nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒02‒05
twenty-one papers chosen by

  1. Generative Models for Stochastic Processes Using Convolutional Neural Networks By Fernando Fernandes Neto
  2. Black-Box Classification Techniques for Demographic Sequences : from Customised SVM to RNN By Muratova, Anna; Sushko, Pavel; Espy, Thomas H.
  3. Using decision tree classifier to predict income levels By Bekena, Sisay Menji
  4. The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios By Igor Halperin
  5. Exploring a negative income tax for South Africa: impacts on income inequality and poverty By Emma Helen Rasmussen
  6. Can a reduction in fuel use result from an endogenous technical progress in motor vehicles? A partial and general equilibrium analysis By Gioele Figus; J Kim Swales; Karen Turner
  7. Numerical analysis on quadratic hedging strategies for normal inverse Gaussian models By Takuji Arai; Yuto Imai; Ryo Nakashima
  8. Macro-Economic Impact of MGNREGA in India: An Analysis in CGE Modeling Framework By Akhilesh K. Sharma; Atul Sarma; Charanjit Kaur; Javier Cabrera; Deeksha Tayal
  9. Global Supply Chains: towards a CGE analysis By Prema-chandra Athukorala; Peter B. Dixon; Maureen T. Rimmer
  10. Endogenous growth and global divergence in a multi-country agent-based model By Giovanni Dosi; Andrea Roventini; Emanuele Russo
  11. Energy efficiency as an instrument of regional development policy? Trading-off the benefits of an economic stimulus and energy rebound effects By Gioele Figus; Patrizio Lecca; Peter McGregor; Karen Turner
  12. Is the production function Translog or CES? An empirical illustration using UK data By Elena Lagomarsino; Karen Turner
  13. The role of micro and small scale enterprises in the Ethiopian economy, government intervention and alternative strategies: A CGE analysis By Ermias Engida; Mekdim Dereje; Ibrahim Worku; Saba Yifredew; Feiruz Yimer
  14. Using a regional CGE model for rapid assessments of the economic implications of terrorism events: creating GRAD-ECAT (Generalized, Regional And Dynamic Economic Consequence Analysis Tool) By Peter B. Dixon; Michael Jerie; Maureen T. Rimmer; Glyn Wittwer
  15. Part 1: Training Sets & ASG Transforms By Rilwan Adewoyin
  16. Cambodia Macroeconomic Impacts of Public Consumption on Education: A Computable General Equilibrium Approach By Ear Sothy; Sim Sokcheng; Khiev Pirom
  17. Large-Scale Simulation of Multi-Asset Ising Financial Markets By Tetsuya Takaishi
  18. Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach By Chen, Jiaqi; Wu, Wenbo; Tindall, Michael
  19. Disaggregation of the 2010 UK Social Accounting Matrix to report household income quintiles By Antonios Katris; Gioele Figus; Karen Turner
  20. Using Simulation and Six-Sigma Tools in Improving Process Flow in Outpatient Clinics By Heidarzadeh, Elham; Sajadnia, Sahar
  21. Exploiting MIT Shocks in Heterogeneous-Agent Economies: The Impulse Response as a Numerical Derivative By Boppart, Timo; Krusell, Per; Mitman, Kurt

  1. By: Fernando Fernandes Neto
    Abstract: The present paper aims to demonstrate the usage of Convolutional Neural Networks as a generative model for stochastic processes, enabling researchers from a wide range of fields (such as quantitative finance and physics) to develop a general tool for forecasts and simulations without the need to identify/assume a specific system structure or estimate its parameters.
    Date: 2018–01
  2. By: Muratova, Anna; Sushko, Pavel; Espy, Thomas H.
    Abstract: Nowadays there is a large amount of demographic data which should be analysed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. The aim of this study is to compare the methods of classification of demographic data by customising the SVM kernels using various similarity measures. Since demographers are interested in sequences without discontinuity, formulas for such sequences similarity measures were derived. Then they were used as kernels in the SVM method, which is the novelty of this study. Recurrent neural network algorithms, such as Simple RNN, GRU and LSTM, are also compared. The best classification result with SVM method is obtained using a special kernel function in SVM by transforming sequences into features, but recurrent neural network outperforms SVM.
