New Economics Papers
on Computational Economics
Issue of 2013‒10‒18
seven papers chosen by

  1. Wage Subsidy in the DRC: A CGE Analysis By Jean Luc Erero, Daniel Djauhari Pambudi and Lumengo Bonga Bonga
  2. Strategic Planning of Large-scale, Multimodal and Time-definite Networks for Overnight Express Delivery Services By Xue, Yida
  3. How Transparent About Its Inflation Target Should a Central Bank be? An Agent-Based Model Assessment By Isabelle SALLE; Marc-Alexandre SENEGAS; Murat YILDIZOGLU
  4. Estimation Errors in Input-Output Tables and Prediction Errors in Computable General Equilibrium Analysis By Nobuhiro Hosoe
  5. Optimal consumption under uncertainty, liquidity constraints, and bounded rationality By Ömer Özak
  6. Would Border Carbon Adjustments prevent carbon leakage and heavy industry competitiveness losses? Insights from a meta-analysis of recent economic studies By Frédéric Branger; Philippe Quirion
  7. A statistical physics perspective on criticality in financial markets By Thomas Bury

  1. By: Jean Luc Erero, Daniel Djauhari Pambudi and Lumengo Bonga Bonga
    Abstract: This paper analyses wage subsidies on lower-skilled formal workers in the Democratic Republic of Congo (DRC). A multi-sectoral empirically-calibrated general equilibrium model capturing the economy-wide transactions between the formal and informal sectors is used to analyse one policy simulation in the DRC. The short and long run simulation in which the government provides wage subsidy to lower-skilled workers indicates that the government is able to significantly improve the deficiencies of the formal and informal households’ real disposable incomes. There is a general increase across formal and informal sectors in real household disposable incomes due to wage subsidy. The simulation results show that subsidy allocation narrowed the income gap between high and low income households, and between formal and informal sectors as well. The result seems somewhat insightful for wage policy simulation as the wage subsidy that targets lower-skilled formal workers increases real GDP from the expenditure side by 1.19% and 3.19% in the short and long run, respectively, from the baseline economy.
    Keywords: wage subsidy, informal sector, CGE model, Democratic Republic of Congo
    JEL: C68 D58 E24 E26 O17 R28
    Date: 2013
  2. By: Xue, Yida
    Abstract: The rising demand of express delivery service (EDS) and fierce market competition motivate EDS providers to improve service quality by modifying current networks. This project-based dissertation focuses on strategic planning of a large-scale, multi-modal and time-definite EDS network for a top nationwide EDS provider in China, based on its current network. An air-ground Hub-and-Spoke (H/S) network with a fully interconnected/star shaped structure was established to provide trans-city overnight EDS among relatively developed cities in China. The corresponding models are a combination of the hub location problem with fixed cost and the hub set covering problem. The objective function is to minimize the sum of the hub-location fixed cost and transportation cost under the constraints that all demand nodes are covered by their "home" hub. First, the basic model with linear air cost was proposed. Next, the basic model was extended to include air service selection decisions (or aircraft fleet owner-ship decisions) under the consideration of a cost select function for the backbone air service. Finally, two ex-tension models were studied, one to obtain the optimal aircraft fleet composition (Ext.1) and the other under the constraints of current aircraft fleet composition (Ext.2). Due to the large scale of project instances, hybrid genetic algorithms (GAs) were applied to get desirable solutions in an acceptable time period, but without the guarantee of finding optimal solutions. In particular, the overall problem includes three kinds of decisions: 1) hub location decisions, 2) demand allocation decisions and 3) air service selection decisions. A specific algorithm was proposed for each kind of decision, namely, GAs,local search heuristics and integer programming, respectively. These three algorithms were invoked hierarchically and iteratively to solve the original problem. 5 improvement techniques were proposed to different procedures of the original algorithms in order to improve the performance of the algorithms. Computational tests were conducted to evaluate the performance of the proposed algorithms in terms of computational time and solution quality. Tests under small-scale instances with CAB data sets were conducted to evaluate the overall performance of the proposed algorithm by comparing the solutions with the optimal solutions generated by CPLEX. Tests under large-scale instances with AP data sets and project data sets were conducted to evaluate the performance of the proposed improvement techniques. Since neither the optimal solutions nor solutions by other algorithms under large-scale instances were available to serve as benchmarks,the performance of the tailored algorithms and that of the un-tailored simple GAs was compared. Information about the stability of the algorithms with values of the coefficient of variation (CV) and the reliability of the results with T-tests was also provided. The models and the tailored GAs were applied to real-life instances of the project. This study introduces how the input data were collected and modified and how to deal with pertinent problems. By analyzing and com-paring the basic solutions of Ext.1 and Ext.2, the study not only reveals some important features of the net-work, but also arrives at some general conclusions and provided a dynamic aircraft fleet update strategy to guide the implementation of the project. Finally, scenario planning was executed to help decision-makers balance between costs and corresponding decision risks by identifying critical uncontrollable and controllable factors.
    Date: 2013–07
  3. By: Isabelle SALLE; Marc-Alexandre SENEGAS; Murat YILDIZOGLU
