nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒03‒21
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. A Framework for using Machine Learning to Support Qualitative Data Coding By Baumgartner, Peter; Smith, Amanda; Olmsted, Murrey; Ohse, Dawn
  2. Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings? By Matthew Harding; Gabriel F. R. Vasconcelos
  3. Machine Learning for Credit Scoring: Improving Logistic Regression with Non-Linear Decision-Tree Effects By Elena Ivona Dumitrescu; Sullivan Hué; Christophe Hurlin; Sessi Tokpavi
  4. FisrEbp: Enterprise Bankruptcy Prediction via Fusing its Intra-risk and Spillover-Risk By Yu Zhao; Shaopeng Wei; Yu Guo; Qing Yang; Gang Kou
  5. Application of Quantum Computers in Foreign Exchange Reserves Management By Martin Vesely
  6. A Neural Phillips Curve and a Deep Output Gap By Philippe Goulet Coulombe
  7. Could Machine Learning be a General Purpose Technology? A Comparison of Emerging Technologies Using Data from Online Job Postings By Avi Goldfarb; Bledi Taska; Florenta Teodoridis
  8. Desarrollo de una herramienta de aprendizaje automático (machine learning) para establecer relaciones entre ocupaciones y programas de capacitación en el Uruguay By Velardez, Miguel Omar; Dima, Germán César
  9. Artificial Intelligence (AI) .. Marketing touchpoints By Khalida Abi; Salah Zakraoui; Ahmed Benahmed
  10. Host type and pricing on Airbnb: Seasonality and perceived market power By Georges Casamatta; Sauveur Giannoni; Daniel Brunstein; Johan Jouve
  11. The impact of Lung-Strengthening Qigong on wellbeing: a case study By kurt, zeyneb; Sice, Petia; Krajewska, Krystyna; Elvin, Garry; Xie, Hailun; Ogwu, Suzannah; Wang, Pingfan; Turgut, Sevgi
  12. Orchestrating coordination among humanitarian organizations By Ruesch, Lea; Tarakci, Murat; Besiou, Maria; Van Quaquebeke, Niels
  13. Simulating media platform mergers By Marc Ivaldi; Jiekai Zhang
  14. Post-COVID fiscal rules: a central bank perspective By Hauptmeier, Sebastian; Leiner-Killinger, Nadine; Muggenthaler, Philip; Haroutunian, Stephan

  1. By: Baumgartner, Peter (RTI International); Smith, Amanda; Olmsted, Murrey; Ohse, Dawn
    Abstract: Open-ended survey questions provide qualitative data that are useful for a multitude of reasons. However, qualitative data analysis is labor intensive, and researchers often lack the needed time and resources resulting in underutilization of qualitative data. In attempting to address these issues, we looked to machine learning and recent advances in language models and transfer learning to assist in qualitative coding of responses. We trained a machine learning model following the BERT architecture to predict thematic codes that were then adjudicated by human coders. Results suggest this is a promising approach that can be used to support traditional coding methods and has the potential to alleviate some of the burden associated with qualitative data analysis.
    Date: 2021–11–16
    URL: http://d.repec.org/n?u=RePEc:osf:osfxxx:fueyj&r=
  2. By: Matthew Harding; Gabriel F. R. Vasconcelos
    Abstract: We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
    Date: 2022–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2202.04218&r=
  3. By: Elena Ivona Dumitrescu (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); Sullivan Hué (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Christophe Hurlin (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours); Sessi Tokpavi (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)
    Abstract: In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we propose a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with original predictive variables are used as predictors in a penalised logistic regression model. PLTR allows us to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model. Monte Carlo simulations and empirical applications using four real credit default datasets show that PLTR predicts credit risk significantly more accurately than logistic regression and compares competitively to the random forest method.