    Keywords: data mining, demographics, support vector machines, neural networks, classification, sequences similarity
    JEL: C14 J11
    Date: 2017–09–17
  3. By: Bekena, Sisay Menji
    Abstract: In this study Random Forest Classifier machine learning algorithm is applied to predict income levels of individuals based on attributes including education, marital status, gender, occupation, country and others. Income levels are defined as a binary variable 0 for income
    Keywords: random-forest classifier, data science
    JEL: A10 D1 D10
    Date: 2017–07–30
  4. By: Igor Halperin
    Abstract: The QLBS model is a discrete-time option hedging and pricing model that is based on Dynamic Programming (DP) and Reinforcement Learning (RL). It combines the famous Q-Learning method for RL with the Black-Scholes (-Merton) model's idea of reducing the problem of option pricing and hedging to the problem of optimal rebalancing of a dynamic replicating portfolio for the option, which is made of a stock and cash. Here we expand on several NuQLear (Numerical Q-Learning) topics with the QLBS model. First, we investigate the performance of Fitted Q Iteration for a RL (data-driven) solution to the model, and benchmark it versus a DP (model-based) solution, as well as versus the BSM model. Second, we develop an Inverse Reinforcement Learning (IRL) setting for the model, where we only observe prices and actions (re-hedges) taken by a trader, but not rewards. Third, we outline how the QLBS model can be used for pricing portfolios of options, rather than a single option in isolation, thus providing its own, data-driven and model independent solution to the (in)famous volatility smile problem of the Black-Scholes model.
    Date: 2018–01
  5. By: Emma Helen Rasmussen (Southern Africa Labour and Development Research Unit, School of Economics, University of Cape Town)
    Abstract: This paper explores the potential of a negative income tax to tackle South Africa's dual challenges of poverty and income inequality. Using a static, arithmetic microsimulation model with NIDS Wave 4 as the base dataset, we simulate a negative income tax in which recipients receive an income subsidy proportional to their income if it is below a set amount and a guaranteed subsidy if they have zero income. Two different sizes for the guaranteed subsidy are simulated, both pegged to recent poverty lines. The simulations show that the negative income tax significantly reduces both inequality and poverty levels, but that this necessarily comes at a high cost.
    Date: 2017
  6. By: Gioele Figus (CEP, Institute for International Public Policy, University of Strathclyde); J Kim Swales (Departrment of Economics, University of Strathclyde); Karen Turner (CEP, Institute for International Public Policy, University of Strathclyde)
    Abstract: In this paper we employ a partial equilibrium approach to model private transport consumption as a household self-produced commodity formed by vehicle and fuel use. We show that under certain conditions vehicle-augmenting technical improvements can reduce fuel use. We then extend the analysis through Computable General Equilibrium simulations for the UK in order to investigate the wider implications of vehicle-augmenting efficiency improvements when prices and nominal income are endogenous. With a conventional macroeconomic approach, improvements in the efficiency of household consumption simply change the composition of household demand. However, when we adjust the consumer price index for changes in the price of private transport service (not observable via a market price), as advocated in Gordon (2016) there is an additional supply-side stimulus to competitiveness.
    Keywords: technical progress, energy efficiency, private transport, energy service.
    JEL: C68 D58 Q43 Q48
    Date: 2017–05
  7. By: Takuji Arai; Yuto Imai; Ryo Nakashima
    Abstract: The authors aim to develop numerical schemes of the two representative quadratic hedging strategies: locally risk minimizing and mean-variance hedging strategies, for models whose asset price process is given by the exponential of a normal inverse Gaussian process, using the results of Arai et al. \cite{AIS}, and Arai and Imai. Here normal inverse Gaussian process is a framework of L\'evy processes frequently appeared in financial literature. In addition, some numerical results are also introduced.
    Date: 2018–01
  8. By: Akhilesh K. Sharma; Atul Sarma; Charanjit Kaur; Javier Cabrera; Deeksha Tayal
    Abstract: The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is one of the flagship programmes of the Government of India. The programme aims to deal with rural poverty and unemployment by assuring economic security to the rural poor, by providing guaranteed wage employment when other employment alternatives are scarce or inadequate. This study aims to evaluate the macroeconomic impacts of the MGNREGA on the Indian economy by running counterfactual simulations with the aid of PEP-1-1 CGE model. The findings indicate that MGNREGA has increased the real GDP of the economy as well as household income and real consumption budget. The increase in household income is higher for the bottom quintile classes in comparison to the richer households. If the MGNREGA expenditure is reallocated to educational services, medical services, and public administration, the GDP of the economy as well as household income will decline.
