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on Computational Economics |
|
Issue of 2013‒06‒04
fourteen papers chosen by |
| By: | Terje Lensberg; Klaus Reiner Schenk-Hopp\'e |
| Abstract: | Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper we use a genetic programming algorithm to derive explicit formulas for near-optimal hedging strategies under nonlinear transaction costs. The strategies are valid over a large range of parameter values and require no information about the structure of the optimal hedging strategy. |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1305.6762&r=cmp |
| By: | Wieland, Volker; Cwik, Tobias J.; Müller, Gernot J.; Schmidt, Sebastian; Wolters, Maik H. |
| Abstract: | In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models' implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development. -- |
| Keywords: | Macroeconomic Models,Model Uncertainty,Policy Rules,Robustness,Monetary Policy,Fiscal Policy,Model Comparison |
| JEL: | E52 E58 E62 F41 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:201203&r=cmp |
| By: | Ivan Savin (DFG Research Training Program "The Economics of Innovative Change", Friedrich Schiller University Jena and the Max Planck Institute of Economics); Abiodun Egbetokun (DFG Research Training Program "The Economics of Innovative Change", Friedrich Schiller University Jena and the Max Planck Institute of Economics) |
| Abstract: | This paper extends the existing literature on strategic R&D alliances by presenting a model of innovation networks with endogenous absorptive capacity. The networks emerge as a result of bilateral cooperation over time between firms occupying different locations in the knowledge space. Social capital is ignored, and firms ally purely on the basis of knowledge considerations. Partner selection is driven largely by absorptive capacity which is itself influenced by cognitive distance and investment allocation between inventive and absorptive R&D. Cognitive distance between firms changes as a function of the intensity of cooperation and innovation. Within different knowledge regimes, we examine the structure of networks that emerge and how firms perform within such networks. Our model replicates some stylised empirical results on network structure and the contingent effects of network position on innovative performance. We find networks that exhibit small world properties which are generally robust to changes in the knowledge regime. Second, subject to the extent of knowledge spillovers, certain network strategies such as occupying brokerage positions or maximising accessibility to potential partners pay off. Third and most importantly, absorptive capacity plays an important role in network evolution: firms with different network strategies indeed differ in the build-up of absorptive capacity. |
| Keywords: | absorptive capacity, agent-based modeling, cognitive distance, dynam- ics, innovation, knowledge spillovers, networks |
| JEL: | C61 C63 D83 D85 L14 O33 |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:jrp:jrpwrp:2013-022&r=cmp |
| By: | Peters, M.; Ketter, W. |
| Abstract: | Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf. |
| Keywords: | preferences;software agents;assistive technologies;multi-task learning;autonomous decision-making |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:1765040144&r=cmp |
| By: | Schuster, Stephan |
| Abstract: | A constituent feature of adaptive complex system are non-linear feedback mechanisms between actors. This makes it often difficult to model and analyse them. Agent-based Computational Economics (ACE) uses computer simulation methods to represent such systems and analyse non-linear processes. The aim of this thesis is to explore ways of modelling adaptive agents in ACE models. Its major contribution is of a methodological nature. Artificial intelligence and machine learning methods are used to represent agents and learning processes in ACE models. In this work, a general reinforcement learning framework is developed and realised in a simulation system. This system is used to implement three models of increasing complexity in two different economic domains. One of these domains are iterative games in which agents meet repeatedly and interact. In an experimental labour market, it is shown how statistical discrimination can be generated simply by means of the learning algorithm used. The aim of this model is mainly to illustrate the features of the learning framework. The results resemble actual patterns of observed human behaviour in laboratory settings. The second model treats strategic network formation. The main contribution here is to show how agent-based modelling helps to analyse non-linearity that is introduced when assumptions of perfect information and full rationality are relaxed. The other domain has a Health Economics background. The aim here is to provide insights of how the approach might be useful in real-world applications. For this, a general model of primary care is developed, and the implications of different consumer behaviour (based on the learning features introduced before) analysed. |
| Keywords: | agent-based economics, reinforcement learning, statistical discrimination, health care market, network formation games |
| JEL: | C63 D85 I11 J71 Y40 |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:47201&r=cmp |
| By: | Eben Upton; William J. Nuttall |
