nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒12‒09
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The complexity of the intangible digital economy: an agent-based model By Bertani, Filippo; Ponta, Linda; Raberto, Marco; Teglio, Andrea; Cincotti, Silvano
  2. Multi-Scale RCNN Model for Financial Time-series Classification By Liu Guang; Wang Xiaojie; Li Ruifan
  3. Exports and imports in Zimbabwe: recent insights from artificial neural networks By NYONI, THABANI
  4. Investigating the Impacts of Customer Experience and Attribute Performances on Overall Ratings using Online Review Data: Nonlinear Estimation and Visualization with a Neural Network By Toshikuni Sato
  5. Insights from self-organizing maps for predicting accessibility demand for healthcare infrastructure By Mayaud, Jerome; Anderson, Sam; Tran, Martino; Radic, Valentina
  6. Incremental Risk Charge Methodology By Xiao, Tim
  7. A Monte Carlo Simulation Framework to Track Panama NDC Target By Suarez, Ronny
  8. Sectoral Impacts of International Labour Migration and Population Ageing in the Czech Republic By Martin Stepanek
  9. Deep Reinforcement Learning for Trading By Zihao Zhang; Stefan Zohren; Stephen Roberts
  10. A singular stochastic control approach for optimal pairs trading with proportional transaction costs By Haipeng Xing
  11. Collectivised Pension Investment with Homogeneous Epstein-Zin Preferences By John Armstrong; Cristin Buescu
  12. Enhancing CGE analysis with PE modelling of Kenyan agricultural and trade policy reforms By Binfield, Julian; Boulanger, Pierre; Davids, Tracy; Dudu, Hasan; Ferrari, Emanuele; Mainar-Causape, Alfredo; Meyer, Ferdi
  13. Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine By Siahaan, Andysah Putera Utama; Nasution, Muhammad Dharma Tuah Putra
  14. Logics and practices of transparency and opacity in real-world applications of public sector machine learning By Veale, Michael
  15. The Spatial Structure of US Metropolitan Employment: New Insights from LODES Data By Manduca, Robert
  16. Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making By Veale, Michael; Van Kleek, Max; Binns, Reuben

  1. By: Bertani, Filippo; Ponta, Linda; Raberto, Marco; Teglio, Andrea; Cincotti, Silvano
    Abstract: During the last decades, we have witnessed a strong development of intangible digital technologies. Software, artificial intelligence and algorithms are increasingly affecting both production systems and our lives; economists have started to figure out the long-run complex economic implications of this new technological wave. In this paper, we address this question through the agent-based modelling approach. In particular, we enrich the macroeconomic model Eurace with the concept of intangible digital technology and investigate its effects both at the micro and macro level. Results show the emergence of the relevant stylized facts observed in the business domain, such as increasing returns, winner-take-most phenomena and market lock-in. At the macro level, our main finding is an increasing unemployment level, since the sizeable decrease of the employment rate in the mass-production system, provided by the higher productivity of digital assets, is usually not counterbalanced by the new jobs created in the digital sector.
    Keywords: Intangible assets, Digital transformation, Technological unemployment, Agent-based economics
    JEL: C63 D24 O33
    Date: 2019–11–21
  2. By: Liu Guang; Wang Xiaojie; Li Ruifan
    Abstract: Financial time-series classification (FTC) is extremely valuable for investment management. In past decades, it draws a lot of attention from a wide extent of research areas, especially Artificial Intelligence (AI). Existing researches majorly focused on exploring the effects of the Multi-Scale (MS) property or the Temporal Dependency (TD) within financial time-series. Unfortunately, most previous researches fail to combine these two properties effectively and often fall short of accuracy and profitability. To effectively combine and utilize both properties of financial time-series, we propose a Multi-Scale Temporal Dependent Recurrent Convolutional Neural Network (MSTD-RCNN) for FTC. In the proposed method, the MS features are simultaneously extracted by convolutional units to precisely describe the state of the financial market. Moreover, the TD and complementary across different scales are captured through a Recurrent Neural Network. The proposed method is evaluated on three financial time-series datasets which source from the Chinese stock market. Extensive experimental results indicate that our model achieves the state-of-the-art performance in trend classification and simulated trading, compared with classical and advanced baseline models.