    Keywords: MGNREGA, unskilled labour, GDP, household income, social accounting matrix, CGE modeling
    JEL: C68 C16 H53
    Date: 2017
  9. By: Prema-chandra Athukorala; Peter B. Dixon; Maureen T. Rimmer
    Abstract: Economists have analysed global supply chains (GSC) using pure theory, case studies, econometrics and input-output calculations. We now need a new type of computable general equilibrium (CGE) model to show how GSC trade affects welfare and its distribution between and within nations. The new model must recognize: fragmentation of production; scale economies; intermediate inputs that cross national borders multiple times embodied in products at different stages of completion; and decision-making by global agents. We describe a prototype that incorporates these features and gives interpretable results not attainable with a standard CGE model. We discuss steps to move from the prototype to a policy-relevant model.
    JEL: F12 C68 C67
    Date: 2017–12
  10. By: Giovanni Dosi (Scuola Superiore Sant'Anna Pisa Italy); Andrea Roventini (Scuola Superiore Sant'Anna Pisa Italy also OFCE Sciences Po Paris); Emanuele Russo (Superiore Sant'Anna,Pisa Italy & OFCE Sciences Po Paris France)
    Abstract: In this paper we present a multi-country, multi-industry agent-based model investigating the different growth patterns of interdependent economies. Each country features a Schumpeterian engine of endogenous technical change which interacts with Keyneasian/Kaldorian demand generation mechanisms. National growth trajectories are driven by firms’ accumulation of technological knowledge, which in turn also leads to emergent specialization patterns in different industries. Interactions among economies occur via trade flows, stemming from the competition of firms in international markets. Simulation results show the emergence of persistent income divergence among countries leading to polarization and club formation. Moreover, each country experiences a structural transformation of its productive structure during the development process. Such dynamics results from firm-level virtuous (or vicious) cycles between knowledge accumulation, trade performances, and growth dynamics. The model accounts for a rich ensemble of empirical regularities at macro, meso and micro levels of aggregation.
    Keywords: Endogenous growth, structural change, technology-gaps, global divergence, absolute advantages, agent based models
    JEL: F41 F43 O4 O3
    Date: 2018–01–15
  11. By: Gioele Figus (CEP, International Public Policy Unit, University of Strathclyde); Patrizio Lecca (European Union); Peter McGregor (Department of Economics, University of Strathclyde); Karen Turner (CEP, International Public Policy Unit, 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: C68 D58 Q43 Q48 R28 R58
    Date: 2017–02
  12. By: Elena Lagomarsino (; Karen Turner (Centre for Energy Policy, University of Strathclyde)
    Abstract: Computable general equilibrium (CGE) studies are increasingly in-terested in informing keys parameters of their models using empirical data. Energy and environmental CGE ndings have been found to be particularly sensible to changes in the values of the elasticities of substitution between inputs of production. Although applied econometric literature provides numerous estimates of substitution elasticities obtained from fexible functional forms cost or production functions, the number of papers dealing with Constant Elasticities of Substitution (CES) production functions, generally favoured in a CGE framework, is still limited. The contribution of this paper is to estimate the substitution relationship between energy and other inputs for the United Kingdom using a new approach that allows to understand whether a nested CES production function is adequate to describe the true input-output relationship. Moreover, the approach can be used to obtain an indication on which nested structure should is the most appropriate for the data considered. Findings suggest that the analysed dataset might support a four-input nested CES production function where the energy-capital are combined in an inner nest.
    Keywords: elasticities of substitution, Translog production function, nested CES, input separability, general equilibrium
    JEL: D24 C68 D58 R15 Q43
    Date: 2017–11
  13. By: Ermias Engida; Mekdim Dereje; Ibrahim Worku; Saba Yifredew; Feiruz Yimer
    Abstract: Given the fact that Micro and Small scale enterprises (MSEs) are high on the Ethiopian government’s agenda for mid-term growth and transformation plan (GTP), this study aims to investigate the major contributions and the potential of this sector to the Ethiopian economy. Using a CGE modeling approach, we assess the role of MSEs towards the major development goals of the government: unemployment and poverty reduction. Three simulation scenarios were designed based on the current MSE development plan but with different implementation strategies. The strategy that the government is currently following to implement the MSE development plan was found to be performing the best on expanding overall production, but failed to tackle the critical issues of poverty and unemployment reduction. However, other alternative strategies were found to give the country the best solutions to these development concerns as well as investment. Female unemployment also reduced the most in these alternative scenarios. This shows that the MSE sector has the potential to meet the envisaged developmental goals in Ethiopia, but strategy adjustment is needed.