| Abstract: | The United Kingdom has twice suffered major disruption as a result of fuel panics first in September 2000 coincident with a wave of fuel protests and more recently in March 2012 following politcal warnings of possible future supply chain disruption. In each case the disruption and economic consequences were serious. Fuel distribution is an example of a supply chain. Approaches to supply-chain planning based on linear programming are poorly suited to modelling non-equilibrium effects, while coarse-grained system dynamics models often fail to capture local phenomena which contribute to the evolution of global demand. In this Paper, we demonstrate that agent-based techniques offer a powerful framework for cosimulation of supply chains and consumers under conditions of transient demand. In the case of fuel panic crisis, we show that even a highly abstract model can reproduce a range of transient phenomena seen in the real world, and present a set of practical recommendations for policymakers faced with panic-buying. |
| Keywords: | Fuel Panics, Agent Based Simulation, Supply Chain |
| JEL: | C15 C63 H30 J48 L91 R40 |
| Date: | 2013–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1309&r=cmp |
| By: | Ketter, W.; Collins, J.; Reddy, P.; Weerdt, M.M. de |
| Abstract: | This is the specification for the Power Trading Agent Competition for 2013 (Power TAC 2013). Power TAC is a competitive simulation that models a “liberalized†retail electrical energy market, where competing business entities or “brokers†offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot.The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time†metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins. |
| Keywords: | power;portfolio management;sustainability;preferences;energy;trading agent competition;electronic commerce;autonomous agents;policy guidance;TAC |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:1765040138&r=cmp |
| By: | Michael McAleer (University of Canterbury); Felix Chan; Les Oxley |
| Abstract: | The papers in this special issue of Mathematics and Computers in Simulation cover the following topics. Improving judgmental adjustment of model-based forecasts, whether forecast updates are progressive, on a constrained mixture vector autoregressive model, whether all estimators are born equal. The empirical properties of some estimators of long memory, characterising trader manipulation in a limitorder driven market, measuring bias in a term-structure model of commodity prices through the comparison of simultaneous and sequential estimation, modeling tail credit risk using transition matrices, evaluation of the DPC-based inclusive payment system in Japan for cataract operations by a new model, the matching of lead underwriters and issuing firms in the Japanese corporate bond market, stochastic life table forecasting. A time-simultaneous fan chart application, adaptive survey designs for sampling rare and clustered populations, income distribution inequality, globalization, and innovation. A general equilibrium simulation, whether exchange rates affect consumer prices. A comparative analysis for Australia, China and India, the impacts of exchange rates on Australia's domestic and outbound travel markets, clean development mechanism in China. Regional distribution and prospects, design and implementation of a Web-based groundwater data management system, the impact of serial correlation on testing for structural change in binary choice model. Monte Carlo evidence, and coercive journal self citations, impact factor, journal influence and article influence. |
| Keywords: | Modeling, simulation, forecasting, time series models, trading, credit risk, empirical finance, health economics, sampling, groundwater systems, exchange rates, structural change, citations |
| JEL: | C15 C63 E27 E37 E47 F37 F47 |
| Date: | 2013–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:13/18&r=cmp |
| By: | Hanappi, Hardy |
| Abstract: | Traditional macroeconomics and agent-based simulation (ABS) seem to be two disjunctive worlds, two different sprachspiele in the sense of Wittgenstein. It is not just the fact that macroeconomics has a long and distinguished history that on top of more than 200 years of discourse has recently adopted a sophisticated dynamic mathematical framework, while ABS is still in its infancy and for outsiders looks more like an intellectual toy than a serious research tool. Both languages are tools and eventually both are aiming at the same object of investigation: political economy. Why they let their object appear differently certainly is due to the intrinsic properties of the two languages. As is the case with every tool, the properties of the tool are to some extent transferred to the results that can be achieved with the respective tool. What aggravates this split of work styles is the fact that two different large research communities are linked to the use of the two languages; and each member of such a community has built already a considerable human capital stock, which consists mainly of elements that belong to exactly one of the two languages. Any expedition into the use of the foreign language runs into danger to make a part of the own toolset look obsolete, and thus to lose hard earned human capital. The incentives for cooperation disappear. To ease the pains of disaggregated research, the aim of this paper is to improve mutual understanding, and to show how far evolutionary macroeconomic simulation can advance political economy by explaining traditional macroeconomics as a (sometimes implausible) special case of its own more general approach. On the other hand ABS researchers often are unaware of the rich interpretative and empirically oriented treasures that classical macroeconomics has in store. What at first sight looks to be easily transferred into an algorithm turns out to be a highly refined argument, which in turn challenges the skills of ABS modelers. The most promising route to follow in the future certainly will be to be versatile in both languages, to walk on both feet. This short paper should provide a modest first step towards this goal. |
