nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒01‒08
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. AMPLE-CGE Model: User Guide By Galang, Ivory Myka R.
  2. Inequality, Redistributive Policies and Multiplier Dynamics in an Agent-Based Model with Credit Rationing By Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
  3. To trust or to control: Informal value transfer systems and computational analysis in institutional economics By Claudius Graebner; Wolfram Elsner; Alexander Lascaux
  4. Time-varying fiscal multipliers in an agent-based model with credit rationing By Napoletano, Mauro; Roventini, Andrea; Gaffard, Jean Luc
  5. A Short Walk on the Wild Side: Agent-Based Models and their Implications for Macroeconomic Analysis By Mauro Napoletano
  6. A Lower VAT Rate on Electricity in Portugal: Towards a Cleaner Environment, Better Economic Performance, and Less Inequality By Alfredo Marvão Pereira; Rui Manuel Pereira
  7. The 2018 Power Trading Agent Competition By Ketter, W.; Collins, J.; de Weerdt, M.M.
  8. Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy By Monica Andini; Emanuele Ciani; Guido de Blasio; Alessio D'Ignazio; Viola Salvestrini
  9. Energy efficiency as an instrument of regional development policy? Trading-off the benefits of an economic stimulus and energy rebound effects By Gioele Figus; Karen Turner; Patrizio Lecca; Peter G McGregor
  10. Learning Objectives for Treatment Effect Estimation By Xinkun Nie; Stefan Wager
  11. Hospital Readmission is Highly Predictable from Deep Learning By Damien Échevin; Qing Li; Marc-André Morin
  12. US financial shocks and the distribution of income and consumption in the UK By Mumtaz, Haroon; Theodoridis, Konstantinos
  13. Inverse Reinforcement Learning for Marketing By Igor Halperin
  14. The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach By Tomasz Weron; Anna Kowalska-Pyzalska; Rafal Weron
  15. Approximation methods for piecewise deterministic Markov processes and their costs By Peter Kritzer; Gunther Leobacher; Michaela Sz\"olgyenyi; Stefan Thonhauser
  16. Monte-Carlo methods for the pricing of American options: a semilinear BSDE point of view By Bruno Bouchard; Ki Chau; Arij Manai; Ahmed Sid-Ali
  17. QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds By Igor Halperin
  18. One-block train formation in large-scale railway networks: An exact model and a tree-based decomposition algorithm By Chen, C.; Dollevoet, T.A.B.; Zhao, J.
  19. Compensating households from carbon tax regressivity and fuel poverty: a microsimulation study By Audrey Berry
  20. Non-discriminatory Trade Policies in Structural Gravity Models. Evidence from Monte Carlo Simulations By Sellner, Richard

  1. By: Galang, Ivory Myka R.
    Abstract: This user guide is written to help prospective users learn how to navigate and run the AMPLE-CGE model. In the beginning, users will learn how to run the finished model and to display results in a software called Generalized Algebraic Modeling System or GAMS. Subsequently, the significance of and other important instructions concerning each part of the model code are explained. The construction and updating of the Philippine social accounting matrix are discussed toward the end. Assuming that users are familiar with computable general equilibrium modelling and with the use of GAMS, they should be able to replicate the results of the AMPLE-CGE model.
    Keywords: Social Accounting Matrix, CGE, computable general equilibrium modelling, AMPLE
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:phd:dpaper:dp_2017-41&r=cmp
  2. By: Elisa Palagi (Scuola Superiore Sant'Anna, Pisa (Italy)); Mauro Napoletano (OFCE Sciences-Po; SKEMA Business School); Andrea Roventini (University of Verona (Italy); Scuola Superiore Sant'Anna, Pisa (Italy)); Jean-Luc Gaffard (OFCE Sciences-Po; Université Côte d'Azur; GREDEG CNRS; Institut Universitaire de France)
    Abstract: We build an agent-based model populated by households with heterogenous and time-varying financial conditions in order to study how different inequality shocks affect income dynamics and the e ects of different types of fiscal policy responses. We show that inequality shocks generate persistent falls in aggregate income by increasing the fraction of credit-constrained households and by lowering aggregate consumption. Furthermore, we experiment with different types of fiscal policies to counter the effects of inequality-generated recessions, namely deficit-spending direct government consumption and redistributive subsidies financed by different types of taxes. We find that subsidies are in general associated with higher fiscal multipliers than direct government expenditure, as they appear to be better suited to sustain consumption of lower income households after the shock. In addition, we show that the effectiveness of redistributive subsidies increases if they are financed by taxing financial incomes or savings.
