nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒05‒07
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method? By Steinkraus, Arne
  2. Inequality and Imbalances : a Monetary Union Agent-Based Model By Alberto Cardacci; Francesco Saraceno
  3. Rational Heuristics ? Expectations and behaviors in Evolving Economies with Heterogeneous interacting agents By Giovanni Dosi; Mauro Napoletano; Andrea Roventini; Joseph Stiglitz; Tania Treibich
  4. A Mixed Integer Linear Programming Model to Regulate the Electricity Sector By Polemis, Michael
  5. Endogenous growth and global divergence in a multi-country agent - based model By Giovanni Dosi; Andrea Roventini; Emmanuele Russo
  6. Eradicating poverty by 2030: Implications for Income Inequality, Population Policies, Food Prices (and Faster Growth?) By Giovanni Andrea Cornia
  7. What if supply-side policies are not enough ? The perverse interaction of flexibility and austerity By Giovanni Dosi; Marcelo C. Pereira; Andrea Roventini; Maria Enrica Virgillito
  8. Does More Female Labor Supply Really Save a Graying Japan? By Ryuta Ray Kato
  9. The quantification of text - Supervised learning methods - The application of textual sentiment indicators to the UK CRE market By Steffen Heinig
  10. Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections By Chang, C-L.; McAleer, M.J.; Wong, W.-K.
  11. Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis By Dimitrios Papastamos; Antonis Alexandridis; Dimitris Karlis
  12. Machine Learning Forecasts of Public Transport Demand: A comparative analysis of supervised algorithms using smart card data By Sebastián M. Palacio
  13. Branch-Cut-and-Price for the Scheduling Deliveries with Time Windows in a Direct Shipping Network By Timo Gschwind; Stefan Irnich; Simon Emde; Christian Tilk
  14. The behavioral economics of currency unions: Economic integration and monetary policy By Akvile Bertasiute; Domenico Massaro; Matthias Weber
  15. Detecting Outliers with Semi-Supervised Machine Learning: A Fraud Prediction Application By Sebastián M. Palacio
  16. The Future Prospect of the Long-term Care Insurance in Japan By Ryuta Ray Kato
  17. Can the US shale revolution be duplicated in continental Europe? An economic analysis of European shale gas resources By Aurélien Saussay
  18. Image Analyses and Real Estate: Evaluation of the Quality of Location Using Remotely Sensed Imagery By Miroslav Despotovic; David Koch; Gunther Maier; Matthias Zeppelzauer
  19. Agents' beliefs and economic regimes polarization in interacting markets By Fausto Cavalli; Ahmad Naimzada; Nicol\`o Pecora; Marina Pireddu
  20. Robust Log-Optimal Strategy with Reinforcement Learning By Yifeng Guo; Xingyu Fu; Yuyan Shi; Mingwen Liu

  1. By: Steinkraus, Arne
    Abstract: With the advent of big data in economics machine learning algorithms become more and more appealing to economists. Despite some attempts of establishing artificial neural networks in in the early 1990s, only little is known about their ability of estimating causal effects in policy evaluation. We employ a simple forecasting neural network to analyze the effect of the construction of the Oresund bridge on the local economy. The outcome is compared to the causal effect estimated by the proven Synthetic Control Method. Our results suggest that – especially in so-called prediction policy problems – neural nets may outperform traditional approaches.
    Keywords: Artificial Neural Nets,Machine Learning,Synthetic Control Method,Policy Evaluation
    JEL: C45 O18
    Date: 2018
  2. By: Alberto Cardacci (Lombardy Advanced School of Economics Milan); Francesco Saraceno (Observatoire français des conjonctures économiques)
    Abstract: Our paper investigates the impact of rising inequality in a two-country macroeconomic model with an agent-based household sector characterised by peer effects in consumption. In particular, the model highlights the role of inequality in determining diverging balance of payments dynamics within a currency union. Inequality may drive the two countries into different growth patterns: where peer effects in consumption interact with higher credit availability, rising income inequality leads to the emergence of a debt-led growth. Where social norms determine weaker emulation and credit availability is lower, an export-led regime arises. Eventually, a crisis emerges endogenously due to the sudden-stop of capital ows from the net lending country, triggered by the excessive risk associated to the dramatic amount of private debt accumulated by households in the borrowing country. Monte Carlo simulations for a wide range of calibrations confirm the robustness of our results.