| Keywords: | Macroeconomics, simulation, evolutionary economics |
| JEL: | E10 E11 E12 |
| Date: | 2013–03–31 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:47181&r=cmp |
| By: | Dismukes, Robert; Coble, Keith H.; Miller, Corey; O'Donoghue, Erik |
| Abstract: | Producers’ increased reliance on crop insurance has led to concerns about losses producers could incur that are not covered by crop insurance. In the current farm bill debate, several proposals that would be based on area (county) revenue and are intended to cover a portion of producers’ crop insurance deductibles, referred to as “shallow loss” programs, have been advanced. We analyze, using an empirically-based simulation model and a certainty equivalent criterion, how shallow loss coverages might affect optimal coverage levels of farm-level revenue insurance for a moderately risk-averse producer. Our analysis suggests that area-based revenue insurance designs have some potential for causing producers to reduce coverage levels for farm-level revenue insurance, though the marginal differences in the certainty equivalents are often relatively small on a percentage basis. |
| Keywords: | Agricultural Finance, Farm Management, |
| Date: | 2013–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea13:149545&r=cmp |
| By: | Benoît Mulkay; Jacques Mairesse |
| Abstract: | This article presents an econometric analysis of the direct effects of the R&D tax credit (RTC) on private R&D in France and proposes an ex ante evaluation of the major reform implemented in 2008. We first estimate an error correction model of a dynamic R&D demand function on a large panel data of R&D doing firms, obtaining a preferred estimate of -0.4 for the long run elasticity of the user cost of R&D capital. We then perform a micro-simulation of the effects of the 2008 RTC reform that shows that the implicit long run budget multiplier would be about 0.7. |
| JEL: | H25 H32 O32 |
| Date: | 2013–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:19073&r=cmp |
| By: | Hagspiel, Simeon (Energiewirtschaftliches Institut an der Universitaet zu Koeln); Jägemann, Cosima (Energiewirtschaftliches Institut an der Universitaet zu Koeln); Lindenberger, Dietmar (Energiewirtschaftliches Institut an der Universitaet zu Koeln); Brown, Tom (Energiewirtschaftliches Institut an der Universitaet zu Koeln); Cherevatskiy, Stanislav (Energiewirtschaftliches Institut an der Universitaet zu Koeln); Tröster, Eckehard (Energiewirtschaftliches Institut an der Universitaet zu Koeln) |
| Abstract: | Electricity market models, implemented as dynamic programming problems, have been applied widely to identify possible pathways towards a cost-optimal and low carbon electricity system. However, the joint optimization of generation and transmission remains challenging, mainly due to the fact that different characteristics and rules apply to commercial and physical exchanges of electricity in meshed networks. This paper presents a methodology that allows to optimize power generation and transmission infrastructures jointly through an iterative approach based on power transfer distribution factors (PTDFs). As PTDFs are linear representations of the physical load flow equations, they can be implemented in a linear programming environment suitable for large scale problems. The algorithm iteratively updates PTDFs when grid infrastructures are modifi ed due to cost-optimal extension and thus yields an optimal solution with a consistent representation of physical load flows. The method is first demonstrated on a simpli fied three-node model where it is found to be robust and convergent. It is then applied to the European power system in order to fi nd its cost-optimal development under the prescription of strongly decreasing CO2 emissions until 2050. |
| Keywords: | Power system planning; Power generation and transmission; Iterative linear optimization; PTDF; Electricity market model; Power flow model; Flow-based market coupling |
| JEL: | C61 H54 L94 Q40 |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:ris:ewikln:2013_009&r=cmp |
| By: | alain Jousten; Mathieu Lefebvre |
| Abstract: | The paper studies retirement behavior of wage-earners in Belgium for the first time using rich survey data to explore retirement incentives as faced by individuals. Specifically, we use SHARE data to estimate a model à la Stock and Wise (1990). Exploring the longitudinal nature of SHARELIFE, we construct measures of financial and non financial incentive. Our analysis explicitly takes into account the different take up rates of the various early retirement exit paths across time and ages. The results show that financial incentives play a strong role. Health and education also matter, as does regional variation though the latter in an unexpected way. A set of policy simulations illustrate the scope and also the limits associated with selective parametric reforms |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:rpp:wpaper:1303&r=cmp |
| By: | Yonatan Ben-Shalom; David Stapleton |
| Keywords: | Medicare Beneficiaries, Disability, Claims-Based Algorithms, CEDR |
| JEL: | I J |
| Date: | 2013–03–11 |
| URL: | http://d.repec.org/n?u=RePEc:mpr:mprres:7767&r=cmp |