    Keywords: income inequality, fiscal multipliers, redistributive policies, credit-rationing, agent-based models
    JEL: E63 E21 C63
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:gre:wpaper:2017-39&r=cmp
  3. By: Claudius Graebner (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Wolfram Elsner (Institute for Institutional and Innovation Economics, University of Bremen, Germany); Alexander Lascaux (Russian Presidential Academy of National Economy and Public Administration, Moscow, Russia)
    Abstract: This paper illustrates the usefulness of computational methods for the investigation of institutions. As an example, we use a computational agent-based model to study the role of general trust and social control in informal value transfer systems (ITVS). We find that, how and in which timeline general trust and social control interact in order to make ITVS work, become stable and highly effective. The case shows how computational models may help (1) to operationalize institutional theory and to clarify the functioning of institutions, (2) to test the logical consistency of alternative hypotheses about institutions, and (3) to relate institutionalist theory with other paradigms and to practice an interested pluralism.
    Keywords: agent-based computational economics, evolutionary-institutional economics, informal value transfer systems, general trust, social control
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:ico:wpaper:74&r=cmp
  4. By: Napoletano, Mauro; Roventini, Andrea; Gaffard, Jean Luc
    Abstract: The authors build a simple agent-based model populated by households with heterogenous and time-varying financial conditions in order to study how fiscal multipliers can change over the business cycle and are affected by the state of credit markets. They find that deficit-spending fiscal policy dampens the effect of bankruptcy shocks and lowers their persistence. Moreover, the size and dynamics of government spending multipliers are related to the degree and persistence of credit rationing in the economy. On the contrary, in presence of balanced-budget rules, output permanently falls below pre-shock levels and the ensuing multipliers fall below one and are much lower than the ones emerging from the deficit-spending policy. Finally, the authors show that different conditions in the credit market significantly affect the size and the evolution of fiscal multipliers.
    Keywords: fiscal multipliers,agent-based models,credit-rationing,balance-sheet recession,bankruptcy shocks
    JEL: E63 E21 C63
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:zbw:ifwedp:2017112&r=cmp
  5. By: Mauro Napoletano (OFCE Sciences-Po; SKEMA Business School)
    Abstract: I discuss recent advances in agent-based modelling applied to macroeconomic analysis. I first present the building blocks of agent- based models. Furthermore, by relying on examples taken from recent works, I argue that agent-based models provide complementary or new lights with respect to more standard models on key macroeconomic issues like endogenous business cycles, the interactions between business cycles and long-run growth, and the role of price vs. quantity adjustments in the return to full employment. Finally, I discuss some limits of agentbased models and how they are currently addressed in the literature.
    Keywords: agent-based models, macroeconomic analysis, endogenous business-cycles, short and long-run dynamics, monetary and fiscal policy, price vs. quantity adjustments
    JEL: B41 B50 E32 E52
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:gre:wpaper:2017-40&r=cmp
  6. By: Alfredo Marvão Pereira (Department of Economics, The College of William and Mary, Williamsburg VA 23187); Rui Manuel Pereira (Department of Economics, The College of William and Mary, Williamsburg VA 23187)
    Abstract: This article determines the budgetary, economic, distributional and environmental impact of permanently increasing the value-added tax on electricity in Portugal. The analysis is carried out in the context of a new multi-sector and multi-household dynamic general equilibrium model. Simulation results suggest that a permanent increase from 6% to 23% in the statutory VAT on electricity improves the public budget as well as the environment, but both gains have detrimental economic and distributional effects. As the economy in Portugal begins to recover in the aftermath of the Great Financial Crisis, and the public budgetary situation becomes less constraining, pressure is mounting for this VAT increase on electricity to be reversed. This mixed bag of results is an important element for the debate. Reverting to a tax of 6% on electricity is desirable, as it would improve economic performance and have positive distributional effects. The question, then, is how to compensate for the loss of tax revenue and, at the same time, protect the environment. To offset the adverse budgetary and environmental effects of a lower VAT, we propose to increase the tax on petroleum products. This proves to be a dominant strategy from all relevant perspectives – economic, distributional, and environmental.