    Keywords: Inequality; Current Account; Currency Union; Agent -based model
    JEL: C63 D31 E21 F32 F43
    Date: 2017–12
  3. By: Giovanni Dosi (Laboratory of Economics and Management); Mauro Napoletano (Observatoire français des conjonctures économiques); Andrea Roventini (Laboratory of Economics and Management (LEM)); Joseph Stiglitz (Columbia Business School); Tania Treibich (Observatoire français des conjonctures économiques)
    Abstract: We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information and coordination hurdles. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naïve learning rules. In fact, more sophisticated, e.g. recursive least squares (RLS) expectations produce less accurate individual forecasts and also considerably worsen the performance of the economy. Finally, we experiment with agents that adjust simply to technological shocks, and we show that individual and aggregate performances dramatically degrade. Our results suggest that fast and frugal robust heuristics are not a second-best option: rather they are “rational” in macroeconomic environments with heterogeneous, interacting agents and changing “fundamentals”.
    Keywords: Complexity; Expectations; Heterogeneity; Heuristics; Learning; Agent based model; Computational economics
    JEL: C63 E32 E6 G1 G21 O4
    Date: 2017–12
  4. By: Polemis, Michael
    Abstract: This paper introduces the concept of market design and make the distinction between the three different levels of market design such as industry structure, wholesale and marketplace design. We present a mixed-integer linear programming (MILP) model for the optimal long-term electricity planning of the Greek wholesale generation system. In order to capture more accurately the technical characteristics of the problem, we have divided the Greek territory into a number of individual interacted networks (geographical zones). In the next stage we solve the system of equations and provide simulation results for the daily/hourly energy prices based on the different scenarios adopted. The empirical findings reveal an inverted-M shaped curve for electricity demand in Greece, while the SMP curve is also non-linear. Lastly, given the simulations results, we provide the necessary policy implications for government officials, regulators and the rest of the marketers.
    Keywords: Electricity market; Linear programming; Constraints; Day-ahead scheduling; Mathematical programming.
    JEL: C60 L94 Q40
    Date: 2018–01–30
  5. By: Giovanni Dosi (Laboratory of Economics and Management); Andrea Roventini (Laboratory of Economics and Management (LEM)); Emmanuele Russo (Scuola Superiore Sant'Anna)
    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
  6. By: Giovanni Andrea Cornia
    Abstract: The paper examines whether the planned eradication of poverty to the year 2030 part of the SDG strategy is compatible with the expected trends in key economic variables such as GDP growth, population growth, income inequality and food prices. To do so, the paper develops a comparativestatic, poverty-accounting model that allows to simulate to 2030 the impact on SDG1 of the fastest improvements recorded for the above four variables during the last 30 years. Numerous model simulations show that – even under the most favorable assumptions – between 16 and 28 countries (mainly from Africa) out of the 78 analyzed will not reach the SDG1 target. Policy suggestions on how to improve on such results are presented at the end.
    Keywords: SDG1, poverty eradication, inequality, GDP growth, population growth, food prices, public policies
    JEL: D31 I32 J11 Q18
    Date: 2018–04
  7. By: Giovanni Dosi (Laboratory of Economics and Management); Marcelo C. Pereira (Universidade Estadual de Campinas); Andrea Roventini (Laboratory of Economics and Management (LEM)); Maria Enrica Virgillito (Scuola Superiore Sant'Anna)
    Abstract: In this work we develop a set of labour market and fiscal policy experiments upon the labour and credit augmented “Schumpeter meeting Keynes” agent-based model. The labour market is declined under two institutional variants, the “Fordist” and the “Competitive” set-ups meant to capture the historical transition from the Fordist toward the post “Thatcher- Reagan” period. Inside these two regimes, we study the different effects of supply-side active labour market policies (ALMPs) vs. demand-management passive labour market ones (PLMPs). In particular, we analyse the effects of ALMPs aimed at promoting job search, and at providing training to unemployed people. Next, we compare the effects of these policies with unemployment benefits simply meant to sustain income and therefore aggregate demand. Considering the burden of unemployment benefits in terms of public budget, we link such provision with the objectives of the European Stability and Growth Pact. Our results show that (i) an appropriate level of skills is not enough to sustain growth when workers face adverse labour demand; (ii) supply-side policies are not able to reverse the perverse interaction between flexibility and austerity; (iii) PLMPs outperform ALMPs in reducing unemployment and workers’ skills deterioration; and (iv) demand-management policies are better suited to mitigate inequality and to improve and sustain long-run growth.
