optimised Search Heuristic, Tabu Search, GRASP, Valid Inequalities, Job Shop Scheduling
    JEL: C61 M11
    Date: 2008–07
    URL: http://d.repec.org/n?u=RePEc:upf:upfgen:1100&r=cmp
  3. By: Birur, Dileep; Hertel, Thomas; Tyner, Wally
    Abstract: This paper introduces biofuels sectors as energy inputs into the GTAP data base and to the production and consumption structures of the GTAP-Energy model developed by Burniaux and Truong (2002), and further modified by McDougall and Golub (2008). We also incorporate Agro-ecological Zones (AEZs) for each of the land using sectors in line with Lee et al. (2005). The GTAP-E model with biofuels and AEZs offers a useful framework for analyzing the growing importance of biofuels for global changes in crop production, utilization, commodity prices, factor use, trade, land use change etc. We begin by validating the model over the 2001-2006 period. We focus on six main drivers of the biofuel boom: the hike in crude oil prices, replacement of MTBE by ethanol as a gasoline additive in the US, and subsidies for ethanol and biodiesel in the US and EU. Using this historical simulation, we calibrate the key elasticities of energy substitution between biofuels and petroleum products in each region. With these parameter settings in place, the model does a reasonably good job of predicting the share of feedstock in biofuels and related sectors in accordance with the historical evidence between 2001 and 2006 in the three major biofuel producing regions: US, EU, and Brazil. The results from the historical simulation reveal an increased production of feedstock with the replacement of acreage under other agricultural crops. As expected, the trade balance in oil sector improves for all the oil exporting regions, but it deteriorates at the aggregate for the agricultural sectors.
    Date: 2008
    URL: http://d.repec.org/n?u=RePEc:gta:workpp:2413&r=cmp
  4. By: Giorgio Garau; Patrizio Lecca
    Abstract: In this paper we present a computable general equilibrium model for the region of Sardinia for the purpose of evaluating the capacity of R&D policies to affect the long run rate of growth. The model incorporates induced technical change and allow for external knowledge spillovers. We find that the cost of R&D policies may change according to the wage setting prevailing into the region. Furthermore, the capacity of such a policy to generate knowledge spillovers from the international and interregional trade are quite modest. Indeed, the capacity of the regional system to internalize the technological level embody in the imported good is partially offset by an increase in internal efficiency lowering the share of import but increasing competitiveness.
    Keywords: Regional modelling, Induced Technical Change and R&D policies
    JEL: R13 R58
    Date: 2008
    URL: http://d.repec.org/n?u=RePEc:cns:cnscwp:200811&r=cmp
  5. By: Andreas Ortmann; Sergey Slobodyan
    Abstract: We propose a computational model to study (the evolution of) post-secondary education. “Consumers” who differ in quality shop around for desirable colleges or universities. “Firms” that differ in quality signal the availability of their services to desirable students. As long as they have capacity, colleges and universities make offers to students, who apply and qualify. Our model generalizes an earlier literature (namely, Vriend 1995) in an important dimension: quality, the model confirms key predictions of an analytical model that we also supply, and the model allows us to systematically explore the emergence of macro regularities and the consequences of various strategies that sellers might try. We supply three such exercises. In our baseline treatment we establish the dynamics and asymptotics of our generalized matching model. In the second treatment we study the consequences of opportunistic behavior of firms and thus demonstrate the usefulness of our computational laboratory for the analysis of this or similar questions (e.g., the problem of early admission). In the third treatment we equip some firms with economies of scale. This variant of our matching model is motivated by the entry of for-profit providers into low-quality segments of post-secondary education in the USA and by empirical evidence that, while traditional nonprofit or state-supported providers of higher education do not have significant economies of scale, the new breed of for-profit providers seems to capture economies in core functions such as curricular design, advertising, informational infrastructure, and regulatory compliance. Our computational results suggest that this new breed of providers is likely to continue to move up the quality ladder, albeit not necessarily all the way up to the top.
    Keywords: Post-secondary education, for-profit higher education providers, computational simulations.
    JEL: C63 D21 D83 I21 L15
    Date: 2008–06
    URL: http://d.repec.org/n?u=RePEc:cer:papers:wp355&r=cmp

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