nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒07‒18
fifteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep Learning the Efficient Frontier of Convex Vector Optimization Problems By Zachary Feinstein; Birgit Rudloff
  2. Impossibility of Collective Intelligence By Krikamol Muandet
  3. Identifying Politically Connected Firms: A Machine Learning Approach By Deni Mazrekaj; Vitezslav Titl; Fritz Schiltz
  4. Urban economics in a historical perspective: Recovering data with machine learning By Pierre-Philippe Combes; Laurent Gobillon; Yanos Zylberberg
  5. Debiased Machine Learning without Sample-Splitting for Stable Estimators By Qizhao Chen; Vasilis Syrgkanis; Morgane Austern
  6. The North-South divide: sources of divergence, policies for convergence By Lucrezia Fanti; Marcelo C. Pereira; Maria Enrica Virgillito
  7. The Fairness of Machine Learning in Insurance: New Rags for an Old Man? By Laurence Barry; Arthur Charpentier
  8. Debiased Semiparametric U-Statistics: Machine Learning Inference on Inequality of Opportunity By Juan Carlos Escanciano; Jo\"el Robert Terschuur
  9. Human Wellbeing and Machine Learning By Ekaterina Oparina; Caspar Kaiser; Niccol\`o Gentile; Alexandre Tkatchenko; Andrew E. Clark; Jan-Emmanuel De Neve; Conchita D'Ambrosio
  10. Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling By Koen W. de Bock; Arno de Caigny
  11. Building and Using Nonlinear Excel Simulations: An Application to the Specific Factors Model By John Gilbert; Onur A. Koska; Reza Oladi
  12. "Density forecasts of inflation using Gaussian process regression models". By Petar Soric; Enric Monte; Salvador Torra; Oscar Claveria
  13. Artificial Intelligence and Firm-level Productivity By Dirk Czarnitzki; Gastón P Fernández; Christian Rammer
  14. Payday loans -- blessing or growth suppressor? Machine Learning Analysis By Rohith Mahadevan; Sam Richard; Kishore Harshan Kumar; Jeevitha Murugan; Santhosh Kannan; Saaisri; Tarun; Raja CSP Raman
  15. Near-Rational Equilibria in Heterogeneous-Agent Models: A Verification Method By Leonid Kogan; Indrajit Mitra

  1. By: Zachary Feinstein; Birgit Rudloff
    Abstract: In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems satisfying Slater's condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of convex vector optimization problems. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.
    Date: 2022–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2205.07077&r=
  2. By: Krikamol Muandet
    Abstract: Democratization of AI involves training and deploying machine learning models across heterogeneous and potentially massive environments. Diversity of data opens up a number of possibilities to advance AI systems, but also introduces pressing concerns such as privacy, security, and equity that require special attention. This work shows that it is theoretically impossible to design a rational learning algorithm that has the ability to successfully learn across heterogeneous environments, which we decoratively call collective intelligence (CI). By representing learning algorithms as choice correspondences over a hypothesis space, we are able to axiomatize them with essential properties. Unfortunately, the only feasible algorithm compatible with all of the axioms is the standard empirical risk minimization (ERM) which learns arbitrarily from a single environment. Our impossibility result reveals informational incomparability between environments as one of the foremost obstacles for researchers who design novel algorithms that learn from multiple environments, which sheds light on prerequisites for success in critical areas of machine learning such as out-of-distribution generalization, federated learning, algorithmic fairness, and multi-modal learning.
    Date: 2022–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2206.02786&r=
  3. By: Deni Mazrekaj; Vitezslav Titl; Fritz Schiltz
    Abstract: This article introduces machine learning techniques to identify politically connected firms. By assembling information from publicly available sources and the Orbis company database, we constructed a novel firm population dataset from Czechia in which various forms of political connections can be determined. The data about firms’ connections are unique and comprehensive. They include political donations by the firm, having members of managerial boards who donated to a political party, and having members of boards who ran for political office. The results indicate that over 85% of firms with political connections can be accurately identified by the proposed algorithms. The model obtains this high accuracy by using only firm-level financial and industry indicators that are widely available in most countries. We propose that machine learning algorithms should be used by public institutions to identify politically connected firms with potentially large conflicts of interests, and we provide easy to implement R code to replicate our results.
