nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒05‒23
seventeen papers chosen by

  1. Interpretable Prediction of Urban Mobility Flows with Deep Neural Networks as Gaussian Processes By Aike Steentoft, Bu-Sung Lee, Markus Schläpfer
  2. How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign By Henrika Langen; Martin Huber
  3. Adversarial Estimators By Jonas Metzger
  4. The North-South divide: sources of divergence, policies for convergence By Lucrezia Fanti; Marcelo C. Pereira; Maria Enrica Virgillito
  5. Missing top incomes and tax-benefit microsimulation: evidence from correcting household survey data using tax records data By Marko Ledic; Ivica Rubil; Ivica Urban
  6. An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest By Yizhe Xu; Tom H. Greene; Adam P. Bress; Brandon K. Bellows; Yue Zhang; Zugui Zhang; Paul Kolm; William S. Weintraub; Andrew S. Moran; Jincheng Shen
  7. Agent-based model generating stylized facts of fixed income markets By Antoine Kopp; Rebecca Westphal; Didier Sornette
  8. Control and spread of contagion in networks with global effects By John Higgins; Tarun Sabarwal
  9. Economic impacts of natural hazards and complexity science: a critical review By Matteo Coronese; Davide Luzzati
  10. Advisory algorithms and liability rules By Marie Obidzinski; Yves Oytana
  11. The economic effects of stopping Russian energy Import in Poland By Jakub Sokolowski; Marek Antosiewicz; Piotr Lewandowski
  12. EUROMOD baseline report By Sofia Maier; Mattia Ricci; Vanda Almeida; Michael Christl; Hugo Cruces; Silvia De Poli; Klaus Grunberger; Adrian Hernandez; Tine Hufkens; Daniela Hupteva; Viginta Ivaskaite-Tamosiune; Marta Jedrych; Alberto Mazzon; Bianey Palma; Andrea Papini; Fidel Picos; Alberto Tumino; Estefanía Vazquez
  13. Social Interactions, Resilience, and Access to Economic Opportunity: A Research Agenda for the Field of Computational Social Science By Theresa Kuchler; Johannes Stroebel
  14. Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions By Almatrudi, Sulaiman; Parvate, Kanaad; Rothchild, Daniel; Vijay, Upadhi
  15. Search Algorithm and Sales on Online Platforms: Evidence from Food Delivery Platforms By Yangguang Huang
  16. More Than Words: Fed Chairs’ Communication During Congressional Testimonies By Michelle Alexopoulos; Xinfen Han; Oleksiy Kryvtsov; Xu Zhang
  17. Reformulation of income transfers in Brazil: simulations and challenges By Luís Henrique Paiva; Leticia Bartholo; Pedro H. G. Ferreira de Souza; Rodrigo Octávio Orair

  1. By: Aike Steentoft, Bu-Sung Lee, Markus Schläpfer
    Abstract: The ability to understand and predict the flows of people in cities is crucial for the planning of transportation systems and other urban infrastructures. Deep-learning approaches are powerful since they can capture non-linear relations between geographic features and the resulting mobility flow from a given origin location to a destination location. However, existing methods cannot quantify the uncertainty of the predictions, limiting their interpretability and thus their use for practical applications in urban infrastructure planning. To that end, we propose a Bayesian deep-learning approach that formulates deep neural networks as Gaussian processes and integrates automatic variable selection. Our method provides uncertainty estimates for the predicted origin-destination flows while also allowing to identify the most critical geographic features that drive the mobility patterns. The developed machine learning approach is applied to large-scale taxi trip data from New York City.
    Keywords: mobility, Bayesian deep learning, smart cities, transportation system planning
    JEL: C45 R41
    Date: 2022–05
  2. By: Henrika Langen; Martin Huber
    Abstract: We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retail company. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across subgroups of customers, e.g. across clients with relatively high vs. low previous purchases. Finally, we use optimal policy learning to learn (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention's effectiveness in terms of sales. Our study provides a use case for the application of causal machine learning in business analytics, in order to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.
    Date: 2022–04
  3. By: Jonas Metzger
    Abstract: We develop an asymptotic theory of adversarial estimators (`A-estimators'). Like maximum-likelihood-type estimators (`M-estimators'), both the estimator and estimand are defined as the critical points of a sample and population average respectively. A-estimators generalize M-estimators, as their objective is maximized by one set of parameters and minimized by another. The continuous-updating Generalized Method of Moments estimator, popular in econometrics and causal inference, is among the earliest members of this class which distinctly falls outside the M-estimation framework. Since the recent success of Generative Adversarial Networks, A-estimators received considerable attention in both machine learning and causal inference contexts, where a flexible adversary can remove the need for researchers to manually specify which features of a problem are important. We present general results characterizing the convergence rates of A-estimators under both point-wise and partial identification, and derive the asymptotic root-n normality for plug-in estimates of smooth functionals of their parameters. All unknown parameters may contain functions which are approximated via sieves. While the results apply generally, we provide easily verifiable, low-level conditions for the case where the sieves correspond to (deep) neural networks. Our theory also yields the asymptotic normality of general functionals of neural network M-estimators (as a special case), overcoming technical issues previously identified by the literature. We examine a variety of A-estimators proposed across econometrics and machine learning and use our theory to derive novel statistical results for each of them. Embedding distinct A-estimators into the same framework, we notice interesting connections among them, providing intuition and formal justification for their recent success in practical applications.
