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on Computational Economics |
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Issue of 2014‒07‒28
eight papers chosen by |
| By: | V. KAMINI; V. RAVI; A. PRINZIE; D. VAN DEN POEL (-) |
| Abstract: | To improve ATMs’ cash demand forecasts, this paper advocates the prediction of cash demand for groups of ATMs with similar day-of-the week cash demand patterns. We first clustered ATM centers into ATM clusters having similar day-of-the week withdrawal patterns. To retrieve “day-of-the-week” withdrawal seasonality parameters (effect of a Monday, etc) we built a time series model for each ATMs. For clustering, the succession of 7 continuous daily withdrawal seasonality parameters of ATMs is discretized. Next, the similarity between the different ATMs’ discretized daily withdrawal seasonality sequence is measured by the Sequence Alignment Method (SAM). For each cluster of ATMs, four neural networks viz., general regression neural network (GRNN), multi layer feed forward neural network (MLFF), group method of data handling (GMDH) and wavelet neural network (WNN) are built to predict an ATM center’s cash demand. The proposed methodology is applied on the NN5 competition dataset. We observed that GRNN yielded the best result of 18.44% symmetric mean absolute percentage error (SMAPE), which is better than the result of Andrawis et al. (2011). This is due to clustering followed by a forecasting phase. Further, the proposed approach yielded much smaller SMAPE values than the approach of direct prediction on the entire sample without clustering. From a managerial perspective, the clusterwise cash demand forecast helps the bank’s top management to design similar cash replenishment plans for all the ATMs in the same cluster. This cluster-level replenishment plans could result in saving huge operational costs for ATMs operating in a similar geographical region. |
| Keywords: | Time Series, Neural Networks, SAM method, Clustering, ATM Cash withdrawal forecasting |
| Date: | 2013–11 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:13/865&r=cmp |
| By: | Guerrieri, Luca (Board of Governors of the Federal Reserve System (U.S.)); Iacoviello, Matteo (Board of Governors of the Federal Reserve System (U.S.)) |
| Abstract: | We describe how to adapt a first-order perturbation approach and apply it in a piecewise fashion to handle occasionally binding constraints in dynamic models. Our examples include a real business cycle model with a constraint on the level of investment and a New Keynesian model subject to the zero lower bound on nominal interest rates. We compare the piecewise linear perturbation solution with a high-quality numerical solution that can be taken to be virtually exact. The piecewise linear perturbation method can adequately capture key properties of the models we consider. A key advantage of this method is its applicability to models with a large number of state variables. |
| Keywords: | Occasionally binding constraints; DSGE models; regime shifts; first-order perturbation |
| Date: | 2014–07–07 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2014-47&r=cmp |
| By: | Hilel HAMADACHE (Marchés, Organisations, Institutions et Stratégies d'Acteurs, INRA; Institut Agronomique Méditerranéen de Montpellier, Centre International de Hautes Etudes Agronomiques Méditerranéennes); Sophie Drogue (Marchés, Organisations, Institutions et Stratégies d'Acteurs, INRA) |
| Abstract: | In this paper we present a Social accounting matrix and a computable general equilibrium model of the Algerian economy for 2009. The model is then use to perform scenarios simulation of reduction and removal of consumption subsidies on the wheat sector in Algeria. |
| Keywords: | social accounting matrix, general equilibrium, food subsidy, algeria, marché agricole, politique agricole, prix alimentaire, ble tendre, matricemodèle d'équilibre généralalgérie |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:inr:wpaper:264676&r=cmp |
| By: | Jun-jie Chen; Bo Zheng; Lei Tan |
| Abstract: | Background: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results. Methods: To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors' asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data. Results: With the model parameters determined for six representative stock-market indices in the world respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities. Conclusions: We reveal that for the leverage and anti-leverage effects, both the investors' asymmetric trading and herding are essential generation mechanisms. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries. |
| Date: | 2014–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1407.5258&r=cmp |