    Keywords: Risk management,Credit scoring,Machine learning,Interpretability,Econometrics
    Date: 2022–03–16
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03331114&r=
  4. By: Yu Zhao; Shaopeng Wei; Yu Guo; Qing Yang; Gang Kou
    Abstract: In this paper, we propose to model enterprise bankruptcy risk by fusing its intra-risk and spillover-risk. Under this framework, we propose a novel method that is equipped with an LSTM-based intra-risk encoder and GNNs-based spillover-risk encoder. Specifically, the intra-risk encoder is able to capture enterprise intra-risk using the statistic correlated indicators from the basic business information and litigation information. The spillover-risk encoder consists of hypergraph neural networks and heterogeneous graph neural networks, which aim to model spillover risk through two aspects, i.e. hyperedge and multiplex heterogeneous relations among enterprise knowledge graph, respectively. To evaluate the proposed model, we collect multi-sources SMEs data and build a new dataset SMEsD, on which the experimental results demonstrate the superiority of the proposed method. The dataset is expected to become a significant benchmark dataset for SMEs bankruptcy prediction and promote the development of financial risk study further.
    Date: 2022–01
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2202.03874&r=
  5. By: Martin Vesely
    Abstract: The main purpose of this article is to evaluate possible applications of quantum computers in foreign exchange reserves management. The capabilities of quantum computers are demonstrated by means of risk measurement using the quantum Monte Carlo method and portfolio optimization using a linear equations system solver (the Harrow-Hassidim-Lloyd algorithm) and quadratic unconstrained binary optimization (the quantum approximate optimization algorithm). All demonstrations are carried out on the cloud-based IBM QuantumTM platform. Despite the fact that real-world applications are impossible under the current state of development of quantum computers, it is proven that in principle it will be possible to apply such computers in FX reserves management in the future. In addition, the article serves as an introduction to quantum computing for the staff of central banks and financial market supervisory authorities.
    Keywords: Foreign exchange reserves, HHL algorithm, portfolio optimization, QAOA algorithm, quantum computing, risk measurement
    JEL: C61 C63 G11
    Date: 2022–03
    URL: http://d.repec.org/n?u=RePEc:cnb:wpaper:2022/2&r=
  6. By: Philippe Goulet Coulombe
    Abstract: Many problems plague the estimation of Phillips curves. Among them is the hurdle that the two key components, inflation expectations and the output gap, are both unobserved. Traditional remedies include creating reasonable proxies for the notable absentees or extracting them via some form of assumptions-heavy filtering procedure. I propose an alternative route: a Hemisphere Neural Network (HNN) whose peculiar architecture yields a final layer where components can be interpreted as latent states within a Neural Phillips Curve. There are benefits. First, HNN conducts the supervised estimation of nonlinearities that arise when translating a high-dimensional set of observed regressors into latent states. Second, computations are fast. Third, forecasts are economically interpretable. Fourth, inflation volatility can also be predicted by merely adding a hemisphere to the model. Among other findings, the contribution of real activity to inflation appears severely underestimated in traditional econometric specifications. Also, HNN captures out-of-sample the 2021 upswing in inflation and attributes it first to an abrupt and sizable disanchoring of the expectations component, followed by a wildly positive gap starting from late 2020. HNN's gap unique path comes from dispensing with unemployment and GDP in favor of an amalgam of nonlinearly processed alternative tightness indicators -- some of which are skyrocketing as of early 2022.
    Date: 2022–02
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2202.04146&r=
  7. By: Avi Goldfarb; Bledi Taska; Florenta Teodoridis
    Abstract: General purpose technologies (GPTs) push out the production possibility frontier and are of strategic importance to managers and policymakers. While theoretical models that explain the characteristics, benefits, and approaches to create and capture value from GPTs have advanced significantly, empirical methods to identify GPTs are lagging. The handful of available attempts are typically context specific and rely on hindsight. For managers deciding on technology strategy, it means that the classification, when available, comes too late. We propose a more universal approach of assessing the GPT likelihood of emerging technologies using data from online job postings. We benchmark our approach against prevailing empirical GPT methods that exploit patent data and provide an application on a set of emerging technologies. Our application exercise suggests that a cluster of technologies comprised of machine learning and related data science technologies is relatively likely to be GPT.