    Keywords: Political Connections, Corruption, Prediction, Machine Learning
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:use:tkiwps:2110&r=
  4. By: Pierre-Philippe Combes (Institut d'Études Politiques [IEP] - Paris, CNRS - Centre National de la Recherche Scientifique); Laurent Gobillon (PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Yanos Zylberberg (University of Bristol [Bristol])
    Abstract: A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications.
    Keywords: Machine learning,History,Urban economics
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:hal:wpspec:halshs-03231786&r=
  5. By: Qizhao Chen; Vasilis Syrgkanis; Morgane Austern
    Abstract: Estimation and inference on causal parameters is typically reduced to a generalized method of moments problem, which involves auxiliary functions that correspond to solutions to a regression or classification problem. Recent line of work on debiased machine learning shows how one can use generic machine learning estimators for these auxiliary problems, while maintaining asymptotic normality and root-$n$ consistency of the target parameter of interest, while only requiring mean-squared-error guarantees from the auxiliary estimation algorithms. The literature typically requires that these auxiliary problems are fitted on a separate sample or in a cross-fitting manner. We show that when these auxiliary estimation algorithms satisfy natural leave-one-out stability properties, then sample splitting is not required. This allows for sample re-use, which can be beneficial in moderately sized sample regimes. For instance, we show that the stability properties that we propose are satisfied for ensemble bagged estimators, built via sub-sampling without replacement, a popular technique in machine learning practice.
    Date: 2022–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2206.01825&r=
  6. By: Lucrezia Fanti (Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italia); Marcelo C. Pereira (Institute of Economics, University of Campinas, Campinas, Brazil); Maria Enrica Virgillito (Institute of Economics, Scuola Superiore Sant’Anna, Pisa, Italia – Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italia)
    Abstract: Building on the labour-augmented K+S framework (Dosi et al., 2010, 2017, 2020), we address the analysis of North-South divide by means of an agent-based model (ABM) endogenously reproducing the divergence between two artificial macro-regions. The latter are characterized by identical initial conditions in terms of productive and innovation structures, but different labour market organizations. We identify the role played by different labour markets functioning on the possible divergence across the two regions, by finding that divergences in labour market reverberate into asymmetric productive performance due to negative reinforcing feedback loop dynamics. We then compare alternative policies by showing that investment schemes aimed at increasing machine renewal and higher substitutionary investment are the most effective in fostering the convergence.
    Keywords: Agent-Based Models; Technology Gap; Labour Market
    JEL: C63 J3 E24 O1
    Date: 2022–05
    URL: http://d.repec.org/n?u=RePEc:ctc:serie5:dipe0027&r=
  7. By: Laurence Barry; Arthur Charpentier
    Abstract: Since the beginning of their history, insurers have been known to use data to classify and price risks. As such, they were confronted early on with the problem of fairness and discrimination associated with data. This issue is becoming increasingly important with access to more granular and behavioural data, and is evolving to reflect current technologies and societal concerns. By looking into earlier debates on discrimination, we show that some algorithmic biases are a renewed version of older ones, while others show a reversal of the previous order. Paradoxically, while the insurance practice has not deeply changed nor are most of these biases new, the machine learning era still deeply shakes the conception of insurance fairness.
    Date: 2022–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2205.08112&r=
  8. By: Juan Carlos Escanciano; Jo\"el Robert Terschuur
    Abstract: We construct locally robust/orthogonal moments in a semiparametric U-statistics setting. These are quadratic moments in the distribution of the data with a zero derivative with respect to first steps at their limit, which reduces model selection bias with machine learning first steps. We use orthogonal moments to propose new debiased estimators and valid inferences in a variety of applications ranging from Inequality of Opportunity (IOp) to distributional treatment effects. U-statistics with machine learning first steps arise naturally in these and many other applications. A leading example in IOp is the Gini coefficient of machine learning fitted values. We introduce a novel U-moment representation of the First Step Influence Function (U-FSIF) to take into account the effect of the first step estimation on an identifying quadratic moment. Adding the U-FISF to the identifying quadratic moment gives rise to an orthogonal quadratic moment. Our leading and motivational application is to measuring IOp, for which we propose a simple debiased estimator, and the first available inferential methods. We give general and simple regularity conditions for asymptotic theory, and demonstrate an improved finite sample performance in simulations for our debiased measures of IOp. In an empirical application, we find that standard measures of IOp are about six times more sensitive to first step machine learners than our debiased measures, and that between $42\%$ and $46\%$ of income inequality in Spain is explained by circumstances out of the control of the individual.