    Keywords: Industrial -relation Regimes; Flexibility; Active Labour Market Policies; Austerity; Agent-based models
    JEL: C63 E24 H53 J88
    Date: 2018–01
  8. By: Ryuta Ray Kato (International University of Japan)
    Abstract: This paper examines the impact of stimulated female labor supply on the Japanese economy as well as the government fiscal imbalance within a numerical dynamic general equilibrium model with multiple overlapping generations, particularly by paying attention to females' time costs of child rearing and elderly care in a graying Japan. Several numerical results indicate that even complete elimination of females' time costs of child rearing and elderly care stimulates the total GDP only by 1 percent. If complete elimination of time costs occurs in accordance with no gender gap in wage profiles, then the total GDP expands by 4 percent. The results also suggest importance of government policies not only to stimulate female labor force participation but also to improve human capital accumulation of females to reduce a gender gap in wage profiles.
    Keywords: Female Labor Supply, Childcare, Child Allowance, Elderly Care, Public Pension, Long-Term Care Insurance, Population Aging, Japan, Simulation
    JEL: C68 H51 E62 H55 J16
    Date: 2017–09
  9. By: Steffen Heinig
    Abstract: In the real estate industry information are an essential good, which influences the behaviour of market participants. One main source of information about the market are news articles. For the financial markets and especially for the real estate market the quantification of text represents a new source for the extraction of market sentiment. In this study, I examine a newly constructed corpus of news articles regarding the London real estate market, with the help of supervised learning algorithms (i.e. SVM, Maximum Entropy, GLMNET). More than 100,000 articles are used over a period of 11 years (2004-2015). One central issue during this process is the annotation of the documents in the training corpus. Since the real estate market does not offer an annotated news corpus and labelling such a large corpus manually would be expensive in different ways, I propose a new method of how this gap can be overcome. The use of real estate related Amazon book reviews for the training process of the different classifiers has been proven to be quite promising. I used more than 220,000 reviews for the training process. The results suggest, that the book reviews are a good substitute and classifiers trained on the reviews are able to extract the sentiment from the articles. Satisfying graphical results reveal, at least for some of the different classifiers, that the underlying market sentiment was extracted. The textual sentiment indicators are also able to improve the performance of different models. In this study, I will use the textual indicators in a probit model to see whether they have any signalling power about future developments.
    Keywords: Natural Language Processing; Quantification of text; Sentiment Analysis; Supervised Learning algorithm
    JEL: R3
    Date: 2017–07–01
  10. By: Chang, C-L.; McAleer, M.J.; Wong, W.-K.
    Abstract: This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas.
    Keywords: Decision sciences, economics, finance, business, computing, and big data, theoretical models, econometric and statistical models, applications
    JEL: A10 G00 G31 O32
    Date: 2018–03–01
  11. By: Dimitrios Papastamos; Antonis Alexandridis; Dimitris Karlis
    Abstract: In recent years big financial institutions are interested in creating and maintaining property valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensible manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model for property valuation using a large database of historical prices from Greece. The Greek property market is an inefficient, non- homogeneous market, still at its infancy governed by lack of information. As a result modelling the Greek real estate market is a very challenging problem. The available data cover a big range of properties across time and include the financial crisis period in Greece which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and non-linear models based on regression, hedonic equations and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also to identify the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve performance. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in non- homogeneous, newly developed markets.
    Keywords: Artificial Neural Networks; Automated Valuation Models; Forecasting Accuracy; Residential Market; Valuations
    JEL: R3
    Date: 2017–07–01
  12. By: Sebastián M. Palacio (GiM, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona)
    Abstract: Public transport smart cards are widely used around the world. However, while they provide information about various aspects of passenger behavior, they have not been properly exploited to predict demand. Indeed, traditional methods in economics employ linear unbiased estimators that pay little attention to accuracy, which is the main problem faced by the sector’s regulators. This paper reports the application of various supervised machine learning (SML) techniques to smart card data in order to forecast demand, and it compares these outcomes with traditional linear model estimates. We conclude that the forecasts obtained from these algorithms are much more accurate.
