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on Computational Economics |
| By: | Yassine Sekaki (UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]); Hamza Ziane (UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]); Abdelhafid Khazzar (UAE - Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan]) |
| Abstract: | AI has risen as one of the most influential topics in scientific research over the past decade, focusing on the application of AI technologies in multiple disciplines. Management, as a science that has been studied, researched, and applied, shows great opportunities in terms of AI applications, notably as a tool for enhancing decisionmaking, sustainability in managerial practices, and so on. This bibliometric study aims to investigate the trends of AI research in management, defining the focus areas and research gap publications were gathered from the scopus database, and were processed through the bibliometrix library of the R studio analysis software , data were represented using the VOSviewer software, the research yielded a significant number of papers on different topics, data analysis comprised the country of origin, publication journal, author and keyword co-occurrence. The results showed an increase in publications stuying the uses of AI in management from 2021 to 2024 and a sharp decline of publication output in 2025. leading countries were shown to be China, india and the United States which dominated in volume of publication, while the United Kingdom dominated in citation impact. The dominating journals were ustainability, Technological Forecasting and Social Change, and IEEE Transactions on Engineering Management respectively. The Keywords co-occurrence and thematic mapping showed an increasing shift from technical applications to societal aspects such as sustainability, digital transformation, and decisionmaking support. The research shows a strategic shift in AI research, a changing landscape of pure and applied AI toward a more nuanced type of studied. |
| Keywords: | Artificial Intelligence, Management, Bibliometric Analysis, Technological Innovation, Digital Transformation |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05236423 |
| By: | Noufel Frikha (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Libo Li (School of Mathematics and Statistics - UNSW - University of New South Wales [Sydney]); Daniel Chee (School of Mathematics and Statistics - UNSW - University of New South Wales [Sydney]) |
| Abstract: | In this paper, we investigate optimal stopping problems in a continuous-time framework where only a discrete set of stopping dates is admissible, corresponding to the Bermudan option, within the so-called exploratory formulation. We introduce an associated control problem for the value function, represented as a non-cadlag reflected backward stochastic differential equation (RBSDE) with an entropy regulariser that promotes exploration, and we establish existence and uniqueness results for this entropy-regularised RBSDE. We then compare the entropy-regularised RBSDE with the theoretical value of a Bermudan option and propose a reinforcement learning algorithm based on a policy improvement scheme, for which we prove both monotone improvement and convergence. This methodology is further extended to Bermudan game options, where we obtain analogous results. Finally, drawing on the preceding analysis, we present two numerical approximation schemes - a BSDE solver based on a temporal-difference scheme and neural networks and the policy improvement algorithm - to illustrate the feasibility and effectiveness of our approach. |
| Keywords: | Bermudan option, Entropy Regularization, Reflected Backward Stochastic Differential Equation, Reinforcement Learning, Policy improvement |
| Date: | 2025–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:hal:cesptp:hal-05265653 |
| By: | Seung Jung Lee; Anne Lundgaard Hansen |
| Abstract: | This paper investigates the impact of the adoption of generative AI on financial stability. We conduct laboratory-style experiments using large language models to replicate classic studies on herd behavior in investment decisions. Our results show that AI agents make more rational decisions than humans, relying predominantly on private information over market trends. Increased reliance on AI-powered investment advice could therefore potentially lead to fewer asset price bubbles arising from animal spirits that trade by following the herd. However, exploring variations in the experimental settings reveals that AI agents can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. While optimal herding improves market discipline, this behavior still carries potential implications for financial stability. In other experimental variations, we show that AI agents are not purely algorithmic, but have inherited some elements of human conditioning and bias. |
| Keywords: | Herd behavior; Large language models; AI-powered traders; Financial markets; Financial stability |
| JEL: | C90 D82 G11 G14 G40 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-90 |
| By: | James R. Markusen |
