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on Computational Economics |
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Issue of 2019‒05‒20
sixteen papers chosen by |
| By: | Dominique Guegan (UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Panthéon-Sorbonne, IPAG Business School, University of Ca’ Foscari [Venice, Italy]); Peter Addo (AFD - Agence française de développement, Labex ReFi - UP1 - Université Panthéon-Sorbonne); Bertrand Hassani (Labex ReFi - UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Capgemini Consulting [Paris], UCL-CS - Computer science department [University College London] - UCL - University College of London [London]) |
| Abstract: | Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises. |
| Keywords: | financial regulation,deep learning,Big data,data science,credit risk |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-01835164&r=all |
| By: | Bernardo J. Zubillaga; Andr\'e L. M. Vilela; Chao Wang; Kenric P. Nelson; H. Eugene Stanley |
| Abstract: | We propose a three-state microscopic opinion formation model for the purpose of simulating the dynamics of financial markets. In order to mimic the heterogeneous composition of the mass of investors in a market, the agent-based model considers two different types of traders: noise traders and contrarians. Agents are represented as nodes in a network of interactions and they can assume any of three distinct possible states (e.g. buy, sell or remain inactive). The time evolution of the state of an agent is dictated by probabilistic dynamics that include both local and global influences. A noise trader is subject to local interactions, tending to assume the majority state of its nearest neighbors, whilst a contrarian is subject to a global interaction with the behavior of the market as a whole, tending to assume the state of the global minority of the market. The model exhibits the typical qualitative and quantitative features of real financial time series, including distributions of returns with heavy tails, volatility clustering and long-time memory for the absolute values of the returns. The distributions of returns are fitted by means of coupled Gaussian distributions, quantitatively revealing transitions between leptokurtic, mesokurtic and platykurtic regimes in terms of a non-linear statistical coupling which describes the complexity of the system. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.04370&r=all |
| By: | Menoncin, Francesco; Vergalli, Sergio |
| Abstract: | In this work we solve in a closed form the problem of an agent who wants to optimise the inter-temporal utility of both his consumption and leisure by choosing: (i) the optimal inter-temporal consumption, (ii) the optimal inter-temporal labour supply, (iii) the optimal share of wealth to invest in a risky asset, and (iv) the optimal retirement age. The wage of the agent is assumed to be stochastic and correlated with the risky asset on the financial market. The problem is split into two sub-problems: the optimal consumption, labour, and portfolio problem is solved first, and then the optimal stopping time is approached. The martingale method is used for the first problem, and it allows to solve it for any value of the stopping time which is just considered as a stochastic variable. The problem of the agent is solved by assuming that after retirement he received a utility that is proportional to the remaining human capital. Finally, a numerical simulation is presented for showing the behaviour over time of the optimal solution. |
| Keywords: | Research Methods/ Statistical Methods |
| Date: | 2019–05–15 |
| URL: | http://d.repec.org/n?u=RePEc:ags:feemth:288459&r=all |
| By: | Pierre Henry-Labordère (Societe Generale - Société Générale) |
| Abstract: | In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. As some additional applications, we consider anomaly detection and automatic generation of financial data. |
| Date: | 2019–04–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02095222&r=all |
| By: | Bekkers, Eddy; Koopman, Robert; Lemos Rego, Carolina |
| Abstract: | This paper examines the impact of structural change in China, in particular a reduction in the savings rate, an increase in the share of skilled workers, and an increase in productivity in technologically advanced manufacturing sectors targeted by Made in China 2025. Baseline projections until 2040 are generated with the WTO Global Trade Model, a dynamic computable general equilibrium model. With the modelled structural changes the Chinese economy is projected to reorient its focus increasingly onto the domestic economy, raising the share of private household and government consumption in GDP, turning China's trade surplus into a trade deficit, reducing China's share in global exports, raising the share of services in both production and exports, shifting the destination markets of Chinese exports from developed to developing countries, and changing its pattern of comparative advantage away from sectors like light and heavy manufacturing to electronic and machinery equipment. The large bilateral trade surplus vis-a-vis the United States is projected to fall to almost zero. |