  13. By: Timo Gschwind (Johannes Gutenberg-University); Stefan Irnich (Johannes Gutenberg-University); Simon Emde (Johannes Gutenberg-University); Christian Tilk (Technische Universität Darmstadt)
    Abstract: In a direct shipping (or point-to-point) network, individual deliveries are round trips from one supplier to one customer and back to either the same or another supplier, i.e., a truck can only visit one customer at a time before it has to return to a supplier. We consider the multiple sources, multiple sinks case, where a given set of direct deliveries from a set of suppliers to a set of customers must be scheduled such that the customer time windows are not violated, the truck fleet size is minimal, and the total weighted customer waiting time is as small as possible. Direct shipping policies are, for instance, commonly employed in just-in-time logistics (e.g., in the automotive industry) or in humanitarian logistics. We present an exact branch-cut-and-price algorithm for this problem, which is shown to perform well on instances from the literature and newly generated ones. We also investigate under what circumstances bundling suppliers in so-called supplier parks actually facilitates logistics operations under a direct shipping policy.
    Keywords: direct deliveries, branch-cut-and-price, weighted customer waiting times, just-in-time logistics
    Date: 2018–04–23
  14. By: Akvile Bertasiute (Budget Policy Monitoring Department, National Audit Office of Lithuania); Domenico Massaro (Universita Cattolica del Sacro Cuore & Complexity Lab in Economics); Matthias Weber (CEFER, Bank of Lithuania & Faculty of Economics, Vilnius University)
    Abstract: Currency unions are often modeled as homogeneous economies, although they are fundamentally different. The expectations that impact macroeconomic behavior in any given country are not the expectations of variables at the currency-union level but at the country level. We model these expectations with a behavioral reinforcement learning model. In our model, economic integration is of particular importance in determining whether economic behavior in a currency union is stable. Monetary policy alone is insufficient to guarantee stable economic behavior, as the central bank cannot conduct different monetary policies in different countries. These results are easily overlooked when modeling expectations as rational.
    Keywords: Behavioral Macroeconomics, Monetary Unions, Reinforcement Learning, Expectation Formation
    JEL: E52 D84
    Date: 2018–04–27
  15. By: Sebastián M. Palacio (GiM, Department of Econometrics, Statistics and Applied Economics, Universitat de Barcelona)
    Abstract: Abnormal pattern prediction has received a great deal of attention from both academia and industry, with applications that range from fraud, terrorism and intrusion detection to sensor events, medical diagnoses, weather patterns, etc. In practice, most abnormal pattern prediction problems are characterized by the presence of a small number of labeled data and a huge number of unlabeled data. While this points most obviously to the adoption of a semi-supervised approach, most empirical studies have opted for a simplification and treated it as a supervised problem, resulting in a severe bias of false negatives. In this paper, we propose an innovative methodology based on semi-supervised techniques and introduce a new metric the Cluster-Score for abnormal homogeneity measurement. Specifically, the methodology involves transmuting unsupervised models to supervised models using the Cluster-Score metric, which defines the objective boundaries between clusters and evaluates the homogeneity of the abnormalities in the cluster construction. We apply this methodology to a problem of fraud detection among property insurance claims. The objectives are to increase the number of fraudulent claims detected and to reduce the proportion of claims investigated that are, in fact, non-fraudulent. The results from applying our methodology considerably improved these objectives.
    Keywords: Outlier Detection, Semi-Supervised Models, Fraud, Cluster, Insurance
  16. By: Ryuta Ray Kato (International University of Japan)
    Abstract: This paper explores the impact of population aging on the Japanese public longterm care insurnace (LTCI) within a numerical dynamic general equilibrium model with multiple overlapping generations. The impact of three policy options, such as an increase in co-payments, an earlier starting age of contribution, and more distribution of the cost to the public sector, is also examined. The numerial results show that in the next about forty years the burdens on the first (age 65 and over) and second (age 40 to 64) groups become more than 1.7 times and more than 2.7 times as much, respectively. A relatively more increase in the burdens on the second group cannot be avaiodable, even if adjustment of the cost distribution between both groups is made every three years in the future in accordance with the schedule by the MHLW. Furthermore, in order to reduce future burdens in the LTCI, an increase in co-payments is most preferable, rather than an earlier starting age of contribution in longer duration of contribution with lower burdens every year, or a shift of the cost to the public sector followed by a very higher consumption tax.
