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on Computational Economics |
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Issue of 2020‒08‒10
nineteen papers chosen by |
| By: | Montebruno, Piero; Bennett, Robert; Smith, Harry; van Lieshout, Carry |
| Abstract: | This paper presents a binary classification of entrepreneurs in British historical data based on the recent availability of big data from the I-CeM dataset. The main task of the paper is to attribute an employment status to individuals that did not fully report entrepreneur status in earlier censuses (1851-1881). The paper assesses the accuracy of different classifiers and machine learning algorithms, including Deep Learning, for this classification problem. We first adopt a ground-truth dataset from the later censuses to train the computer with a Logistic Regression (which is standard in the literature for this kind of binary classification) to recognize entrepreneurs distinct from non-entrepreneurs (i.e. workers). Our initial accuracy for this base-line method is 0.74. We compare the Logistic Regression with ten optimized machine learning algorithms: Nearest Neighbors, Linear and Radial Support Vector Machine, Gaussian Process, Decision Tree, Random Forest, Neural Network, AdaBoost, Naive Bayes, and Quadratic Discriminant Analysis. The best results are boosting and ensemble methods. AdaBoost achieves an accuracy of 0.95. Deep-Learning, as a standalone category of algorithms, further improves accuracy to 0.96 without using the rich text-data that characterizes the OccString feature, a string of up to 500 characters with the full occupational statement of each individual collected in the earlier censuses. Finally, and now using this OccString feature, we implement both shallow (bag-of-words algorithm) learning and Deep Learning (Recurrent Neural Network with a Long Short-Term Memory layer) algorithms. These methods all achieve accuracies above 0.99 with Deep Learning Recurrent Neural Network as the best model with an accuracy of 0.9978. The results show that standard algorithms for classification can be outperformed by machine learning algorithms. This confirms the value of extending the techniques traditionally used in the literature for this type of classification problem. |
| Keywords: | machine learning; deep learning; logistic regression; classification; big data; census |
| JEL: | M13 N83 |
| Date: | 2019–08–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100469&r=all |
| By: | Christoph Boehringer; Knut Einar Rosendahl (Norwegian University of Life Sciences, Ås / Norway, and Statistics Norway, Oslo / Norway) |
| Abstract: | Several European countries have decided to phase out coal power generation. Emissions from electricity generation are already regulated by the EU Emissions Trading System (ETS), and in some countries like Germany the phaseout of coal will be accompanied with cancellation of emissions allowances. In this paper we examine the consequences of phasing out coal, both for the broader economy, the electricity sector, and for CO2 emissions. We show analytically how the welfare impacts for a phaseout region depend on i) whether and how allowances are canceled, ii) whether other countries join phaseout policies, and iii) terms-of-trade effects in the ETS market. Based on numerical simulations with a computable general equilibrium model for the European economy, we quantify the economic and environmental impacts of alternative phaseout scenarios, considering both unilateral and multilateral phaseout. We find that terms-of-trade effects in the ETS market play an important role for the welfare effects across EU member states. For Germany, coal phaseout combined with unilateral cancellation of allowances is found to be welfare-improving if the German citizens value emissions reductions at 65 Euro per ton or more. |
| Keywords: | coal phaseout, emissions trading, electricity market |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:old:dpaper:430&r=all |
| By: | Dominique Guégan (Department of Economics, University Of Venice Cà Foscari; University Paris 1 Panthéon-Sorbonne; labEx ReFi Paris;) |
| Abstract: | We are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the “How”, i.e., how a black box or a very complex algorithm works, and the “Why”, i.e. why an algorithm produces such a result. These questions appeal to many actors, users, professions, regulators among others. Using a formal standardized framework, we indicate the solutions that exist by specifying which elements of the supply chain are impacted when we provide answers to the previous questions. This presentation, by standardizing the notations, allows to compare the different approaches and to highlight the specificities of each of them: both their objective and their process. The study is not exhaustive and the subject is far from being closed. |
