nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒02‒05
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Large Multiple Neighborhood Search for the Clustered Vehicle-Routing Problem By Timo Hintsch; Stefan Irnich
  2. Cost-effectiveness and incidence of renewable energy promotion in Germany By Böhringer, Christoph; Landis, Florian; Tovar Reaños, Miguel Angel
  3. Computational aspects of assigning agents to a line By Haris Aziz; Jens L. Hougaard; Juan D. Moreno-Ternero; Lars P. Osterdal
  4. The market resources method for solving dynamic optimization problems By Martinez-Garcia, Enrique; Kabukcuoglu, Ayse
  5. Scenarios for potential macroeconomic impact of Brexit on Hungary By László Békési; Zsolt Kovalszky; Tímea Várnai
  6. Simulated Western Kentucky Grain Farm Cash Flows, Working Capital Erosion, and Evaluation of Risk Management Tools to Manage these Risks By Davis, Todd; Mark, Tyler; Shepherd, Jonathan
  7. Long Term Winter Stocker Profitability By Benavides, Justin; Martinez, Charley; Anderson, David; Roquette, Monte
  8. Statistical Approximation of High-Dimensional Climate Models By Alena Miftakhova; Kenneth L. Judd; Thomas S. Lontzek; Karl Schmedders
  9. The sensitivity of VPIN to the choice of trade classification algorithm By Pöppe, T.; Moos, S.; Schiereck, D.
  10. Spectral ranking using seriation By Fajwel Fogel; Alexandre d'Aspremont; Milan Vojnovic

  1. By: Timo Hintsch (Johannes Gutenberg University Mainz); Stefan Irnich (Johannes Gutenberg University Mainz)
    Abstract: The clustered vehicle-routing problem (CluVRP) is a variant of the classical capacitated vehicle-routing problem (CVRP) in which customers are partitioned into clusters, and it is assumed that each cluster must have been served completely before the next cluster is served. This decomposes the problem into three subproblems, i.e., the assignment of clusters to routes, the routing inside each cluster, and the sequencing of the clusters in the routes. The second task requires the solution of several Hamiltonian path problems, one for each possibility to route through the cluster. We pre-compute the Hamiltonian paths for every pair of customers of each cluster. We present a large multiple neighborhood search (LMNS) which makes use of multiple cluster destroy and repair operators and a variable-neighborhood descent (VND) for postoptimization. The VND is based on classical neighborhoods such as relocate, 2-opt, and swap all working on the cluster level and a generalization of the Balas-Simonetti neighborhood modifying simultaneously the intra-cluster routings and the sequence of clusters in a route. Computational results with our new approach compare favorably to existing approaches from the literature.
    Keywords: Vehicle Routing, Clustered Vehicle Routing, Large Neighborhood Search
    Date: 2017–01–23
    URL: http://d.repec.org/n?u=RePEc:jgu:wpaper:1701&r=cmp
  2. By: Böhringer, Christoph; Landis, Florian; Tovar Reaños, Miguel Angel
    Abstract: Over the last decade Germany has boosted renewable energy in power production by means of massive subsidies. The flip side are very high electricity prices which raises concerns that the transition cost towards a renewable energy system will be mainly borne by poor households. In this paper, we combine computable general equilibrium and microsimulation analysis to investigate the cost-effectiveness and incidence of Germany's renewable energy promotion. We find that the regressive effects of renewable energy promotion could be ameliorated by alternative subsidy financing mechanisms which achieve the same level of electricity generation from renewable energy sources.
    Keywords: renewable energy policy,feed-in tariffs,CGE,microsimulation
    JEL: Q42 H23 C63
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:zbw:zewdip:17004&r=cmp
  3. By: Haris Aziz (University of New South Wales, Australia); Jens L. Hougaard (Department of Food and Resource Economics, University of Copenhagen); Juan D. Moreno-Ternero (Department of Economics, Universidad Pablo de Olavide; CORE, Université catholique de Louvain); Lars P. Osterdal (Department of Business and Economics, University of Southern Denmark)
    Abstract: We consider the problem of assigning agents to slots on a line, where only one agent can be served at a slot and each agent prefers to be served as close as possible to his target. We introduce a general approach to compute aggregate gap-minimizing assignments, as well as gap-egalitarian assignments. The approach relies on an algorithm which is shown to be faster than general purpose algorithms for the assignment problem. We also extend the approach to probabilistic assignments and explore the computational features of existing, as well as new, methods for this setting.
    Keywords: Random assignment, congested facility, aggregate gap minimization, gap-egalitarian assignments, computational speed.
    JEL: C78 D61 D63
    Date: 2017–03
    URL: http://d.repec.org/n?u=RePEc:pab:wpaper:17.03&r=cmp
  4. By: Martinez-Garcia, Enrique (Federal Reserve Bank of Dallas); Kabukcuoglu, Ayse (Koç University)
    Abstract: We introduce the market resources method (MRM) for solving dynamic optimization problems. MRM extends Carroll’s (2006) endogenous grid point method (EGM) for problems with more than one control variable using policy function iteration. The MRM algorithm is simple to implement and provides advantages in terms of speed and accuracy over Howard’s policy improvement algorithm. Codes are available.