    Keywords: Long-term Care Insurance, Population Aging, Japan, Simulation
    JEL: C68 H51 E62 H55 J16
    Date: 2017–10
  17. By: Aurélien Saussay (Observatoire français des conjonctures économiques)
    Abstract: Over the past decade, the rapid increase in shale gas and shale oil production in the United States has profoundly changed energy markets in North America, and has led to a significant decrease in American natural gas prices. The possible existence of large shale deposits in continental Europe, mainly in France, Denmark, the Netherlands and Germany, has fostered speculation on whether the U.S. shale revolution could be duplicated in Europe. However, a number of uncertainties, notably geological, technological, regulatory, and relating to public acceptance make this possibility unclear. We present a techno-economic model of shale gas production amenable to direct estimation on historical production data to analyze the main determinants of the profitability of shale wells and plays. We contribute an in-depth analysis of an extensive production dataset covering 40,000 wells and accounting for nearly 90% of shale gas production in the six main plays of the continental United States from 2004 to 2014. We combine this analysis with a discussion of the main differences between the American and European contexts to calibrate our model and conduct Monte-Carlo simulations. This enables us to estimate the distribution of breakeven prices for shale gas extraction in continental Europe. We find a median gross breakeven price before taxes and royalties of $10.1 per MMBtu. This would make extraction unprofitable in Europe in the current natural gas price environment, with
    Keywords: Shale gas; Extraction costs; United States ; Europe
    JEL: Q31 Q32 Q33 Q41 Q54
    Date: 2018–01
  18. By: Miroslav Despotovic; David Koch; Gunther Maier; Matthias Zeppelzauer
    Abstract: A growing number of applied studies examine the impact of urban space quality on property price. Especially the planning and development of the immediate neighborhood (micro location) is an important influencing factor in regional economics. An image-based method for the estimation of location quality, in the context of property valuation, does not exist yet. We develop method for the determination of the quality of location using image processing, taking at the same time into account the classification in quality classes based on regional structural characteristics. With the help of automatic image analysis, a new information source is leveraged, which previously could not be taken into account within the scope of evaluation of location quality or within the scope of automated valuation models (e.g. hedonic models). In the field of image analysis, the extraction of parameters related to location quality is a new task. It is so far not clear to which degree meaningful parameters can be found autonomously by machine learning. This dissertation will investigate this question in detail and is to our knowledge the first approach for the automatic image-based valuation of location quality.
    Keywords: Hedonic Pricing; Image Processing; location quality; Machine Learning; Neighborhoods
    JEL: R3
    Date: 2017–07–01
  19. By: Fausto Cavalli; Ahmad Naimzada; Nicol\`o Pecora; Marina Pireddu
    Abstract: In the present paper a model of a market consisting of real and financial interacting sectors is studied. Agents populating the stock market are assumed to be not able to observe the true underlying fundamental, and their beliefs are biased by either optimism or pessimism. Depending on the relevance they give to beliefs, they select the best performing strategy in an evolutionary perspective. The real side of the economy is described within a multiplier-accelerator framework with a nonlinear, bounded investment function. We show that strongly polarized beliefs in an evolutionary framework can introduce multiplicity of steady states, which, consisting in enhanced or depressed levels of income, reflect and reproduce the optimistic or pessimistic nature of the agents' beliefs. The polarization of these steady states, which coexist with an unbiased steady state, positively depends on that of the beliefs and on their relevance. Moreover, with a mixture of analytical and numerical tools, we show that such static characterization is inherited also at the dynamical level, with possibly complex attractors that are characterized by endogenously fluctuating pessimistic and optimistic levels of national income and price. This framework, when stochastic perturbations are included, is able to account for stylized facts commonly observed in real financial markets, such as fat tails and excess volatility in the returns distributions, as well as bubbles and crashes for stock prices.
    Date: 2018–04
  20. By: Yifeng Guo; Xingyu Fu; Yuyan Shi; Mingwen Liu
    Abstract: We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion. It avoids GLOS's complex CDF estimation process,hence resists the "Butterfly Effect" caused by estimation error. Besides,RLOS retains GLOS's profitability and the optimization problem involved in RLOS is computationally far more practical compared to GLOS. Further, we combine RLOS with Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the analyzed results from RLOS and observes the trading environment to make comprehensive investment decisions. The RLOSRL's performance is compared to some traditional strategies on several back tests, where we randomly choose a selection of constituent stocks of the CSI300 index as assets under management and the test results validate its profitability and stability.
    Date: 2018–05

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