| Keywords: | China; Dynamic CGE-Modelling; structural change |
| JEL: | F14 F43 I25 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:13721&r=all |
| By: | Chantal Kegels; Dirk Verwerft |
| Abstract: | This working paper analyses the economic impact of a regulated professional services reform in Belgium through simulations based on the European Commission's DSGE model QUEST III R&D |
| JEL: | D24 E17 K23 L11 |
| Date: | 2018–06–30 |
| URL: | http://d.repec.org/n?u=RePEc:fpb:wpaper:1809&r=all |
| By: | Andre Lorentz; Tommaso Ciarli; Maria Savona; Marco Valente |
| Abstract: | We build upon the evolutionary model developed in prior works (Ciarli, Lorentz, Savona and Valente 2010b), which formalises the links between production, organisation and functional composition of the employment on the supply side and the endogenous evolution of consumption patterns on the demand side. The main contribution resulting from the exercise proposed here is to derive the Kaldorian cumulative causation mechanism as an emergent property of the dynamics generated by the micro-founded model. More precisely, we discuss the main transition dynamics to a self-sustained growth regime in a two-stage growth patterns generated through the numerical simulation of the model. We then show that these mechanisms lead to the emergence of a Kaldor-Verdoorn law. Finally we show that the structure of demand (among others the heterogeneity in consumption behaviour) itself shapes the type of growth regime emerging from the endogenous structural changes, fostering or hampering the emergence of the Kaldor Verdoorn law. |
| Keywords: | Structural change; growth; consumption; technological change; cumulative causation; evolutionary economics; Kaldor-Verdoorn Law. |
| JEL: | O41 L16 C63 E11 O14 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ulp:sbbeta:2019-15&r=all |
| By: | Gijs Dekkers; Ekaterina Tarantchenko; Karel Van den Bosch |
| Abstract: | The Federal Planning Bureau has developed within the Nowcasting project a dynamic microsimulation model for nowcasting and medium-term forecasts (currently up to 2020) of indicators of poverty and social exclusion. Key messages of this project are that nowcasting and medium-term forecasting are now possible using a fully dynamic microsimulation model. The provisional results of the model suggest that the overall poverty risk would remain stable, but that of the 65+ subpopulation would decrease over time, while that of the younger population would show a small increase. Furthermore, the increase of overall ine-quality would come to a halt and the level of inequality would become more stable. Finally, the very low work intensity rate would continue its decrease, driven by the continuing increase of the employment rate among the working-age population. |
| JEL: | C53 H31 I32 |
| Date: | 2019–02–28 |
| URL: | http://d.repec.org/n?u=RePEc:fpb:wpaper:1903&r=all |
| By: | Fix, Blair |
| Abstract: | What makes the rich different? Are they more productive, as mainstream economists claim? I offer another explanation. What makes the rich different, I propose, is hierarchical power. The rich command hierarchies. The poor do not. It is this greater control over subordinates, I hypothesize, that explains the income and class of the very rich. I test this idea using evidence from US CEOs. I find that the relative income of CEOs increases with their hierarchical power, as does the capitalist portion of their income. This suggests that among CEOs, both income size and income class relate to hierarchical power. I then use a numerical model to test if the CEO evidence extends to the US general public. The model suggest that this is plausible. Using this model, I infer the relation between income size, income class, and hierarchical power among the US public. The results suggests that behind the income and class of the very rich lies immense hierarchical power. |
| Keywords: | hierarchy,power,functional income distribution,personal income distribution,inequality,capital as power,class |
| JEL: | D31 D33 B5 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:capwps:201902&r=all |
| By: | Hauber, Philipp; Schumacher, Christian; Zhang, Jiachun |
| Abstract: | We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this paper, we derive the corresponding Kalman smoother moments and propose an efficient simulation smoother, which relies on mean corrections for unconditional vectors. When applied to a factor model, the proposed simulation smoother for the states is efficient compared to other state-space models without lagged states and/or lagged dependent variables in terms of computing time. |
| Keywords: | state-space model,missing observations,Kalman filter and smoother,simulation smoothing,factor model |