| Keywords: | Agnostic models, Artificial Intelligence, Counterfactual approach, Interpretability, LIME method, Machine learning |
| JEL: | C K |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2020:20&r=all |
| By: | Nobuhiro Hosoe (National Graduate Institute for Policy Studies, Japan) |
| Abstract: | We investigate the impact of US steel and aluminum tariffs, and the resumption of auto tariffs under the revised North American Free Trade Agreement, on trade in North America and foreign direct investment (FDI) from Japan, from the perspective of the auto industry. The results of policy simulation analyses with a recursive dynamic computable general equilibrium model are as follows. Canada and Mexico would benefit from US steel and aluminum tariffs, being alternative trade partners with both the US and other countries. Due to the auto tariffs on intra- North America exports, Canada and Mexico would lose a large part of the windfall benefits from the US steel and aluminum tariffs. Japan’s FDI in Canada and Mexico would fall sharply. The more de-integrated North American economies become, the more Japan would regain its auto production, although at a painful cost in terms of welfare. That negative welfare impact would be neutralized by abolition of auto tariffs with the US. |
| Keywords: | Economic de-integration; foreign direct investment; auto industry; computable general equilibrium analysis |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:ngi:dpaper:20-02&r=all |
| By: | Iryna Veryzhenko (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM]); Arthur Jonath (Auteur indépendant); Etienne Harb (NDU - Notre Dame University-Louaize [Lebanon]) |
| Abstract: | Promotion of both market fairness and efficiency has long been a goal of securities market regulators worldwide. While previous studies mostly focused on market efficiency, our paper proposes tools to improve market fairness. We define market fairness as the ability of a market structure and its regulatory framework to guarantee unimpeded competition while curbing excessive speculation and market manipulation. Such behaviors undermine the quality of financial markets in the sense that they cause volatility and lead to instability. To encourage value generation and improve market quality, we advance a graduated Non-Value-Added Tax. The proposed tax is implemented in a simulation-based model whereby a profitable transaction is taxed at the higher rate if it does not enhance efficiency measured by deviation from fundamentals. When an agent locks in profit not supported by fundamentals but driven by trend-following strategies, the generated profit is taxed at graduate rates under the Non-Value-Added Tax regime. Unlike existing Financial Transaction Taxes, the Non-Value-Added Tax is levied on profit and not on price. More concretely, our findings show that this tool encourages profitable trades that add-value to the market and discourages valueless profit making. It significantly curtails volatility, and prevents the occurrence of extreme market events like bubbles and crashes. |
| Keywords: | high-frequency trading,Agent-based modelling and simulation,bubbles and crashes,market fairness,market regulation,Non-Value-Added tax |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02881064&r=all |
| By: | Belharet, Adel (Korea University); Bharathan, Urmila; Dzingina, Benjamin; Madhavan, Neha; Mathur, Charul; Toti, Yves-Daniel Boga; Babbar, Divij; Markowski, Krzysztof |
| Abstract: | Artificial intelligence and machine learning have found a wide range of business applications, but their impact is only just starting to be seen in project management. This study explores how our existing PM profession will change to be more suitable to AI inputs; and how project management will be forced to change because of the advent of AI, along with concrete, succinct and precise recommendations backed by demonstrable reasoning. |
| Date: | 2020–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:osf:frenxi:8mxfk&r=all |
| By: | Sergio A. Pernice |
| Abstract: | En este documento presentamos una primera revisión de álgebra lineal de una forma especialmente adaptada para sus eventuales aplicaciones en aprendizaje automático (machine learning). Es el primero de una serie de documentos sobre machine learning en español. Es parte del contenido del curso “Métodos de Machine Learning para Economistas” de la Maestría en Economía de la UCEMA. |
| Keywords: | álgebra lineal, regresiones, machine learning, aprendizaje automático. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:cem:doctra:736&r=all |
| By: | Minke Remmerswaal (CPB Netherlands Bureau for Economic Policy Analysis); Jan Boone (CPB Netherlands Bureau for Economic Policy Analysis) |