    JEL: C6 C61 C63 C68
    Date: 2016–06–01
    URL: http://d.repec.org/n?u=RePEc:fip:feddgw:274&r=cmp
  5. By: László Békési (Magyar Nemzeti Bank (Central Bank of Hungary)); Zsolt Kovalszky (Magyar Nemzeti Bank (Central Bank of Hungary)); Tímea Várnai (Magyar Nemzeti Bank (Central Bank of Hungary))
    Abstract: The purpose of this paper is to illustrate the economic impact mechanism of the secession of Great Britain from the European Union (Brexit) on the Hungarian economy, and to quantify the domestic growth risks. Several international studies have dealt with this topic using partial analysis and model based simulations, but only partial analyses are available regarding the Hungarian economy. Our analysis provides a broader picture by using the new macroeconomic forecast model of the MNB. Upon determining the exogenous assumptions in our simulations we relied on the central bank experts’ broad knowledge. The applied model handles the wealth heterogeneity in the decision making processes of the households and the corporate sector. This feature makes the model suitable for explaining prolonged after-crisis recovery and the role of the financial accelerator feedback mechanism. In the course of illustrating the economic impact mechanism we show the main channels through which Brexit can spread over to Hungarian economic growth. Besides the primary channels, our analysis includes the secondary channel effects increasingly in the spotlight: we investigate the shock resilience ability of the financial system, and analyze the potential room for manoeuvre for fiscal policy.
    Keywords: Macroeconomic modelling, Simulation, Alternative Scenarios, Brexit, Economic Outlook
    JEL: E27 E66
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:mnb:opaper:2017/125&r=cmp
  6. By: Davis, Todd; Mark, Tyler; Shepherd, Jonathan
    Abstract: A stochastic simulation model is used to evaluate the profitability and liquidity of a low cost / low debt and high cost / high debt Western Kentucky corn-soybean farm over a five-year period. The model evaluates the effectiveness of crop insurance, government programs, and cash-forward contracts risk management tools and the impact on liquidity and profitability.
    Keywords: simulation, grain, insurance, farm policy, price risk, Agricultural Finance, Farm Management, Risk and Uncertainty, Q,
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:ags:saea17:252745&r=cmp
  7. By: Benavides, Justin; Martinez, Charley; Anderson, David; Roquette, Monte
    Abstract: Winter stocker grazing programs are commonly used by cattle producers in the South. A number of decisions are made in these production systems including type of forage, planting dates, fertilization rates, grazing periods, stocking rates and strategies, and feed supplementation. Of course, some important variables such as rainfall, cattle prices, weather, and rates of gain are out of producer’s control. There is a rich literature examining costs and returns to winter grazing. This research is unique in that it uses 40 years of East Texas winter pasture data to analyze the economics of alternative winter grazing production systems. The data includes planting dates and fertilization, rainfall, types of pasture, stocking rates, in and out weights, monthly weights, and supplementation. Pasture establishment costs, feed supplementation costs, and cattle prices are included in the data. Costs and returns for each strategy are estimated and the grazing strategy that leads to the maximum profits over time is identified. Simulation and econometric methods are used in the analysis of each strategy. A set of key management factors including dates for each production practice affecting profit are estimated.
    Keywords: stocker, cattle, winter, grazing, Livestock Production/Industries,
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:ags:saea17:252808&r=cmp
  8. By: Alena Miftakhova (University of Zurich); Kenneth L. Judd (Stanford University, Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP), and National Bureau of Economic Research (NBER)); Thomas S. Lontzek (University of Zurich; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP)); Karl Schmedders (University of Zurich)
    Abstract: In many studies involving complex representation of the Earth's climate, the number of runs for the particular model is highly restricted and the designed set of input scenarios has to be reduced correspondingly. Furthermore, many integrated assessment models, in particular those focusing on intrinsic uncertainty in social decision-making, suffer from poor representations of the climate system ue to computational constraints.In this study, using emission scenarios as input and the temperature anomaly as a predicted response variable, we construct low-dimensional approximations of high-dimensional climate models, as represented by MAGICC. In order to extract as much explanatory power as possible from the high-dimensional climate models, we construct orthogonal emissions scenarios that carry minimum repetitive information. Our method is especially useful when there is pressure to keep the number of scenarios as low as possible. We demonstrate that temperature levels can be inferred immediately from the CO2 emissions data within a one-line model that performs very well on conventional scenarios. Furthermore, we provide a system of equations that is ready to be deployed in macroeconomic optimization models. Thus, our study enhances the methodology applied in the emulation of complex climate models and facilitates the use of more realistic climate representations in economic integrated assessment models.
    Keywords: Climate Change, Greenhouse Gas, Single Equation Models
    JEL: Q54 C20
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp1676&r=cmp
  9. By: Pöppe, T.; Moos, S.; Schiereck, D.
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:83467&r=cmp
  10. By: Fajwel Fogel; Alexandre d'Aspremont; Milan Vojnovic
    Abstract: We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order. We then show that ranking reconstruction is still exact when some pairwise comparisons are corrupted or missing, and that seriation based spectral ranking is more robust to noise than classical scoring methods. Finally, we bound the ranking error when only a random subset of the comparions are observed. An additional benefit of the seriation formulation is that it allows us to solve semi-supervised ranking problems. Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods.
    Keywords: ranking; seriation; spectral methods
    JEL: C1
    Date: 2016–02
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:68987&r=cmp

This nep-cmp issue is ©2017 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at http://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.