    Date: 2019–11
    Abstract: This study, which is the first of its kind in the case of Zimbabwe; attempts to model and forecast Zimbabwe’s exports and imports using annual time series data ranging over the period 1975 – 2017. In order to analyze Zimbabwe’s export and import dynamics, the study employed the Neural Network approach, a deep-learning technique which has not been applied in this area in the case of Zimbabwe. The Hyperbolic Tangent function was selected and applied as the activation function of the neural networks applied in this study. The neural networks applied in this research were evaluated using the most common forecast evaluation statistics, i.e. the Error, MSE and MAE; and it was clearly shown that the neural networks yielded reliable forecasts of Zimbabwe’s exports and imports over the period 2018 – 2027. The main results of the study indicate that imports will continue to outperform exports over the out-of-sample period. Amongst other policy recommendations, the study encourages Zimbabwean policy makers to intensify export growth promotion policies and strategies such as clearly identifying export drivers as well as export diversification if persistant current account deficits in Zimbabwe are to be dealt with effectively.
    Keywords: ANNs; exports; forecast; hyperbolic tangent function; imports; trade deficits; Zimbabwe
    JEL: F13 P33 Q17
    Date: 2019–11–04
  4. By: Toshikuni Sato
    Abstract: This study investigates interpretable neural networks for marketing and consumer behavior research using customer reviews instead of measurement scales to better understand customer experiences. Service attribute ratings are used to measure attribute performances to compare the influence of customer experience and service performance on overall satisfaction. Although many researchers have investigated word-of-mouth reviews and their practical applications, the detailed contents of those reviews were generally disregarded, possibly because of their high dimensionality. To solve this problem, this study proposes some useful neural-network methods for specifying the expected assumptions based on previous knowledge or theories in consumer behavior research. Because neural networks help estimate nonlinear relationships between objective and predictive variables, a partial dependence plot is used to visualize the estimated functions and marginal effects. Empirical results not only provide a highly accurate neural-network model, they also create better marketing implications.
    Date: 2019–11
  5. By: Mayaud, Jerome; Anderson, Sam; Tran, Martino; Radic, Valentina
    Abstract: As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility – the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application remains limited in this field. In this study, we aim to more robustly relate socio-economic factors to healthcare accessibility across a city experiencing rapid population growth, using a novel combination of clustering methods. We applied a powerful ML clustering tool, the self-organising map (SOM), in conjunction with principal component analysis (PCA), to examine how income shifts over time (2016–2022) could affect accessibility equity to healthcare for senior populations (65+ years) in the City of Surrey, Canada. We characterised accessibility levels to hospitals and walk-in clinics using door-to-door travel times, and combined this with high-resolution census data. Higher income clusters are projected to become more prevalent across the city over the study period, in some cases incurring into previously low income areas. However, low income clusters have on average much better accessibility to healthcare facilities than high income clusters, and their accessibility levels are projected to increase between 2016 and 2022. By attributing temporal differences through cross-term analysis, we show that population growth will be the biggest accessibility challenge in neighbourhoods with existing access to healthcare, whereas income change (both positive and negative) will be most challenging in poorly connected neighbourhoods. A dual accessibility problem may therefore arise in Surrey. First, large senior populations will reside in areas with access to numerous, and close-by, clinics, putting pressure on existing facilities for specialised services. Second, lower-income seniors will increasingly reside in areas poorly connected to healthcare services; since these populations are likely to be highly reliant on public transportation, accessibility equity may suffer. To our knowledge, this study is the first to apply a combination of PCA and SOM techniques in the context of urban accessibility, and it demonstrates the value of this clustering approach for drawing planning policy recommendations from large multivariate datasets.