    Keywords: Value-Added Tax on Electricity, Tax on Petroleum Products, Macroeconomic Effects, Distributional Effects, Environmental Effects, Portugal
    JEL: C68 E62 H23 Q43 Q48
    Date: 2017–07
    URL: http://d.repec.org/n?u=RePEc:mde:wpaper:0090&r=cmp
  7. By: Ketter, W.; Collins, J.; de Weerdt, M.M.
    Abstract: This is the specification for the Power Trading Agent Competition for 2018 (Power TAC 2018). 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 2018 are focused on stability, on simplifying interaction with the balancing market, and on encouraging more vigorous competition, and are highlighted by change bars in the margins. See Sections 3.1.2, 6, and 8.5 for details.
    Keywords: Autonomous Agents, Electronic Commerce, Energy, Preferences, Portfolio Management, Power, Policy Guidance, Sustainability, Trading Agent Competition
    Date: 2017–12–13
    URL: http://d.repec.org/n?u=RePEc:ems:eureri:103283&r=cmp
  8. By: Monica Andini (Bank of Italy); Emanuele Ciani (Bank of Italy); Guido de Blasio (Bank of Italy); Alessio D'Ignazio (Bank of Italy); Viola Salvestrini (London School of Economics and Political Science)
    Abstract: Machine Learning (ML) can be a powerful tool to inform policy decisions. Those who are treated under a programme might have different propensities to put into practice the behaviour that the policymaker wants to incentivize. ML algorithms can be used to predict the policy-compliers; that is, those who are most likely to behave in the way desired by the policymaker. When the design of the programme is tailored to target the policy-compliers, the overall effectiveness of the policy is increased. This paper proposes an application of ML targeting that uses the massive tax rebate scheme introduced in Italy in 2014.
    Keywords: machine learning, prediction, programme evaluation, fiscal stimulus
    JEL: C5 H3
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1158_17&r=cmp
  9. By: Gioele Figus (Centre for Energy Policy, University of Strathclyde); Karen Turner (Centre for Energy Policy, University of Strathclyde); Patrizio Lecca; Peter G McGregor (Department of Economics, University of Strathclyde)
    Abstract: Previous studies show that improving efficiency in household energy use can stimulate a national economy through an increase and change in the pattern of the aggregate demand. However, this may impact competitiveness. Here we find that in an open region, interregional migration of workers may give additional momentum to the economic expansion, by relieving pressure on the real wage and the CPI. Furthermore, the stimulus will be further enhanced by the greater fiscal autonomy that Scotland is set shortly to enjoy. By considering a range of CGE simulation scenarios we show that there is a tension between the economic stimulus from energy efficiency and the scale of rebound effects. However, we also show that household energy efficiency increases do typically generate a “double dividend†of increased regional economic activity and a reduction in carbon emissions.
    Keywords: energy efficiency, regional development policy, energy rebound, regional fiscal autonomy, general equilibrium
    JEL: D58 Q43 Q48 R28 R58
    Date: 2017–02
    URL: http://d.repec.org/n?u=RePEc:str:wpaper:17-02&r=cmp
  10. By: Xinkun Nie; Stefan Wager
    Abstract: We develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities to form an objective function that isolates the heterogeneous treatment effects, and then optimize the learned objective. This approach has several advantages over existing methods. From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice, e.g., penalized regression, a deep net, or boosting; moreover, these methods can be fine-tuned by cross-validating on the learned objective. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property, whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these nuisance components. We implement variants of our method based on both penalized regression and convolutional neural networks, and find promising performance relative to existing baselines.
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1712.04912&r=cmp
  11. By: Damien Échevin; Qing Li; Marc-André Morin
    Abstract: Hospital readmission is costly and existing models are often poor or moderate in predicting readmission. We sought to develop and test a method that can be applied generally by hospitals. Such a tool can help clinicians identify patients who are more likely to be readmitted, either at early stages of hospital stay or at hospital discharge. Relying on state-of-the art machine learning algorithms, we predict probability of 30-day readmission at hospital admission and at hospital discharge using administrative data on 1,633,099 hospital stays from Quebec between 1995 and 2012. We measure performance of the predictions with the area under receiver operating characteristic curve (AUC). Deep Learning produced excellent prediction of readmission province-wide, and Random Forest reached very similar level. The AUC for these two algorithms reached above 78% at hospital admission and above 87% at hospital discharge, and the diagnostic codes are among the most predictive variables. The ease of implementation of machine learning algorithms, together with objectively validated reliability, brings new possibilities for cost reduction in the health care system.