| Abstract: | Traditional applied general-equilibrium (AGE) models have always faced trade-offs between analytical and computational tractability and counter-empirical restrictions. One is the assumption of homothetic preferences implying unitary income elasticities of demand, significantly inconsistent with data. Similarly, there is no “choke” income level, below which a certain good is not purchased and there is no choke price above which a good is not purchased, implying no changes in the extensive margin of trade. Here I exploit what I will label a Stone-Geary Modified (SGM) formulation. This produces a model in which there are non-unitary income elasticities, choke income levels for some/all goods, and choke prices. The second approach modifies CRIE (constant relative income elasticity) preferences which are preferred for modeling income elasticities, but don’t by themselves permit choke income and prices. While other authors have explored these properties in alternative ways, both my approaches have considerable advantages for high-dimension simulation models in that they retain CES structures and functional forms so that they can slot right into existing modeling formats. They require only small modifications to off-the-shelf cost and expenditure functions, and therefore goods and factor demand functions via Shepard’s lemma. Unobserved parameters can be calibrated from observed data and econometric estimates. |
| Keywords: | income elasticities, choke incomes, choke prices, applied general equilibrium |
| JEL: | F10 F17 C63 C68 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12159 |
| By: | Gaul, Johannes; Schrader, Pascal |
| Abstract: | We study the relationship between investors' social media activity and earnings announcement returns. To distinguish between information contained in peer-to-peer interaction and user-posted content, we analyze conversation networks on Reddit using centrality metrics from network science and classify user sentiment with large language models. We show that pre-announcement sentiment is positively associated with short-term cumulative abnormal returns only if it does not spark pre-announcement controversy. If pre-announcement controversy arises, we document a negative association. Our findings present a more nuanced view on the wisdom of crowds hypothesis, highlighting that peer-to-peer interaction on social media exhibits a pattern of normalization, and thus contains informational value beyond content. |
| Keywords: | Information Processing, Reddit Wallstreet Bets, Wisdom of Crowds, Conversation Networks, Large Language Models, Eigenvector Centrality, High-frequency Data |
| JEL: | G12 G14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:327108 |
| By: | Matteo Aquilina; Douglas Kiarelly Godoy de Araujo; Gaston Gelos; Taejin Park; Fernando Perez-Cruz |
| Abstract: | Predicting financial market stress has long proven to be a largely elusive goal. Advances in artificial intelligence and machine learning offer new possibilities to tackle this problem, given their ability to handle large datasets and unearth hidden nonlinear patterns. In this paper, we develop a new approach based on a combination of a recurrent neural network (RNN) and a large language model. Focusing on deviations from triangular arbitrage parity (TAP) in the Euro-Yen currency pair, our RNN produces interpretable daily forecasts of market dysfunction 60 business days ahead. To address the "black box" limitations of RNNs, our model assigns data-driven, time-varying weights to the input variables, making its decision process transparent. These weights serve a dual purpose. First, their evolution in and of itself provides early signals of latent changes in market dynamics. Second, when the network forecasts a higher probability of market dysfunction, these variable-specific weights help identify relevant market variables that we use to prompt an LLM to search for relevant information about potential market stress drivers. |
| Keywords: | market dysfunction, liquidity, arbitrage, artificial intelligence, financial stability |
| JEL: | G14 G15 G17 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1291 |
| By: | Pierre Courtioux (DVHE - De Vinci Higher Education, DVRC - De Vinci Research Center - DVHE - De Vinci Higher Education, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); François Métivier (IPG Paris - Institut de Physique du Globe de Paris) |
| Abstract: | Based on a microsimulation analysis over the period 2009-2019, this note presents the results of different scenarios for making the French R&D tax credit (CIR - Crédit Impôt Recherche) conditional on a diviend payment criterion. It shows that 27% of companies declaring R&D expenditure for the CIR in a given year pay dividends to their shareholders. Furthermore, 14% of companies declaring R&D expenditure eligible for the CIR increased their dividends payments in the same year. Depending on the scenario adopted, the introduction of a condition on the non-payment of dividends or the absence of an increase in payments could yield between 1 and 2.1 billion euros (i.e. between 16 and 36% of the total annual R&D tax credit claim). |