    Date: 2022–04
  4. By: Lucrezia Fanti; Marcelo C. Pereira; Maria Enrica Virgillito
    Abstract: Building upon the labour-augmented K+S modelling framework (Dosi et al., 2010, 2017, 2020), we address the analysis of the North-South divide by means of an agent-based model (ABM) endogenously reproducing divergence between two artificial macro-regions characterized by identical initial conditions in terms of productive and innovation structures, but different labour market organizations. Given the ex-ante initial conditions, we identify the role played by different functioning of the labour markets on the possible divergence across the two regions. We do find that divergences in labour market reverberate into asymmetric productive performance due to negative reinforcing feedback loop dynamics. We then confront alternative policy schemes by showing that investment policies directed at increasing machine renewal and higher substitutionary investment are the most effective in fostering the convergence process.
    Keywords: Agent-Based Models; Technology Gap; Labour Market.
    Date: 2022–05–17
  5. By: Marko Ledic (Faculty of Economics and Business Zagreb); Ivica Rubil (The Institute of Economics, Zagreb); Ivica Urban (Institute of Public Finance)
    Abstract: Using the microsimulation model EUROMOD for Croatia, we compare the results of simulation based on the original survey data (EU-SILC) with those based on the survey data corrected using tax records data and a recent survey correction method. We show that the correction method, although it debiases inequality estimates, may not be able to correct the income structure by source if some income sources are severely under-represented. In Croatia, this is the case for income from capital, property, and contractual work. As a solution, we propose to complement the correction method with an ad hoc pre-correction procedure. The corrections bring the aggregate amount, distribution, and structure of survey income closer to those in the tax data. Consequently, the simulated fiscal instruments become more like those in the tax data. Simulation of a hypothetical tax reform shows the results based on the uncorrected data may be misleading in terms of the estimated budgetary impact and the distributional incidence of the reform.
    Keywords: top incomes, survey data, tax records, tax-benefit microsimulation, EUROMOD, EU-SILC
    JEL: D31 H24
    Date: 2022–03
  6. By: Yizhe Xu; Tom H. Greene; Adam P. Bress; Brandon K. Bellows; Yue Zhang; Zugui Zhang; Paul Kolm; William S. Weintraub; Andrew S. Moran; Jincheng Shen
    Abstract: Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules (ITRs) that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective ITR (CE-ITR) under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit (NMB) to assess the trade-off between health benefits and related costs. We estimate CE-ITR as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal CE-ITR using NMB-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial (SPRINT) to illustrate the CE gains of assigning customized intensive blood pressure therapy.
    Date: 2022–04
  7. By: Antoine Kopp (ETH Zurich); Rebecca Westphal (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)); Didier Sornette (ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology)
    Abstract: We develop an agent-based model (ABM) of a financial market with multiple assets belonging either to the fixed income or equity asset classes. The aim is to reproduce the main stylized facts of fixed income markets with regards to the emerging dynamics of the yield curves. Our ABM is rooted in the market model of Kaizoji, Leiss, Saichev, and Sornette (2015) formulated with two types of traders: the rational and risk-averse fundamentalist investors and the noise traders who invest under the influence of social imitation and price momentum. The investors involved in the present market model diversify their investments between a preferred stock equivalent to a perpetual bond and multiple bonds of selected maturities. Among those, a zero-coupon bond provides a constant rate of return, while the prices of the coupon-paying bonds are determined at each time step by the equilibrium between the investors' demands and supplies. As a result, the ABM creates an evolving yield curve determined by the aggregate impact of the traders' investments. In agreement with real markets, it also produces transient turbulent periods in the prices' time series as well as a humped term structure of volatility. We compare the dynamics arising from different processes governing the risk-free rate with those of the historical U.S. treasury market. Introducing Vasicek's model of interest rates to both synthetic and empirical rates demonstrates the capacity of our ABM in reproducing the main characteristics of the surface of autocorrelation of the volatilities of the yields to maturity of the U.S. Treasury bonds for the selected time-frame.
    Keywords: Agent-based model, stylized facts, transient phenomena, fixed-income, yield curve.