    Date: 2022–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2206.05235&r=
  9. By: Ekaterina Oparina; Caspar Kaiser; Niccol\`o Gentile; Alexandre Tkatchenko; Andrew E. Clark; Jan-Emmanuel De Neve; Conchita D'Ambrosio
    Abstract: There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, standard regression models explain surprisingly little of the variation in wellbeing, limiting our ability to predict it. In response, we here assess the potential of Machine Learning (ML) to help us better understand wellbeing. We analyse wellbeing data on over a million respondents from Germany, the UK, and the United States. In terms of predictive power, our ML approaches do perform better than traditional models. Although the size of the improvement is small in absolute terms, it turns out to be substantial when compared to that of key variables like health. We moreover find that drastically expanding the set of explanatory variables doubles the predictive power of both OLS and the ML approaches on unseen data. The variables identified as important by our ML algorithms - $i.e.$ material conditions, health, and meaningful social relations - are similar to those that have already been identified in the literature. In that sense, our data-driven ML results validate the findings from conventional approaches.
    Date: 2022–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2206.00574&r=
  10. By: Koen W. de Bock (Audencia Business School); Arno de Caigny (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
    Abstract: An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provides the ability to gain insights into why customers are at risk. Hence, customer churn prediction models should complement predictive performance with model insights. Inspired by their ability to reconcile strong predictive performance and interpretability, this study introduces rule ensembles and their extension, spline-rule ensembles, as a promising family of classification algorithms to the customer churn prediction domain. Spline-rule ensembles combine the flexibility of a tree-based ensemble classifier with the simplicity of regression analysis. They do, however, neglect the relatedness between potentially conflicting model components which can introduce unnecessary complexity in the models and compromises model interpretability. To tackle this issue, a novel algorithmic extension, spline-rule ensembles with sparse group lasso regularization (SRE-SGL) is proposed to enhance interpretability through structured regularization. Experiments on fourteen real-world customer churn data sets in different industries (i) demonstrate the superior predictive performance of spline-rule ensembles with sparse group lasso over a set well yet powerful benchmark methods in terms of AUC and top decile lift; (ii) show that spline-rule ensembles with sparse group lasso regularization significantly outperform conventional rule ensembles whilst performing at least as well as conventional spline-rule ensembles; and (iii) illustrate the interpretable nature of a spline-rule ensemble model and the advantage of structured regularization in SRE-SGL by means of a case study on customer churn prediction for a telecommunications company.
    Keywords: Customer churn prediction,Predictive analytics,Spline-rule ensemble,Interpretable data science,Sparse group lasso,Regularized regression
    Date: 2021–11
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03391564&r=
  11. By: John Gilbert; Onur A. Koska (University of Canterbury); Reza Oladi
    Abstract: Excel simulation models have become increasingly common in the economics classroom, as their ability to combine numerical and graphical information has proved a useful support to traditional teaching methods. Recent efforts have tended to embed the solution within the Excel sheet, avoiding the need to use the Solver add-in and allowing changes in the exogenous characteristics of the model to be instantly reflected in the numerical solutions and any associated geometry. While this is quite simple in small-scale linear models, it is less straightforward in larger non-linear models such as those that dominate the theory of international trade. We discuss various methods that can be used in building Excel simulations when it is not possible to solve the underlying model explicitly. We illustrate the ideas and describe our experiences along with a new simulation of the specific factors model.