| JEL: | C11 C32 C38 C63 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:152019&r=all |
| By: | Fernando Fernandes Neto; Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas |
| Abstract: | The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by nonlinear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We outperformed the best available algorithms in 4 out of 18 important data classification problems, and obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with invariances and symmetries, with desirable features, such as possibility to evaluate parameter stability and easy structural assessment. |
| Keywords: | Haar Scattering Network; Pattern Recognition; Classification; Regression; Time Series. |
| JEL: | C38 C45 C52 C63 |
| Date: | 2019–05–07 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2019wpecon16&r=all |
| By: | Loughrey, Jason; Hennessy, Thia |
| Abstract: | Agricultural land rental markets contribute towards structural change in the farming sector by offering farmers the opportunity to adjust their farm size without committing to a transfer of land ownership. In Irish agriculture, the share of agricultural land being rented is however, among the lowest in Europe. Many Irish farmers continue to produce output and remain in agricultural employment despite persistently negative market returns. This implies that land-use decisions are not solely influenced by market returns. In this paper, we utilize Teagasc National Farm survey data to analyse the agricultural land rental market in Ireland with a newly developed microsimulation model. This model is compared to an equilibrium model of the land rental market. The microsimulation model has a number of advantages over the equilibrium model in addressing path dependency, the interaction between landowners and tenants and the farm size concentration. The model requires some further refinement in simulating the variability of land rental prices between contracts. |
| Keywords: | Land Economics/Use |
| Date: | 2019–05–09 |
| URL: | http://d.repec.org/n?u=RePEc:ags:eaa165:288439&r=all |
| By: | Joseph Staudt; Yifang Wei; Lisa Singh; Shawn D. Klimek; J. Bradford Jensen; Andrew L. Baer |
| Abstract: | Between the 2007 and 2012 Economic Censuses (EC), the count of franchise-affiliated establishments declined by 9.8%. One reason for this decline was a reduction in resources that the Census Bureau was able to dedicate to the manual evaluation of survey responses in the franchise section of the EC. Extensive manual evaluation in 2007 resulted in many establishments, whose survey forms indicated they were not franchise-affiliated, being recoded as franchise-affiliated. No such evaluation could be undertaken in 2012. In this paper, we examine the potential of using external data harvested from the web in combination with machine learning methods to automate the process of evaluating responses to the franchise section of the 2017 EC. Our method allows us to quickly and accurately identify and recode establishments have been mistakenly classified as not being franchise-affiliated, increasing the unweighted number of franchise-affiliated establishments in the 2017 EC by 22%-42%. |
| JEL: | C81 L8 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25818&r=all |
| By: | Takashi Yamashita; Ryozo Miura |
| Abstract: | This paper introduces a new correction scheme to a conventional regression-based event study method: a topological machine-learning approach with a self-organizing map (SOM).We use this new scheme to analyze a major market event in Japan and find that the factors of abnormal stock returns can be easily can be easily identified and the event-cluster can be depicted.We also find that a conventional event study method involves an empirical analysis mechanism that tends to derive bias due to its mechanism, typically in an event-clustered market situation. We explain our new correction scheme and apply it to an event in the Japanese market --- the holding disclosure of the Government Pension Investment Fund (GPIF) on July 31, 2015. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.06536&r=all |
| By: | Jian Gao; Yi-Cheng Zhang; Tao Zhou |
| Abstract: | Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1905.06166&r=all |
| By: | Jørgen-Vitting Andersen (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Philippe de Peretti (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We introduce a new methodology that enables the detection of onset of convergence towards Nash equilibria, in simple repeated-games with infinite large strategy spaces. The method works by constraining on a special and finite subset of strategies. We illustrate how the method can predict (in special time periods) with a high success rate the action of participants in a series of experiments. |
| Keywords: | multi-period games,infinite strategy space,decoupling,bounded rationality,agent-based modeling |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-01960900&r=all |