    JEL: C60 D53 D70 G01 G17
    Date: 2022–04
  8. By: John Higgins (Department of Economics, University of Wisconsin, Madison, WI 53706, USA); Tarun Sabarwal (Department of Economics, University of Kansas, Lawrence, KS 66045, USA)
    Abstract: We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, and computationally tractable algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations. Our analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework.
    Keywords: Network games, coordination games, contagion, algorithmic computation
    JEL: C62 C72
    Date: 2022–04
  9. By: Matteo Coronese; Davide Luzzati
    Abstract: Extreme natural hazards represent, together with crises and wars, the most disruptive phenomena for economic activity. Their economic impact has been shown to be remarkable, long-lasting, and growing over time, though the exact mechanisms at stake are challenging to isolate and quantify. As these trends are likely to endure as global warming becomes more severe, the need for appropriate modeling of both short and long-run impacts of natural disasters is becoming increasingly pressing. Building on a mounting number of empirical works, we here provide a critical review of the modeling approaches traditionally employed in the related literature. Although with notable exceptions, conventional methods are generally based on Input-Output or Computational General Equilibrium models. These approaches, while analytically sound, are structurally ill-suited to capture certain aspects of natural hazard consequences. Systemic responses to such extreme events are typically characterized by complex interactions among heterogeneous agents, adaptive behavior, and out-of-equilibrium dynamics. We here argue that complexity methods can represent a valid alternative to bridge this policy-relevant gap. In particular, Agent-Based Models offer a powerful toolkit to account for non-linear geographical and temporal interdependencies, the presence of hysteresis and path dependency, the impact of technology changes, and can be fruitfully employed as laboratories for adaptation and mitigation policies.
    Keywords: Natural disasters; Socio-economic networks; Complexity; Agent-based models.
    Date: 2022–05–05
  10. By: Marie Obidzinski (Université Paris Panthéon Assas, CRED EA 7321, 75005 Paris, France); Yves Oytana (CRESE EA3190, Univ. Bourgogne Franche-Comté, F-25000 Besançon, France)
    Abstract: We study the design of optimal liability rules when the use of an advisory algorithm by a human operator (she) may generate an external harm. An artificial intelligence (AI) manufacturer (he) chooses the level of quality with which the algorithm is developed and the price at which it is distributed. The AI gives a prediction about the state of the world to the human operator who buys it, who can then decide to exert a judgment effort to learn the payoffs in each possible state of the world. We show that when the human operator overestimates the algorithm's accuracy (overestimation bias), imposing a strict liability rule on her is not optimal, because the AI manufacturer will exploit the bias by under-investing in the quality of the algorithm. Conversely, imposing a strict liability rule on the AI manufacturer may not be optimal either, since it has the adverse effect of preventing the human operator from exercising her judgment effort. We characterize the liability sharing rule that achieves the highest possible quality level of the algorithm, while ensuring that the human operator exercises a judgment effort. We then show that, when it can be used, a negligence rule generally achieves the first-best optimum. To conclude, we discuss the pros and cons of each type of rule.
    Keywords: Liability rules, Decision-making, Artificial intelligence, Cognitive bias, Judgment, Prediction
    JEL: K4
    Date: 2022–05
  11. By: Jakub Sokolowski; Marek Antosiewicz; Piotr Lewandowski
    Abstract: We estimate the macroeconomic and distributional effects that a ban on fuel imports from Russia would have in Poland. We simulate the embargo as a hike in oil, gas and coal prices, and evaluate the macroeconomic effects with a dynamic general equilibrium model. We soft-link it with a microsimulation model based on Household Budget Survey data to assess the impacts on various income groups. We find that the effects of an embargo on Russian fuels would be substantial but manageable. Depending on the severity of the price hikes, we expect Poland’s GDP to be lower by 0.2–3.3% by the end of 2022, and by 2.1–5.7% by 2025. Furthermore, depending on the price increases, high-income households would spend an additional 0.2–1.3% of their incomes on energy in 2022 and 0.7–1.6% in 2025, and low-income households would spend 0.3–4.7% more of their incomes on energy in 2022 and 2.6–4.8% in 2025. We suggest direct money transfers to less affluent households, and investments in alternative gas and oil supplies, energy efficiency, renewable energy and nuclear power as instruments that could ease the negative economic impacts of the embargo.