    Date: 2018–10–28
  6. By: Xiao, Tim
    Abstract: The incremental risk charge (IRC) is a new regulatory requirement from the Basel Committee in response to the recent financial crisis. Notably few models for IRC have been developed in the literature. This paper proposes a methodology consisting of two Monte Carlo simulations. The first Monte Carlo simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second Monte Carlo simulation is the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC.
    Date: 2018–08–16
  7. By: Suarez, Ronny
    Abstract: The 2015 Paris Agreement represents a restarting point for combating climate change. The Agreement introduces the National Determined Contributions (NDC) to control greenhouse gas emissions. This paper provides a step-by-step framework to evaluate Panama’s renewable energy contribution commitment in terms of CO2eq mitigation. Monte Carlo Simulations are used to compute dynamic scenarios of MtCO2eq emissions determining that the occurrence of delays in the entry into operation of specific projects combined with the presence of El Niño phenomenon could increase, up to 45%, the value of the CO2eq emissions compared against baseline scenario.
    Keywords: Climate Change, Paris Agreement, NDC, Panama, CO2eq, Monte Carlo
    JEL: A1 C8
    Date: 2019–11–18
  8. By: Martin Stepanek (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic)
    Abstract: This study assesses macroeconomic and sectoral impacts of demographic changes in the Czech Republic, as a result of population ageing and international migration. To do so, it develops a unique dynamic Overlapping Generations Computable General Equilibrium (OLG-CGE) model with detailed representation of individuals of different ages, educational attainment and occupations, as well as interrelations among industrial sectors in producing intermediate and final outputs. The numeric simulations show that the Czech economy will face a substantial reduction in its effective labour supply and changes in sectoral demand patterns, leading to an increase in unit labour costs and consequent shift towards more capitalbased production, price increase for the consumers, and a long-term decrease in demand particularly for agricultural products. While international migration may alleviate the pressure, the annual net immigration would need to increase by at least 8 thousand individuals on average in the 2020-2035 period and by 17 thousand individuals in the 2036-2050 period to offset the negative effects in the long term.
    Keywords: OLG, CGE, migration, labour force, economic impact
    JEL: C68 E27 J11
    Date: 2019–06
  9. By: Zihao Zhang; Stefan Zohren; Stephen Roberts
    Abstract: We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show that our method outperforms such baseline models, delivering positive profits despite heavy transaction costs. The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods.
    Date: 2019–11
  10. By: Haipeng Xing
    Abstract: Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and number of shares in pairs trading process, we use a singular stochastic control approach to study an optimal pairs trading problem with proportional transaction costs. Assuming a cointegrated relationship for a pair of stock log-prices, we consider a portfolio optimization problem which involves dynamic trading strategies with proportional transaction costs. We show that the value function of the control problem is the unique viscosity solution of a nonlinear quasi-variational inequality, which is equivalent to a free boundary problem for the singular stochastic control value function. We then develop a discrete time dynamic programming algorithm to compute the transaction regions, and show the convergence of the discretization scheme. We illustrate our approach with numerical examples and discuss the impact of different parameters on transaction regions. We study the out-of-sample performance in an empirical study that consists of six pairs of U.S. stocks selected from different industry sectors, and demonstrate the efficiency of the optimal strategy.
    Date: 2019–11
  11. By: John Armstrong; Cristin Buescu
    Abstract: In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We compute analytically the optimal investment-consumption strategy for a fund of $n$ identical investors with homogeneous Epstein--Zin preferences, investing in the Black--Scholes market in continuous time but consuming in discrete time. Our result holds for arbitrary mortality distributions. We also compute the optimal strategy for an infinite fund of investors, and prove the convergence of the optimal strategy as $n\to \infty$. The proof of convergence shows that effective strategies for inhomogeneous funds can be obtained using the optimal strategies found in this paper for homogeneous funds, using the results of [2]. We find that a constant consumption strategy is suboptimal even for infinite collectives investing in markets where assets provide no return so long as investors are "satisfaction risk-averse." This suggests that annuities and defined benefit investments will always be suboptimal investments. We present numerical results examining the importance of the fund size, $n$, and the market parameters.