    Date: 2017
  14. By: Peter B. Dixon; Michael Jerie; Maureen T. Rimmer; Glyn Wittwer
    Abstract: The Terrorism Risk Assessment (TRA) groups in the Department of Homeland Security assess millions of terrorism scenarios defined by location, agent (e.g. nuclear device), and delivery method (e.g. car bomb). For each scenario they estimate deaths, injuries, property damage, clean-up and health expenses, visitor discouragement, and other damage dimensions. The TRA groups translate damages into economic measures, e.g. loss of GDP. Previously they used an input-output (I-O) model. Here we replace I-O with computable general equilibrium (CGE). Solving CGE models is computationally time-consuming and requires specialist skills. For the TRA groups this creates two challenges: feasibility and security. A model that cannot be solved in less than a fraction of a second is infeasible for analyzing millions of scenarios. The TRAs can rely only on people with high security clearances, limiting the possibilities for obtaining specialist advice. Our approach to these challenges was to use a CGE model to estimate elasticities that show the sensitivity of economic variables to direct damage effects of events occurring in different regions. For example, we supplied the TRA groups with CGE-based estimates of the percentage effect on national welfare of destruction of 1 per cent of the capital stock in congressional district NY14. Our elasticity approach meets both challenges. First, for any given terrorism scenario specified by a location and a vector of direct damage shocks, the TRA groups can use the elasticities in linear equations to estimate in nanoseconds the implications for a wide range of economic variables. Second, as outside contractors, we have no need for access to sensitive information on specific shock vectors and target regions. We describe how we used a dynamic, multi-regional CGE model, USAGE-TERM, to estimate the elasticities.
    JEL: C68 F52 R13
    Date: 2017–04
  15. By: Rilwan Adewoyin
    Abstract: In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators. I approach this by training separate classifiers on the equivalent dataset from a range of countries. Using these classifiers, a three level meta learning algorithm (MLA) is developed. I develop a transform, ASG, to create a country agnostic proxy for the macro-financial indicators. Using these proposed methods, I investigate the degree to which a predictive algorithm for the US 5Y bond price, predominantly using macro-financial indicators, can outperform an identical algorithm which only uses statistics deriving from previous price.
    Date: 2017–12
  16. By: Ear Sothy; Sim Sokcheng; Khiev Pirom
    Abstract: Employing the available social accounting matrix, this paper examines the impacts of different public education consumption schemes on Cambodian macroeconomics, the labour market and household welfare. The results from the simulation scenarios in the CGE model revealed that the reallocation of public spending from primary and secondary education to higher education produced a negative impact on the wage rate of low and fairly educated labour, dropped outputs, and reduced household welfare, which had adverse effects on macroeconomic variables in general. However, the shift of public spending from administration to the three education sectors, showed positive impacts on the economy, household income and welfare. Given the factor endowment structure of the Cambodian education sector, the policy that focuses on higher education by providing more spending to this sector did not yield results as good as keeping the initial education spending structure.
    Keywords: Public education spending, labour market, household Welfare, CGE, simulation modeling
    JEL: C63 C67 C68
    Date: 2017
  17. By: Tetsuya Takaishi
    Abstract: We perform a large-scale simulation of an Ising-based financial market model that includes 300 asset time series. The financial system simulated by the model shows a fat-tailed return distribution and volatility clustering and exhibits unstable periods indicated by the volatility index measured as the average of absolute-returns. Moreover, we determine that the cumulative risk fraction, which measures the system risk, changes at high volatility periods. We also calculate the inverse participation ratio (IPR) and its higher-power version, IPR6, from the absolute-return cross-correlation matrix. Finally, we show that the IPR and IPR6 also change at high volatility periods.
