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on Computational Economics |
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Issue of 2017‒02‒26
ten papers chosen by |
| By: | Abrell, Jan; Rausch, Sebastian |
| Abstract: | This paper analyzes hybrid emissions trading systems (ETS) under partitioned environmental regulation when firms’ abatement costs and future emissions are uncertain. We show that hybrid policies that introduce bounds on the price or the quantity of abatement provide a way to hedge against differences in marginal abatement costs across partitions. Price bounds are more efficient than abatement bounds as they also use information on firms’ abatement technologies while abatement bounds can only address emissions uncertainty. Using a numerical stochastic optimization model with equilibrium constraints for the European carbon market, we find that introducing hybrid policies in EU ETS reduces expected excess abatement costs of achieving targeted emissions reductions under EU climate policy by up to 89 percent. We also find that under partitioned regulation there is a high likelihood for hybrid policies to yield sizeable ex-post cost reductions. |
| JEL: | H23 Q54 C63 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc16:145726&r=cmp |
| By: | Dalmeijer, K.; Spliet, R. |
| Abstract: | This paper presents a branch-and-cut algorithm for the Time Window Assignment Vehicle Routing Problem (TWAVRP), the problem of assigning time windows for delivery before demand volume becomes known. A novel set of valid inequalities, the precedence inequalities, is introduced and multiple separation heuristics are presented. In our numerical experiments the branch-and-cut algorithm is 3.8 times faster when separating precedence inequalities. Furthermore, in our experiments, the branch-and-cut algorithm is 193.9 times faster than the best known algorithm in the literature. Finally, using our algorithm, instances of the TWAVRP are solved which are larger than the small scale instances previously presented in the literature. |
| Keywords: | Vehicle Routing, Time Window Assignment, Precedence Inequalities, 90B06 (Transportation), 90C11 (Mixed integer programming), 90C57 (Branch-and-cut) |
| Date: | 2016–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:97802&r=cmp |
| By: | Aykut Mert Yakut (Postdoctoral Research Fellow, Department of Economics, Middle East Technical University, Ankara, Turkey); Ebru Voyvoda (Department of Economics, Middle East Technical University, Ankara, Turkey) |
| Abstract: | This study focuses on the effects of public policies on the size distribution of income in Turkey. To this end, an intertemporal dynamic equilibrium model with heterogeneous agents in a small open economy framework is constructed. This study serves several extensions to the literature via its algebraic structure and the calibration process in which various micro-level data sets are utilized. The results reveal that, in line with the previous findings of the literature, increasing budget allocations to unilateral social transfer programs has no significant effect on the size distribution of income and has adverse effects on the labor market decisions of relatively poor laborers. On the contrary, subsidizing the cost of labor has positive impacts on labor supplies and the size distribution of income improves in favor of relatively poor households. |
| Keywords: | Income distribution, Redistributive policies, Internal migration, Intertemporal CGE |
| JEL: | D33 D58 D91 D92 H23 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:met:wpaper:1703&r=cmp |
| By: | Ketter, W.; Collins, J.; de Weerdt, M.M. |
| Abstract: | This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). 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; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. 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 approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. 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. Real-time balancing of supply and demand is managed by 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. Changes for 2017 are focused on a more realistic wholesale market, reducing the market power of brokers by making the simulation scenario into a relatively small part of a larger market, and are highlighted by change bars in the margins. See Section 5.3 for details. |
| Keywords: | Autonomous Agents, Electronic Commerce, Energy, Preferences, Portfolio Management, Power, Policy Guidance, Sustainability, Trading Agent Competition |
| Date: | 2017–02–13 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:96687&r=cmp |
| By: | J. Idier; T. Piquard |
| Abstract: | We propose in this paper a simulation framework of pandemic in financial system composed of banks, asset markets and interbank markets. This framework aims at complementing the usual stress-test strategies that evaluate the impact of shocks on individual balance-sheets without taking into account the interactions between several components of the financial system. We build on the network model of Gourieroux, Heam, and Monfort (2012) for the banking system, adding some asset market channels as in Greenwood, Landier, and Thesmar (2015) and interbank markets characterized by collateralized debt and margin calls. We show that rather small shocks can be amplified and destabilize the entire financial system. In our framework, the fact that the system enters in an adverse situation comes from first round losses amplification triggered by asset depreciation, interbank contraction and bank failures in chain. From our simulations, we explain how the different channels of transmission play a role in weakening the financial system, and measure the extent to which each channel could make banks more vulnerable. |