    Keywords: Machine learning; Logistic regression; Risk of re-hospitalisation; Healthcare costs
    JEL: I10 C52
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:lvl:criacr:1705&r=cmp
  12. By: Mumtaz, Haroon (Queen Mary University); Theodoridis, Konstantinos (Cardiff Business School)
    Abstract: We show that US financial shocks have an impact on the distribution of UK income and consumption. Households with higher income and higher levels of consumption are affected more by this shock than households located towards the lower end of these distributions. An estimated multiple agent DSGE model suggests that the heterogeneity in the household responses can be explained by the different levels of access to financial markets. We find that this heterogeneity magnifies the effect of this shock on aggregate output.
    Keywords: FAVAR, DSGE model, Financial Shock
    JEL: D31 E32 E44
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:cdf:wpaper:2017/18&r=cmp
  13. By: Igor Halperin
    Abstract: Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL that leads to a highly tractable model formulation that amounts to low-dimensional convex optimization in the search for optimal model parameters. Using simulations of consumer demand, we show that observational noise for identical customers can be easily confused with an apparent consumer heterogeneity.
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1712.04612&r=cmp
  14. By: Tomasz Weron; Anna Kowalska-Pyzalska; Rafal Weron
    Abstract: Using an agent-based modeling approach we examine the impact of educational programs and trainings on the diffusion of smart metering platforms (SMPs). We also investigate how social responses, like conformity or independence, mass-media advertising as well as opinion stability impact the transition from predecisional and preactional behavioral stages (opinion formation) to actional and postactional stages (decision-making) of individual electricity consumers. We find that mass-media advertising (i.e., a global external field) and educational trainings (i.e., a local external field) lead to similar, though not identical adoption rates. Secondly, that spatially concentrated 'group' trainings are never worse than randomly scattered ones, and for a certain range of parameters are significantly better. Finally, that by manipulating the time required by an agent to make a decision, e.g., through promotions, we can speed up or slow down the diffusion of SMPs.
    Keywords: Smart meter; Smart metering platform (SMP); Behavioral strategy; Demand response; Diffusion of innovations; Agent-based model
    JEL: C63 O33 Q40 Q55 Q56
    Date: 2017–11–25
    URL: http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1704&r=cmp
  15. By: Peter Kritzer; Gunther Leobacher; Michaela Sz\"olgyenyi; Stefan Thonhauser
    Abstract: In this paper, we analyse piecewise deterministic Markov processes, as introduced in Davis (1984). Many models in insurance mathematics can be formulated in terms of the general concept of piecewise deterministic Markov processes. In this context, one is interested in computing certain quantities of interest such as the probability of ruin of an insurance company, or the insurance company's value, defined as the expected discounted future dividend payments until the time of ruin. Instead of explicitly solving the integro-(partial) differential equation related to the quantity of interest considered (an approach which can only be used in few special cases), we adapt the problem in a manner that allows us to apply deterministic numerical integration algorithms such as quasi-Monte Carlo rules; this is in contrast to applying random integration algorithms such as Monte Carlo. To this end, we reformulate a general cost functional as a fixed point of a particular integral operator, which allows for iterative approximation of the functional. Furthermore, we introduce a smoothing technique which is applied to the integrands involved, in order to use error bounds for deterministic cubature rules. On the analytical side, we prove a convergence result for our PDMP approximation, which is of independent interest as it justifies phase-type approximations on the process level. We illustrate the smoothing technique for a risk-theoretic example, and provide a comparative study of deterministic and Monte Carlo integration.
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1712.09201&r=cmp
  16. By: Bruno Bouchard (CEREMADE); Ki Chau (CWI); Arij Manai (UM); Ahmed Sid-Ali
    Abstract: We extend the viscosity solution characterization proved in [5] for call/put American option prices to the case of a general payoff function in a multi-dimensional setting: the price satisfies a semilinear re-action/diffusion type equation. Based on this, we propose two new numerical schemes inspired by the branching processes based algorithm of [8]. Our numerical experiments show that approximating the discontinu-ous driver of the associated reaction/diffusion PDE by local polynomials is not efficient, while a simple randomization procedure provides very good results.