    JEL: O32 O33
    Date: 2022–02
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:29767&r=
  8. By: Velardez, Miguel Omar; Dima, Germán César
    Abstract: En este trabajo se desarrolló una herramienta automática y no supervisada que tiene por objeto recomendar programas de capacitación para una serie de ocupaciones sobre la base de similitudes entre el perfil de egreso de un conjunto de programas de la Universidad del Trabajo del Uruguay (UTU) y la descripción de las tareas correspondientes a 22 ocupaciones obtenidas a partir del relevamiento de ONET de Uruguay. En la herramienta se utilizan instrumentos del procesamiento de lenguaje natural (Natural Language Processing o NLP) cuya atención se centra en la repetibilidad de conceptos claves y en las similitudes del texto como un todo. Con el fin de evaluar este método, se contrastaron las recomendaciones obtenidas a partir de la herramienta con las que brindó un grupo de personas expertas. Los resultados muestran que la herramienta desarrollada permite recomendar un promedio de hasta nueve programas de capacitación para cada ocupación con un porcentaje de éxito medio del 85%. El potencial de esta metodología radica en que permite manejar de forma eficiente grandes volúmenes de datos que pueden contribuir a brindar información no sesgada en los servicios de desarrollo de carrera.
    Date: 2022–02–01
    URL: http://d.repec.org/n?u=RePEc:ecr:col022:47724&r=
  9. By: Khalida Abi (Université d'El-Oued); Salah Zakraoui (Université d'El-Oued); Ahmed Benahmed (Université d'El-Oued)
    Abstract: Data analysts behind the "Marketing" desk keep trying to find links between identified data, but they can't easily find patterns in complex and huge amount of data, in order to generate insights. "There is where Artificial Intelligence brightly shines and supports many more functions intelligently in marketing department ... in real time..." The main objective for these papers is to put the magnifying-glass above the underlined part in the Previous §, To examine scene for marketing academics, managers and activists. Within trying to develop a marketing understanding of (AI) and its current potentials, in the way where we highlight its latest use-cases in the marketing context.
    Keywords: Online Advertising campaign,Advertising,AI Marketing,Marketing,Artificial intelligence (AI)
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03505864&r=
  10. By: Georges Casamatta (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique, TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Sauveur Giannoni (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Daniel Brunstein (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Johan Jouve (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique)
    Abstract: The literature on short-term rental emphasises the heterogeneity of the hosts population. Some argue that professional and opportunistic hosts differ in terms of their pricing strategy. This study highlights how differences in market perception and information create a price differential between professional and non-professional players. Proposing an original and accurate definition of professional hosts, we rely on a large dataset of almost 9,000 properties and 73,000 observations to investigate the pricing behaviour of Airbnb sellers in Corsica (France). Using OLS and the double-machine learning methods, we demonstrate that a price differential exists between professional and opportunistic sellers. In addition, we assess the impact of seasonality in demand on the size and direction of this price differential. We find that professionals perceive a higher degree of market power than others during the peak season and it allows them to enhance their revenues.
    Keywords: Short-term rental,Pricing,Professionalism,Double machine learning,Seasonality,Market-power
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03250484&r=
  11. By: kurt, zeyneb; Sice, Petia; Krajewska, Krystyna; Elvin, Garry; Xie, Hailun; Ogwu, Suzannah; Wang, Pingfan; Turgut, Sevgi
    Abstract: Qigong is an umbrella term for a group of traditional exercises originated from China. Lung- Strengthening Qigong (LSQ) is one of these techniques enabling practitioners to maintain and improve their physical and mental wellbeing. We recruited 170 practitioners and 42 non- practitioner/control samples to investigate the impacts of LSQ practice on body, mind, thoughts, and feelings. We requested completion of a questionnaire regularly from both of the practitioner and control group, fill in an online diary and end of study survey (EOS) only from the practitioners. Statistical analysis was conducted on the questionnaires, whereas qualitative thematic- and quantitative machine learning-based analyses were applied to the free-text diary entries. We evaluated all different data resources together and observed that (a) there was a significant improvement in physical and mental wellbeing (increase in sleep quality, feeling life, coping with life, feeling life energy and a decline in stress amount) of the practitioners, which were not observed in the control group, (b) four different groups (non, low, moderate, high-level) of benefits were emanated among the practitioners, (c) numerical evaluation of questionnaires and EOS, as well as the qualitative and quantitative analyses of the diary entries were all found to be consistent, and (d) majority of the participants (84%) reported a striking improvement in their well-being, (e) majority of the positively impacted practitioners had no or some little prior experience with LSQ. This study is novel in various aspects including (i) increasing the sample size radically compared to other conventional studies as well as considering a control group for comparisons, (ii) providing regular live LSQ sessions to the practitioners, (iii) incorporating both qualitative and quantitative type of analyses to understand the impacts of Qigong.