| Keywords: | Bank network, systemic risk, contagion, stress-testing |
| JEL: | E52 E44 G12 C58 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:621&r=cmp |
| By: | Jon Kleinberg; Himabindu Lakkaraju; Jure Leskovec; Jens Ludwig; Sendhil Mullainathan |
| Abstract: | We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial—at home or in jail. By law, this decision hinges on the judge’s prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges’ decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to ‘noise’ as if it were signal. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. |
| JEL: | C01 C54 C55 D8 H0 K0 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23180&r=cmp |
| By: | Tesfatsion, Leigh |
| Abstract: | Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these properties imply real-world economies are locally-constructive sequential games. This study discusses a modeling approach, agent-based computational economics (ACE), that permits researchers to study economic systems from this point of view. ACE modeling principles and objectives are first concisely presented. The remainder of the study then highlights challenging issues and edgier explorations that ACE researchers are currently pursuing. |
| Date: | 2017–02–18 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genstf:201702180800001022&r=cmp |
| By: | Kumagai, Satoru; Isono, Ikumo; Keola, Souknilanh; Hayakawa, Kazunobu; Gokan, Toshitaka; Tsubota, Kenmei |
| Abstract: | The impact of connecting Kashgar, Trougart, Uzgen, and Karasuu and facilitating customs at the national border between China and Kyrgyzstan are examined by using IDE-GSM (Institute of Developing Economies, JETRO Geographical Simulation Model). We found that the railway connection has a positive impact in southern Kyrgyzstan and a negative impact in regions of northern Kyrgyzstan, neither of which are the capital city of Kyrgyzstan. |
| Keywords: | Economic geography, Railway, China(AECC), Kyrgyzstan(AZKG) |
| JEL: | R12 R13 R42 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:jet:dpaper:dpaper630&r=cmp |
| By: | Bonnet, Céline; Schain, Jan Philip |
| Abstract: | In this article, we extend the literature on merger simulation models by incorporating its potential synergy gains into structural econometric analysis. We present a three-step integrated approach. We estimate a structural demand and supply model, as in Bonnet and Dubois (2010). This model allows us to recover the marginal cost of each differentiated product. Then we estimate potential efficiency gains using the Data Envelopment Analysis approach of Bogetoft and Wang (2005), and some assumptions about exogenous cost shifters. In the last step, we simulate the new price equilibrium post merger taking into account synergy gains, and derive price and welfare effects. We use a homescan dataset of dairy dessert purchases in France, and show that for two of the three mergers considered, synergy gains could offset the upward pressure on prices post. Some mergers could then be considered as not harmful for consumers. |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:31503&r=cmp |
| By: | Mazzocchetti, Andrea; Raberto, Marco; Teglio, Andrea; Cincotti, Silvano |
| Abstract: | We study the effects of loans and mortgages securitisation on business cycles by using a large-scale agent-based stock-flow consistent macroeconomic model and simulator, that we enriched by including a financial vehicle corporation (FVC), that buys loans and mortgages from banks and issues ABSs and MBSs, and a mutual fund, that invests both in ABSs and MBSs. Households own the equity of the mutual fund in the form of equity shares. By means of securitisation, banks conduct regulatory capital arbitrage and reduce risk weighted assets in their balance sheet, in order to lend more loans and mortgages. Results show that different levels of securitisation propensity are able to affect credit and business cycles in different manners. On one side, securitisation increases banks lending activity, influencing positively investment and consumption. On the onther side, the increased amount of credit amplifies the negative shocks, due to higher loans write-offs probability, triggered by the boosted leding activity. Firms' bankruptcies impact the equity of banks, affecting their ability to grant new loans to consumption goods producers (CGPs), which need credit for their production activity, and mortgages to households, which are not able to purchase housing units. CGPs soon go bankrupt and households see their capital income reduced. The predominance of one effect on the other depends on the level of securitisation propensity and the time span considered. |
| Keywords: | Securitisation; Business Cycle; Financial Regulation; Agent-Based Macroeconomics |
| JEL: | C63 E32 G23 |
| Date: | 2017–02–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:76760&r=cmp |