| Abstract: | Demand-side cost-sharing schemes reduce moral hazard in healthcare at the expense of out-of-pocket risk and equity. With a structural microsimulation model, we show that shifting the starting point of the deductible away from zero to 400 euros for all insured individuals, leads to an average 4 percent reduction in healthcare expenditure and 47 percent lower out-of-pocket payments. We use administrative healthcare expenditure data and focus on the price elastic part of the Dutch population to analyze the differences between the cost-sharing schemes. The model is estimated with a Bayesian mixture model to capture distributions of healthcare expenditure with which we predict the effects of cost-sharing schemes that are not present in our data. DOI: https://doi.org/10.34932/2dcx-9103 |
| JEL: | I11 I13 I14 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:cpb:discus:415.rdf&r=all |
| By: | Colombino, Ugo; Colombino, Ugo; Islam, Nizamul; Islam, Nizamul |
| Abstract: | We use a behavioural microsimulation model embedded in a numerical optimization procedure in order to identify optimal (social welfare maximizing) tax-transfer rules. We consider the class of tax-transfer rules consisting of a universal basic income and a tax defined by a 4th degree polynomial. The rule is applied to total taxable household income. A microeconometric model of household, which simulates household labour supply decisions, is embedded into a numerical routine in order to identify – within the class defined above – the tax-transfer rule that maximizes a social welfare function. We present the results for five European countries: France, Italy, Luxembourg, Spain and United Kingdom. For most values of the inequality aversion parameter, the optimized rules provide a higher social welfare than the current rule, with the exception of Luxembourg. In France, Italy and Luxembourg the optimized rules are significantly different from the current ones and are close to a Negative Income Tax or a Universal basic income with a flat tax rate. In Spain and the UK, the optimized rules are instead close to the current rule. With the exception of Spain, the optimal rules are slightly disequalizing and the social welfare gains are due to efficiency gains. Nonetheless, the poverty gap index tends to be lower under the optimized regime. |
| Date: | 2020–07–22 |
| URL: | http://d.repec.org/n?u=RePEc:ese:emodwp:em13-20&r=all |
| By: | Nurdaulet Abilov (NAC Analytica, Nazarbayev University); Alisher Tolepbergen (NAC Analytica, Nazarbayev University); Klaus Weyerstrass (Institute for Advanced Studies, Macroeconomics and Public Finance Group) |
| Abstract: | The paper builds a structural macroeconometric model for Kazakhstan to generate short-term and medium-term forecasts for main macroeconomic variables and conduct scenario analyses based on dynamic simulation of the model. Due to the poor quality of quarterly data on GDP and its expenditure components, they have been adjusted using volume indexes. The model consists of aggregate supply, aggregate demand, labor market, asset market, the central bank policy and government side equations. Most equations are estimated via econometric techniques and identities are explicitly introduced in line with economic theory. We combine all the regression equations into a single model and solve for the baseline scenario from 2003 to 2017. The simulation results show that the structural macroeconometric model approximates Kazakhstani economy reasonably well. Ex-ante forecasts under oil prices remaining around 50 and 60 US dollars per barrel are generated and compared with the baseline forecast of the National Bank of the Republic of Kazakhstan. |
| Keywords: | Macroeconometric model; Cowles Commission approach; Forecasting; Simulation. |
| JEL: | B32 E17 E27 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:ajx:wpaper:1&r=all |
| By: | Tovar Reaños, Miguel; Lynch, Muireann Á. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:esr:wpaper:wp665&r=all |
| By: | Thomas Blanchet (PSE - Paris School of Economics, WIL - World Inequality Lab); Lucas Chancel (PSE - Paris School of Economics, WIL - World Inequality Lab , IDDRI - Institut du Développement Durable et des Relations Internationales - Institut d'Études Politiques [IEP] - Paris); Amory Gethin (PSE - Paris School of Economics, WIL - World Inequality Lab) |