    Abstract: This paper revisits the benefits of explicitly announcing an inflation target for the con- duct of monetary policy in the framework of an agent-based model (ABM). This framework offers a flexible tool for modeling heterogeneity among individual agents and their bounded rationality, and to emphasize, on this basis, the role of learning in macroeconomic dynamics. We consider that those three features (heterogeneity, bounded rationality, and learning) are particularly relevant if one desires to question the rationale for the monetary authorities to be transparent about the inflation target, and to achieve credibility. Indeed, the inflation targeting’s potential role in anchoring inflation expectations and stabilizing the inflation and the economy can be analyzed more realistically if we do not assume a representative agent framework based on substantial rationality in behaviors and expectations. Our results show that a dynamic loop between credibility and success can arise, and stabilize inflation, but only in the case of a learning environment that corresponds to a moderate degree in heterogeneity regarding the behavior and decisions of individual agents. In a more general way, we analyze, using this ABM, different assumptions about the nature of the economic volatility, and the degree of disclosure of the target.
    Keywords: Monetary Policy, Inflation Targeting, Credibility, Expectations, Agent-Based Model.
    JEL: C61 C63 E52 E58
    Date: 2013
  4. By: Nobuhiro Hosoe (National Graduate Institute for Policy Studies)
    Abstract: We used 1995-2000-2005 linked input-output (IO) tables for Japan to examine estimation errors of updated IO tables and the resulting prediction errors in computable general equilibrium (CGE) analysis developed with updated IO tables. As we usually have no true IO tables for the target year and therefore need to estimate them, we cannot evaluate estimation errors of updated IO tables without comparing the updated ones with true ones. However, using the linked IO tables covering three different years enables us to make this comparison. Our experiments showed that IO tables estimated with more detailed and recent data contained smaller estimation errors and led to smaller quantitative prediction errors in CGE analysis. Despite the quantitative prediction errors, prediction was found to be qualitatively correct. As for the performance of updating techniques of IO tables, a cross-entropy method often outperformed a least-squares method in IO estimation with only aggregate data for the target year but did not necessarily outperform the least-squares method in CGE prediction.
    Date: 2013–10
  5. By: Ömer Özak (Southern Methodist University)
    Abstract: I study how boundedly rational agents can learn a “good” solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. Using an empirically plausible theory of learning I propose a class of adaptive learning algorithms that agents might use to choose a consumption rule. I show that the algorithm always has a globally asymptotically stable consumption rule, which is optimal. Additionally, I present extensions of the model to finite horizon settings, where agents have finite lives and life-cycle income patterns. This provides a simple and parsimonious model of consumption for large agent based models.
    Keywords: Adaptive learning models, bounded rationality, dynamic programming, consumption function, behavioral economics, liquidity constraint, saving behavior .
    JEL: C6 D8 D9 E21
    Date: 2013–09
  6. By: Frédéric Branger (CIRED - Centre International de Recherche sur l'Environnement et le Développement - Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR56 - CNRS : UMR8568 - École des Hautes Études en Sciences Sociales [EHESS] - École des Ponts ParisTech (ENPC) - AgroParisTech); Philippe Quirion (CIRED - Centre International de Recherche sur l'Environnement et le Développement - Centre de coopération internationale en recherche agronomique pour le développement [CIRAD] : UMR56 - CNRS : UMR8568 - École des Hautes Études en Sciences Sociales [EHESS] - École des Ponts ParisTech (ENPC) - AgroParisTech)
    Abstract: The efficiency of unilateral climate policies may be hampered by carbon leakage and competitiveness losses. A widely discussed policy option to reduce leakage and protect competitiveness of heavy industries is to impose Border Carbon Adjustments (BCA) to non regulated countries, which remains contentious for juridical and political reasons. The estimation of carbon leakage as well as the assessment of different policy options led to a substantial body of litterature in energy-economic modeling. In order to give a quantitative overview on the most recent research on the topic, we conduct a meta-analysis on 25 studies, altogether providing 310 estimates of carbon leakage ratios according to different assumptions and models. The typical range of carbon leakage ratio estimates are from 5% to 25% (mean 14%) without policy and from -5% to 15% (mean 6%) with BCA. The output change of Energy Intensive Trade Exposed (EITE) sectors varies from -0.1% to -16% without BCA and from +2.2% to -15.5% with BCA. A meta-regression analysis is performed to further investigate the impact of different assumptions on the leakage ratio estimates. The decrease of the leakage ratio with the size of the coalition and its increase with the binding target is confirmed and quantified. Providing flexibility reduces leakage ratio, especially the extension of coverage to all GHG sources. High values of Armington elasticities lead to higher leakage ratio and among the BCA options, the extension of BCA to all sectors is in the meta-regression model the most efficient feature to reduce the leakage ratio. Our most robust statistical finding is that, all other parameters being constant, BCA reduces leakage ratio by 6 percentage points.
    Keywords: Carbon leakage, Competitiveness, Border Carbon Adjustments, Meta-analysis, Meta-regression analysis, Computable General Equilibrium (CGE) models
    Date: 2013–09
  7. By: Thomas Bury
    Abstract: Stock markets are complex systems exhibiting collective phenomena and particular features such as synchronization, fluctuations distributed as power-laws, non-random structures and similarity to neural networks. Such specific properties suggest that markets operate at a very special point. Financial markets are believed to be critical by analogy to physical systems but few statistically founded evidence have been given. Through a data-based methodology and comparison to simulations inspired by statistical physics of complex systems, we show that the Dow Jones and indices sets are not rigorously critical. However, financial systems are closer to the criticality in the crash neighborhood.
    Date: 2013–10

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NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.