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1712.07383&r=cmp
  17. By: Igor Halperin
    Abstract: This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process for a discrete-time version of the classical Black-Scholes-Merton (BSM) model, where the option price is an optimal Q-function. Pricing is done by learning to dynamically optimize risk-adjusted returns for an option replicating portfolio, as in the Markowitz portfolio theory. Using Q-Learning and related methods, once created in a parametric setting, the model is able to go model-free and learn to price and hedge an option directly from data generated from a dynamic replicating portfolio which is rebalanced at discrete times. If the world is according to BSM, our risk-averse Q-Learner converges, given enough training data, to the true BSM price and hedge ratio of the option in the continuous time limit, even if hedges applied at the stage of data generation are completely random (i.e. it can learn the BSM model itself, too!), because Q-Learning is an off-policy algorithm. If the world is different from a BSM world, the Q-Learner will find it out as well, because Q-Learning is a model-free algorithm. For finite time steps, the Q-Learner is able to efficiently calculate both the optimal hedge and optimal price for the option directly from trading data, and without an explicit model of the world. This suggests that RL may provide efficient data-driven and model-free methods for optimal pricing and hedging of options, once we depart from the academic continuous-time limit, and vice versa, option pricing methods developed in Mathematical Finance may be viewed as special cases of model-based Reinforcement Learning. Our model only needs basic linear algebra (plus Monte Carlo simulation, if we work with synthetic data).
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1712.04609&r=cmp
  18. By: Chen, C.; Dollevoet, T.A.B.; Zhao, J.
    Abstract: We investigate the one-block train formation problem (TFP) in the railway freight transportation industry given a car route for each shipment. The TFP considers both the block design and the car-to-block assignment in the tactical level. Moving beyond current researches on service network design, the unitary rule and the intree rule are taken into account in this study based on the Chinese railway background. We develop a linear binary programming formulation to minimize the sum of train cost and classication delay subject to limitations on the classication capacity and the number of sort tracks at each station. Furthermore, we propose a novel solution methodology that applies a tree-based decomposition algorithm. Here, we rst decompose the whole network into a series of rooted trees for each destination separately. Then, we divide the trees into suciently small subtrees, whose size is regulated by a node size parameter. Finally, we construct a restricted linear binary model for each subtree and solve these models sequentially to nd their optimal solutions. Our computational results on a realistic network from the Chinese railway system with 83 stations, 158 links and 5700 randomly generated demands show that the proposed algorithm can derive high-quality solutions within 3 hours. These solutions are on average 43.89% better than those obtained after solving the linear binary program for 1 day.
    Keywords: Railway Freight Transportation, Train Formation Problem, Service Network Design, Tree-based Decomposition, Arborescence Structure
    Date: 2017–12–01
    URL: http://d.repec.org/n?u=RePEc:ems:eureir:103193&r=cmp
  19. By: Audrey Berry (CIRED)
    Abstract: For households, taxing carbon raises the cost of the energy they use to heat their home and to travel. This paper studies the distributional impacts of the recently introduced French carbon tax and the design of compensation measures. Using a microsimulation model built on a representative sample of the French population from 2012, I simulate for each household the taxes levied on its consumption of energy for housing and transport. Without recycling, the carbon tax is regressive and increases fuel poverty. However, I show how compensation measures can offset these impacts. A flat cash transfer offsets tax regressivity by redistributing
    Keywords: Carbon tax, Distributional impacts, Fuel poverty, Revenue recycling, Microsimulation
    JEL: Q5 I3
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:fae:ppaper:2017.08&r=cmp
  20. By: Sellner, Richard (Institute for Advanced Studies (IHS), Vienna)
    Abstract: This paper provides Monte Carlo simulation evidence on the performance of methods used for identifying the effects of non-discriminatory trade policy (NDTP) variables in structural gravity models (SGM). The benchmarked methods include the identification strategy of Heid, Larch & Yotov (2015) that utilizes data on intra-national trade flows and three other methods that do not rely on this data. Results indicate that under the assumption of a data generating process that conforms with SGM theory, data on intra-national trade flows is required for identification. The bias of the three methods that do not utilize this data, is a result of the correlation between the NDTP variable and the collinear fixed effects. The MC results and an empirical application demonstrate the severity of this bias in methods that have been applied in previous empirical research.
    Keywords: Structural Gravity Model, Non-discriminatory Trade Policies, Monte Carlo Simulation
    JEL: C31 F10 F13
    Date: 2017–12
    URL: http://d.repec.org/n?u=RePEc:ihs:ihsesp:335&r=cmp

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