    Keywords: Numerical simulation, Excel, specific factors model
    JEL: A22 C63 F01
    Date: 2022–03–01
    URL: http://d.repec.org/n?u=RePEc:cbt:econwp:22/08&r=
  12. By: Petar Soric (Faculty of Economics & Business University of Zagreb.); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).); Salvador Torra (Riskcenter–IREA, University of Barcelona (UB).); Oscar Claveria (AQR–IREA, University of Barcelona (UB).)
    Abstract: The present study uses Gaussian Process regression models for generating density forecasts of inflation within the New Keynesian Phillips curve (NKPC) framework. The NKPC is a structural model of inflation dynamics in which we include the output gap, inflation expectations, fuel world prices and money market interest rates as predictors. We estimate country-specific time series models for the 19 Euro Area (EA) countries. As opposed to other machine learning models, Gaussian Process regression allows estimating confidence intervals for the predictions. The performance of the proposed model is assessed in a one-step-ahead forecasting exercise. The results obtained point out the recent inflationary pressures and show the potential of Gaussian Process regression for forecasting purposes.
    Keywords: Machine learning, Gaussian process regression, Time-series analysis, Economic forecasting, Inflation, New Keynesian Phillips curve. JEL classification: C45, C51, C53, E31.
    Date: 2022–07
    URL: http://d.repec.org/n?u=RePEc:ira:wpaper:202210&r=
  13. By: Dirk Czarnitzki; Gastón P Fernández; Christian Rammer
    Abstract: Artificial Intelligence (AI) is often regarded as the next general-purpose technology with a rapid, penetrating, and far-reaching use over a broad number of industrial sectors. A main feature of new general-purpose technology is to enable new ways of production that may increase productivity. So far, however, only very few studies investigated likely productivity effects of AI at the firm-level; presumably because of lacking data. We exploit unique survey data on firms’ adoption of AI technology and estimate its productivity effects with a sample of German firms. We employ both a cross-sectional dataset and a panel database. To address the potential endogeneity of AI adoption, we also implement IV estimators. We find positive and significant effects of the use of AI on firm productivity. This finding holds for different measures of AI usage, i.e., an indicator variable of AI adoption, and the intensity with which firms use AI methods in their business processes.
    Keywords: Artificial Intelligence, Productivity, CIS data
    Date: 2022–02–17
    URL: http://d.repec.org/n?u=RePEc:ete:msiper:690486&r=
  14. By: Rohith Mahadevan; Sam Richard; Kishore Harshan Kumar; Jeevitha Murugan; Santhosh Kannan; Saaisri; Tarun; Raja CSP Raman
    Abstract: The upsurge of real estate involves a variety of factors that have got influenced by many domains. Indeed, the unrecognized sector that would affect the economy for which regulatory proposals are being drafted to keep this in control is the payday loans. This research paper revolves around the impact of payday loans in the real estate market. The research paper draws a first-hand experience of obtaining the index for the concentration of real estate in an area of reference by virtue of payday loans in Toronto, Ontario in particular, which sets out an ideology to create, evaluate and demonstrate the scenario through research analysis. The purpose of this indexing via payday loans is the basic - debt: income ratio which states that when the income of the person bound to pay the interest of payday loans increases, his debt goes down marginally which hence infers that the person invests in fixed assets like real estate which hikes up its growth.
    Date: 2022–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2205.15320&r=
  15. By: Leonid Kogan; Indrajit Mitra
    Abstract: We propose a general simulation-based procedure for estimating quality of approximate policies in heterogeneous-agent equilibrium models, which allows to verify that such approximate solutions describe a near-rational equilibrium. Our procedure endows agents with superior knowledge of the future path of the economy, while imposing a suitable penalty for such foresight. The relaxed problem is more tractable than the original, and results in an upper bound on agents’ welfare. Our method is general, straightforward to implement, and can be used in conjunction with various solution algorithms. We illustrate our approach in two applications: the incomplete-markets model of Krusell and Smith (1998) and the heterogeneous firm model of Khan and Thomas (2008).
    JEL: C02 C18 C63 C68 E00 E37 G1
    Date: 2022–06
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:30111&r=

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