    Date: 2019–11
  12. By: Binfield, Julian; Boulanger, Pierre; Davids, Tracy; Dudu, Hasan; Ferrari, Emanuele; Mainar-Causape, Alfredo; Meyer, Ferdi
    Keywords: Research Methods/ Statistical Methods, Agricultural and Food Policy
    Date: 2019–09
  13. By: Siahaan, Andysah Putera Utama (Universitas Pembangunan Panca Budi); Nasution, Muhammad Dharma Tuah Putra
    Abstract: This study discusses the prediction model of entrepreneurship intent for alumni. The data is obtained from the database of an online job market, alumni tracer and survey results to the alumni. This research applies the C4.5 decision tree algorithm to get a prediction model that shows the intention of entrepreneurship. Some essential indicators include Self-efficacy, Need for Achievement, Advisory Quotient, Locus of Control and Passion. The predictive model found that the best predictor was Self-efficacy which contributed to influence the entrepreneurship intention with a value of 79.7 percent. The authors recommend to educational institutions to foster candidate interest through curriculum improvement, field practice or learning models in and out of the classroom.
    Date: 2018–06–30
  14. By: Veale, Michael
    Abstract: Presented as a talk at the 4th Workshop on Fairness, Accountability and Transparency in Machine Learning (FAT/ML 2017), Halifax, Nova Scotia, Canada. Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, have considered how logics and practices concerning transparency have been understood by those involved in the machine learning systems already being piloted and deployed in public bodies today. This short paper distils insights about transparency on the ground from interviews with 27 such actors, largely public servants and relevant contractors, across 5 OECD countries. Considering transparency and opacity in relation to trust and buy-in, better decision-making, and the avoidance of gaming, it seeks to provide useful insights for those hoping to develop socio-technical approaches to transparency that might be useful to practitioners on-the-ground.
    Date: 2017–11–18
  15. By: Manduca, Robert
    Abstract: Urban researchers have long debated the extent to which urban employment is monocentric, polycentric, or diffuse. In this paper I use high-resolution data based on unemployment insurance records to show that employment in US metropolitan areas is not centralized but is spatially concentrated. Unlike residents, who form a continuous surface covering most parts of each MSA, jobs have a bimodal spatial distribution, with most blocks containing no jobs whatsoever and a small number having extremely high employment densities. Across the 100 largest MSAs about 75% of jobs are located on the 10% of built land in Census blocks with at least twice as many jobs as people. Further, most of these jobs are in clustered business districts of more than 5 contiguous employment blocks. These relative proportions are extremely consistent across MSAs, even though cities vary greatly in the physical density at which they are constructed. Motivated by these empirical regularities, I introduce an algorithm to identify contiguous business districts and classify them into four major types. Based solely on the relative densities of employment and population, this algorithm is both simpler to implement and more flexible than current approaches, requiring no metro-specific tuning parameters and no assumptions about urban form. As one output, it provides an inductive, data-driven method of identifying city centers for the purposes of urban economic analysis.
    Date: 2018–09–14
  16. By: Veale, Michael; Van Kleek, Max; Binns, Reuben
    Abstract: Cite as: Michael Veale, Max Van Kleek and Reuben Binns (2018) Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. ACM Conference on Human Factors in Computing Systems (CHI'18). doi: 10.1145/3173574.3174014 Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions—like taxation, justice, and child protection—are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning—absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the `street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.
    Date: 2018–02–04

This nep-cmp issue is ©2019 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.
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