| Abstract: | This paper estimates the evolution of income inequality in 38 European countries from 1980 to 2017 by combining surveys, tax data and national accounts. We develop a harmonized methodology, using machine learning, nonlinear survey calibration and extreme value theory, in order to produce homogeneous pre-tax and post-tax income inequality estimates, comparable across countries and consistent with official national income growth rates. Inequalities have in- creased in a majority of European countries, both at the top and at the bottom of the distribution, especially between 1980 and 2000. The European top 1% grew more than two times faster than the bottom 50% and captured 17% of regional income growth. Relative poverty in Europe went through ups and downs, increasing from 20% in 1980 to 22% in 2017. Inequalities yet remain lower and have increased much less in Europe than in the US, despite the persistence of strong income differences between European countries and the weaker progressivity of European-wide income redistribution. |
| Keywords: | Simplified Distributional National Accounts,DINA,distribution,Inequality,Europe,pre-tax income,post-tax income,national income |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02877000&r=all |
| By: | Hoekman, Bernard; Shepherd, Ben |
| Abstract: | This paper provides the first quantitative evidence on the restrictiveness of services policies in 2016 for a sample of developing countries, based on recently released regulatory data collected by the World Bank and WTO. We use machine learning to recreate to a high degree of accuracy the OECD's Services Trade Restrictiveness Index (STRI), which takes account of nonlinearities and dependencies across measures. We use the resulting estimates to extend the OECD STRI approach to 23 additional countries, producing what we term a Services Policy Index (SPI). Converting the SPI to ad valorem equivalent terms shows that services policies are typically much more restrictive than tariffs on imports of goods, in particular in professional services and telecommunications. Developing countries tend to have higher services trade restrictions, but less so than has been found in research using data for the late 2000s. We show that the SPI has strong explanatory power for bilateral trade in services at the sectoral level, as well as for aggregate goods and services trade. |
| Keywords: | international trade; Machine Learning; restrictiveness indicators; services policies; Trade in Services |
| JEL: | F13 F15 O24 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14181&r=all |
| By: | John Goodell (University of Akron); Stéphane Goutte (Cemotev - Centre d'études sur la mondialisation, les conflits, les territoires et les vulnérabilités - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines) |
| Abstract: | Literature suggests assets become more correlated during economic downturns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocur-rencies provide a diversification for equities is still an unsettled issue. Additionally , the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures , including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We find co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors differentiating themselves as safe havens. |
| Keywords: | Co-movement,COVID-19,Bitcoin,Wavelet,Safe haven JEL classification: C58 |
| Date: | 2020–06–20 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-02876529&r=all |
| By: | Balazs Zelity (Department of Economics, Wesleyan University) |
| Abstract: | This research explores theoretically, empirically and quantitatively the role of age diversity in determining aggregate productivity and output. Age diversity has two conflicting effects on output. On the one hand, due to skill complementarity across different cohorts, age diversity may be beneficial. On the other hand, rapid skill-biased technological change makes age diversity costly as up-to-date education tends to be concentrated among younger cohorts. To study this trade-off, I first build an overlapping-generations (OLG) model which, in view of these two opposing forces, predicts a hump-shaped relationship between age diversity and GDP per capita. This prediction is established analytically, and also quantitatively using real-world population data in an extended computational version of the model. The prediction is then tested using country-level panel data with a novel instrument, and regional data from Europe. Moving one standard deviation closer to the optimal level of age diversity is associated with a 1.5% increase in GDP per capita. In addition, consistent with the predictions of the model, the optimal level of age diversity is lower in economies where skill-biased technological change is more prevalent. |
| Keywords: | age diversity, education, experience, human capital, demographics, skill-biased |
| JEL: | E24 O40 J24 O15 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:wes:weswpa:2020-004&r=all |
| By: | Gupta, Arpit; van Nieuwerburgh, Stijn |