| By: | Fedor Iskhakov (University New South Wales); John Rust (Georgetown University); Bertel Schjerning (Department of Economics, Copenhagen University); Jean-Robert Tyran |
| Abstract: | We define a class of dynamic Markovian games that we call directional dynamic games (DDG) in which directionality is represented by a partial order on the state space. We propose a fast and robust state recursion algorithm that can find a Markov perfect equilibrium (MPE) via backward induction on the state space of the game. When there are multiple equilibria, this algorithm relies on an equilibrium selection rule (ESR) to pick a particular MPE.We propose a recursive lexicographic search (RLS) algorithm that systematically and efficiently cycles through all feasible ESRs and prove that the RLS algorithm finds all MPE of the overall game. We apply the algorithms to find all MPE of a dynamic duopoly model of Bertrand price competition and cost reducing investments which we show is a DDG. Even with coarse discretization of the state space we find hundreds of millions of MPE in this game. |
| Keywords: | Dynamic games, directional dynamic games, Markov-perfect equilibrium, subgame perfect equilibrium, multiple equilibria, partial orders, directed acyclic graphs, d-subgames, generalized stage games, state recursion, recursive lexicographic search algorithm, variable-base arithmetic, successor function |
| JEL: | D92 L11 L13 |
| Date: | 2014–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:1416&r=cmp |
| By: | Olivier Gu\'eant; Jean-Michel Lasry; Jiang Pu |
| Abstract: | In spite of the growing consideration for optimal execution issues in the financial mathematics literature, numerical approximations of optimal trading curves are almost never discussed. In this article, we present a numerical method to approximate the optimal strategy of a trader willing to unwind a large portfolio. The method we propose is very general as it can be applied to multi-asset portfolios with any form of execution costs, including a bid-ask spread component, even when participation constraints are imposed. Our method, based on convex duality, only requires Hamiltonian functions to have $C^{1,1}$ regularity while classical methods require additional regularity and cannot be applied to all cases found in practice. |
| Date: | 2014–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1407.4614&r=cmp |
| By: | J. D’HAEN; D. VAN DEN POEL (-) |
| Abstract: | This article discusses a model designed to help sales representatives acquire customers in a business-to-business environment. Sales representatives are often overwhelmed by available information, so they use arbitrary rules to select leads to pursue. The goal of the proposed model is to generate a high-quality list of prospects that are easier to convert into leads and ultimately customers in three phases: Phase 1 occurs when there is only information on the current customer base and uses the nearest neighbor method to obtain predictions. As soon as there is information on companies that did not become customers, phase 2 initiates, triggering a feedback loop to optimize and stabilize the model. This phase uses logistic regression, decision trees, and neural networks. Phase 3 combines phases 1 and 2 into a weighted list of prospects. Preliminary tests indicate the good quality of the model. The study makes two theoretical contributions: First, the authors offer a standardized version of the customer acquisition framework, and second, they point out the iterative aspects of this process. |
| Keywords: | customer acquisition, sales funnel, prospects, nearest neighbor, decision tree, neural network |
| Date: | 2013–11 |
| URL: | http://d.repec.org/n?u=RePEc:rug:rugwps:13/863&r=cmp |
| By: | Brophy, Tim (SALDRU, School of Economics, University of Cape Town); Daniels, Reza Che (School of Economics, University of Cape Town); Musundwa, Sibongile (SALDRU, School of Economics, University of Cape Town) |
| Abstract: | Geographical coordinates such as Global Positioning System (GPS) latitude and longitude estimates form the foundation of many spatial statistical methods. gpsbound allows users to (1) import geographical information from the attribute table of a polygon shapefile based on the identified location of GPS coordinates in a Stata dataset, and (2) check that the GPS coordinates lie within the bounds of a polygon demarcated in the shapefile (e.g. enumerator areas, primary sampling units, suburb, city, country). One of the contributions of gpsbound is to allow users to work with spatial data in Stata without ever needing Geographical Information System (GIS) software. |
| Keywords: | Stata ado, GPS coordinates, Bounding box, Point in polygon algorithm |
| JEL: | C63 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ldr:wpaper:132&r=cmp |