    Date: 2018–01
  18. By: Chen, Jiaqi (Federal Reserve Bank of Dallas); Wu, Wenbo (University of Oregon); Tindall, Michael (Federal Reserve Bank of Dallas)
    Abstract: A machine-learning approach is employed to forecast hedge fund returns and perform individual hedge fund selection within major hedge fund style categories. Hedge fund selection is treated as a cross-sectional supervised learning process based on direct forecasts of future returns. The inputs to the machine-learning models are observed hedge fund characteristics. Various learning processes including the lasso, random forest methods, gradient boosting methods, and deep neural networks are applied to predict fund performance. They all outperform the corresponding style index as well as a benchmark model, which forecasts hedge fund returns using macroeconomic variables. The best results are obtained from machine-learning processes that utilize model averaging, model shrinkage, and nonlinear interactions among the factors.
    Keywords: Hedge fund selection; hedge fund return prediction; machine learning; the lasso; random forest; gradient boosting; deep neural networks
    Date: 2016–11–01
  19. By: Antonios Katris (Centre for Energy Policy, University of Strathclyde); Gioele Figus (Centre for Energy Policy, University of Strathclyde); Karen Turner (Centre for Energy Policy, University of Strathclyde)
    Abstract: Social Accounting Matrices (SAM) are used as the main data source for Computable General Equilibrium (CGE) models, which in turn can be used to simulate economic shocks and study their economy-wide impact. However, SAMs usually include highly aggregated categories of final consumers, therefore preventing the use of CGE models to study the impact of economic shocks on economic agents with specific characteristics. For this type of analysis, it is necessary to disaggregate the economic agent of interest in the SAM. This paper presents an approach by which it is possible to disaggregate the households in the UK SAM in quintiles based on their gross weekly income. The necessary steps to disaggregate the households are presented in detail, along with the supporting datasets and variables required to replicate the disaggregation approach for future SAMs.
    Keywords: social accounting matrix, household disaggregation, income quintiles
    JEL: D57 D58
    Date: 2017–08
  20. By: Heidarzadeh, Elham; Sajadnia, Sahar
    Abstract: It is apparent that outpatient clinics are becoming complex and need to be optimized and improved on a daily basis. In this project, we used several methods including discrete event simulation, quality function deployment (QFD), and failure modes and effects analysis (FMEA) to optimize and improve these clinics. We conducted this study at a major suburban outpatient clinic to propose main recommendations which most likely apply to a vast majority of such clinics. Firstly, the simulation-based modeling that we ran assisted us in recognizing optimum staff number which would result in decreasing waiting times that patients usually spend and making the process flow at the facility smoother. Secondly, QFD approach for analyzing outpatient clinic requirement is also proposed and realized through a case study. It is realized that the proposed approach can adjust service quality toward customer requirements effectively. Lastly, the health care failure modes and effects analysis (FMEA) that we implemented as a novel method to discover conditions and active failures and to prioritize these based on the potential severity of risks associated with them.
    Keywords: Outpatient clinic, discrete event simulation, quality function deployment (QFD), failure modes and effects analysis (FMEA)
    JEL: C8 C83 L8 L80
    Date: 2017–10–31
  21. By: Boppart, Timo; Krusell, Per; Mitman, Kurt
    Abstract: We propose a new method for computing equilibria in heterogeneous-agent models with aggregate uncertainty. The idea relies on an assumption that linearization offers a good approximation; we share this assumption with existing linearization methods. However, unlike those methods, the approach here does not rely on direct derivation of first-order Taylor terms. It also does not use recursive methods, whereby aggregates and prices would be expressed as linear functions of the state, usually a very high-dimensional object (such as the wealth distribution). Rather, we rely merely on solving nonlinearly for a deterministic transition path: we study the equilibrium response to a single, small "MIT shock" carefully. We then regard this impulse response path as a numerical derivative in sequence space and hence provide our linearized solution directly using this path. The method can easily be extended to the case of many shocks and computation time rises linearly in the number of shocks. We also propose a set of checks on whether linearization is a good approximation. We assert that our method is the simplest and most transparent linearization technique among currently known methods. The key numerical tool required to implement it is value-function iteration, using a very limited set of state variables.
    Keywords: computation; Heterogeneous Agents; linearization; MIT shock.
    Date: 2017–12

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. 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.