| Abstract: | We propose a new valuation method for private equity investments. First, we construct a cash-flow replicating portfolio for the private investment, applying Machine Learning techniques on cash-flows on various listed equity and fixed income instruments. The second step values the replicating portfolio using a flexible asset pricing model that accurately prices the systematic risk in bonds of different maturities and a broad cross-section of equity factors. The method delivers a measure of the risk-adjusted profit earned on a PE investment and a time series for the expected return on PE fund categories. We apply the method to buyout, venture capital, real estate, and infrastructure funds, among others. Accounting for horizon-dependent risk and exposure to a broad cross-section of equity factors results in negative average risk-adjusted profits. Substantial cross-sectional variation and persistence in performance suggests some funds outperform. We also find declining expected returns on PE funds in the later part of the sample. |
| Keywords: | affine asset pricing models; Buyout; cross-section of returns; infrastructure; Natural resources; private equity; real estate; temporal pricing of risk; Valuation; venture capital |
| JEL: | G12 G24 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14241&r=all |
| By: | Massimiliano Nuccio (BLISS – Digital Impact Lab, Department of Management, Università Ca' Foscari Venice); Marco Guerzoni (DESPINA Big Data Lab, Department of Economics and Statistics Cognetti De Martiis, University of Torino); Riccardo Cappelli (Department of Economics and Social Sciences, Polytechnic University of Marche); Aldo Geuna (Department of Culture, Politics and Society, University of Torino) |
| Abstract: | Recent literature on the diffusion of robots mostly ignores the regional dimension. The contribution of this paper at the debate on Industry 4.0 is twofold. First, IFR (2017) data on acquisitions of industrial robots in the five largest European economies are rescaled at regional levels to draw a first picture of winners and losers in the European race for advanced manufacturing. Second, using an unsupervised machine learning approach to classify regions based on their composition of industries. The paper provides novel evidence of the relationship between industry mix and the regional capability of adopting robots in the industrial processes. |
| Keywords: | Robots, Industry 4.0., Innovation, Industry Mix, Self-Organizing Maps |
| JEL: | E32 O33 R11 R12 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:vnm:wpdman:173&r=all |
| By: | Marialuz Moreno Badia; Paulo Medas; Pranav Gupta; Yuan Xiang |
| Abstract: | With public debt soaring across the world, a growing concern is whether current debt levels are a harbinger of fiscal crises, thereby restricting the policy space in a downturn. The empirical evidence to date is however inconclusive, and the true cost of debt may be overstated if interest rates remain low. To shed light into this debate, this paper re-examines the importance of public debt as a leading indicator of fiscal crises using machine learning techniques to account for complex interactions previously ignored in the literature. We find that public debt is the most important predictor of crises, showing strong non-linearities. Moreover, beyond certain debt levels, the likelihood of crises increases sharply regardless of the interest-growth differential. Our analysis also reveals that the interactions of public debt with inflation and external imbalances can be as important as debt levels. These results, while not necessarily implying causality, show governments should be wary of high public debt even when borrowing costs seem low. |
| Keywords: | Domestic debt;Financial statistics;Public debt;Negative interest rates;Economic analysis;crisis,debt,default,fiscal,machine learning,WP,fiscal crisis,predictor,income group,debt level,Reinhart |
| Date: | 2020–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2020/001&r=all |
| By: | Xie, Tian (Shanghai University of Finance and Economics); Yu, Jun (School of Economics, Singapore Management University) |
| Abstract: | In this article, we use econometric methods, machine learning methods, and a hybrid method to forecast the GDP growth rate in Singapore based on the Survey of Professional Forecasters (SPF). We compare the performance of these methods with the sample median used by the Monetary Authority of Singapore (MAS). It is shown that the relationship between the actual GDP growth rates and the forecasts from individual professionals is highly nonlinear and non-additive, making it hard for all linear methods and the sample median to perform well. It is found that the hybrid method performs the best, reducing the mean squared forecast error (MSFE) by about 50% relative to that of the sample median. |
| Date: | 2020–07–14 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2020_017&r=all |