    Keywords: embargo; distributional effects; microsimulation; general equilibrium
    JEL: H23 P18 O15
    Date: 2022–05
  12. By: Sofia Maier (European Commission - JRC); Mattia Ricci (European Commission - JRC); Vanda Almeida (European Commission - JRC); Michael Christl (European Commission - JRC); Hugo Cruces (European Commission - JRC); Silvia De Poli (European Commission - JRC); Klaus Grunberger (European Commission - JRC); Adrian Hernandez (European Commission - JRC); Tine Hufkens (European Commission - JRC); Daniela Hupteva (European Commission - JRC); Viginta Ivaskaite-Tamosiune (European Commission - JRC); Marta Jedrych (European Commission - JRC); Alberto Mazzon (European Commission - JRC); Bianey Palma (European Commission - JRC); Andrea Papini (European Commission - JRC); Fidel Picos (European Commission - JRC); Alberto Tumino (European Commission - JRC); Estefanía Vazquez (European Commission - JRC)
    Abstract: This paper presents baseline results from the latest public version (I4.0+) of EUROMOD, the tax-benefit microsimulation model for the EU. We begin by briefly discussing the process of updating EUROMOD. We then present indicators for income inequality and at-risk-of-poverty using EUROMOD and discuss the main reasons for the differences between these and their correspondent from the EU Statistics on Incomes and Living Conditions (EU-SILC). We further compare EUROMOD distributional indicators across all EU 27 countries and over time between 2018 and 2021. Finally, we provide estimates of marginal effective tax rates (METR), an indicator which captures the effect of tax-benefit systems on work incentives at the intensive margin. Throughout the paper, we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results.
    Keywords: taxes, benefits, inequality, poverty, EUROMOD
    JEL: H24 H53 I32 I38
    Date: 2022–04
  13. By: Theresa Kuchler; Johannes Stroebel
    Abstract: We argue that the increasing availability of digital trace data presents substantial opportunities for researchers and policy makers to better understand the importance of social networks and social interactions in fostering economic opportunity and resilience. We review recent research efforts that have studied these questions using data from a wide range of sources, including online social networking platform such as Facebook, call detail record data, and network data from payment systems. We also describe opportunities for expanding these research agendas by using other digital trace data, and discuss various promising paths to increase researcher access to the required data, which is often collected and owned by private corporations.
    Date: 2022
  14. By: Almatrudi, Sulaiman; Parvate, Kanaad; Rothchild, Daniel; Vijay, Upadhi
    Abstract: Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and then speed back up again; this repeated braking and accelerating leads to higher fuel consumption, and correspondingly increasingly GHG emissions. Flow smoothing, or the use of a specially designed adaptive cruise controllers to dissipate these waves, can reduce fuel consumption of all the cars on the road. By keeping all vehicles at a constant speed, flow smoothing can minimize system-wide GHG emissions. This report presents the results of flow-smoothing when used in simulation, discusses current work on implementing flow-smoothing in real world-highways, and presents policy discussions on how to support flow smoothing.
    Keywords: Engineering, Greenhouse gases, traffic flow, traffic congestion, autonomous intelligent cruise control, intelligent vehicles, fuel consumption, traffic simulation
    Date: 2022–04–01
  15. By: Yangguang Huang (Department of Economics, The Hong Kong University of Science and Technology)
    Abstract: One prominent feature of online sales is that buyers rely on the search tools offered by platforms to process information when searching for products. We develop a model that captures how the search algorithm affects buyers’ search process, which influences the market equilibrium and welfare. The development of online platforms can reduce buyers’ search costs and promote competition among sellers, but a platform may design a search algorithm that is too “selective†from the social welfare perspective, which causes consumers to consider fewer options and suppresses competition. By using data from food delivery platforms, we provide empirical evidence that search algorithms deeply affect restaurant revenues. Markets with more chain restaurants with established brands tend to have more concentrated sales. This is partly caused by search algorithms being biased towards large restaurant chains.
    Keywords: online platform, search algorithm, consideration set, food-delivery platform
    JEL: D83 L11 L13 L42
    Date: 2021–01
  16. By: Michelle Alexopoulos; Xinfen Han; Oleksiy Kryvtsov; Xu Zhang
    Abstract: We measure soft information contained in the congressional testimonies of U.S. Federal Reserve Chairs and analyze its effect on financial markets. Our measures of Fed Chairs’ emotions expressed in words, voice and facial expressions are created using machine learning. Increases in the Chair’s text-, voice-, or face-emotion indices during these testimonies generally raise the SandP500 index and lower the VIX—indicating that these cues help shape market responses to Fed communications. These effects add up and propagate after the testimony, reaching magnitudes comparable to those after a policy rate cut. Markets respond most to the Chair’s emotions expressed about issues related to monetary policy.
    Keywords: Central bank research; Financial markets; Monetary policy communications
    JEL: E52 E58 E71
    Date: 2022–05
  17. By: Luís Henrique Paiva (IPC-IG); Leticia Bartholo (IPC-IG); Pedro H. G. Ferreira de Souza (IPC-IG); Rodrigo Octávio Orair (IPC-IG)
    Keywords: Inequality; poverty; income transfers
    Date: 2021–11

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