    Date: 2021–11–29
    URL: http://d.repec.org/n?u=RePEc:osf:osfxxx:yvt4q&r=
  12. By: Ruesch, Lea; Tarakci, Murat; Besiou, Maria; Van Quaquebeke, Niels
    Abstract: Disasters mobilize hundreds of organizations, but coordination among them remains a challenge. This is why the United Nations has formed clusters to facilitate information and resource exchange among humanitarian organizations. Yet, coordination failures in prior disasters raise questions as to the effectiveness of the cluster approach in coordinating relief efforts. To better understand barriers to coordination, we developed a grounded theory and augmented the theory with an agent-based simulation. Our theory discerns a cluster lead's roles of facilitating coordination, but also investing in its own ground operations. We find that specifically serving such a dual role impairs swift trust and consequent coordination among cluster members. The additional simulation findings generalize the detrimental effect of the cluster lead's dual role versus a pure facilitator role and specify it against various boundary conditions.
    Keywords: agent-based simulations; coordination; humanitarian operations; interorganizational relationships; leadership; localization; resource disparity; swift trust
    JEL: J50
    Date: 2022–01–03
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:113725&r=
  13. By: Marc Ivaldi (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Jiekai Zhang (Hanken School of Economics)
    Abstract: The empirical analysis of media platforms economics has often neglected the multi-homing behaviour of advertisers. Assuming away the cross-substitutability and/or complementarity between the advertising slots of different platforms could damage the quality and the robustness of counterfactual analysis. To evaluate the consequence of such an abstraction, we compare the simulation results of hypothetical platform mergers when the demand on the advertising side is derived from a Translog cost model which allows for multi-homing, and when it is approximated by using a simple log-linear inverse demand model that ignores the differentiation among media platforms' advertising slots. Ignoring the existence of substitutes or complements on the advertising side would result in overpredicting the losses of the viewers' surplus and in underpredicting the gains in platforms' revenues
    Keywords: Two-sided market,Platform merger,Advertising,TV market,Competition policy
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03472984&r=
  14. By: Hauptmeier, Sebastian; Leiner-Killinger, Nadine; Muggenthaler, Philip; Haroutunian, Stephan
    Abstract: Regarding a prospective reform of the European Stability and Growth Pact (SGP) it seems rather consensual that a simplified framework should take account of the prevailing macroeconomic context and enhance the balancing of sustainability and stabilisation considerations. This paper provides simulation analysis for the euro area and individual countries with a view to assessing the short- and longer-term budgetary and macroeconomic implications of a move to a two-tier system with an expenditure growth rule as single operational indicator linked to a debt anchor. Compared to the status quo, our analysis suggests that expenditure growth targets which take account of the ECB’s symmetric 2% inflation target can improve the cyclical properties of the framework. Fiscal policy would be tighter when inflation is above the target but looser when inflation is below target, resulting in a better synchronisation of fiscal and monetary policies. Providing additional fiscal accommodation in a low inflation environment would enable monetary policy to operate more effectively especially in the vicinity of the effective lower bound. The link to a longer-term debt anchor at the same time ensures a transition towards the Treaty’s debt reference level. JEL Classification: E63, H50, H60
    Keywords: debt sustainability, European fiscal rules, monetary and fiscal policy interactions
    Date: 2022–03
    URL: http://d.repec.org/n?u=RePEc:ecb:ecbwps:20222656&r=

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