| Abstract: | Sur la base d'un exercice de microsimulation sur la période 2009-2019, cette note présente les résultats de différents scénarios de conditionnalité du crédit impôt recherche (CIR) à un critère de versement de dividendes. Elle montre que 27% des entreprises déclarant des dépenses de R&D au CIR une année donnée versent des dividendes à leurs actionnaires. Par ailleurs, 14% des entreprises déclarant des dépenses de R&D au CIR ont augmenté leurs dividendes la même année. Selon le scénario retenu, l'introduction d'une conditionnalité du CIR au non-versement de dividende ou à l'absence d'augmentation des versements pourrait rapporter entre 1 et 2, 1 milliards (c'est-à-dire entre 16 et 36% de la créance totale annuelle). |
| Keywords: | France, microsimulation, dividend, R&D tax credit, dividendes, crédit impôt recherche |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:hal:cesptp:halshs-05271781 |
| By: | Ghayal, Achintya |
| Abstract: | Environmental, Social, and Governance (ESG) reporting has shifted from voluntary disclosure to a regulatory imperative and cornerstone of corporate transparency. Traditional cost accounting systems, which emphasize direct, indirect, and overhead costs, often ignore externalities like carbon emissions, social equity investments, and governance overhead. This study investigates how embedding ESG-driven cost allocations reshapes financial reporting and managerial decisions in manufacturing firms. Using a simulated dataset spanning three divisions (Energy, Materials, Consumer), we compare outcomes under conventional accounting and an ESG-adjusted framework that includes carbon pricing equivalents, compliance costs, worker and governance programs. Our results show that ESG adjustments increase reported costs by approximately 20-30% and reduce operating margins by 5-7 percentage points, while significantly improving transparency across environmental, social, and governance metrics. Sensitivity analyses (varying carbon pricing) indicate that margin declines are robust to plausible environmental cost changes, though divisions with higher emissions are most affected. This research contributes to sustainability accounting by operationalizing ESG into cost allocation mechanics rather than treating it as supplementary disclosure. It provides a practical model for managers, regulators, and investors seeking to balance profitability with long-term accountability and risk mitigation. |
| Date: | 2025–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4ge2z_v1 |
| By: | Cecilia Aubrun; Rudy Morel; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique); Jean-Philippe Bouchaud |
| Abstract: | We introduce an unsupervised classification framework that leverages a multi-scale wavelet representation of time-series and apply it to stock price jumps. In line with previous work, we recover the fact that time-asymmetry of volatility is the major feature that separates exogenous, news-induced jumps from endogenously generated jumps. Local mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Using our wavelet-based representation, we investigate the endogenous or exogenous nature of co-jumps, which occur when multiple stocks experience price jumps within the same minute. Perhaps surprisingly, our analysis suggests that a significant fraction of co-jumps result from an endogenous contagion mechanism.E xtreme events and cascades of events are widespread occurrences in both natural and social systems (1). Examples include earthquakes, volcanic eruptions, hurricanes, epileptic crises (2, 3), epidemic spread, financial crashes (4-6), economic crises (7, 8), book sales shocks (9, 10), riot propagation (11, 12) or failures in socio-technical systems (13). Understanding the origin of such events is essential for forecasting and possibly stabilizing their dynamics.A widely studied question is the reflexive, self-exciting nature of those shocks. The concept of financial market reflexivity was introduced by Soros in ( 14), to describe the idea that price dynamics are mostly endogenous and arise from internal feedback mechanisms, as was first surmised by Cutler, Poterba and Summers in 1988 (15) (see also ( 16)). Extreme events, in particular, often arise from feedback mechanisms within the system's structure (1, 17, 18). Quantifying the extent of endogeneity in a complex system and distinguishing events caused by external shocks from those provoked endogenously, and more generally identifying different classes of events, are crucial questions.Prior research has proposed to differentiate between endogenous and exogenous dynamics by analyzing the profile of activity around the shock (9, 10, 19, 20), in particular in the context of financial markets (21-23). It has been observed that endogenous shocks are preceded by a growth phase mirroring the post event powerlaw relaxation, in contrast to exogenous shocks that are strongly asymmetric. The universality of this result is quite intriguing as they have been observed in various contexts: intra-day book sales on Amazon (9, 10), daily views of YouTube videos (20) and intra-day financial market volatility and price jumps (23, 24). Meanwhile, Wu et al. (25) differentiate exogenous and endogenous bursts of comment posting on social media using the analysis of collective emotion dynamics and time-series distributions of comment arrivals.Furthermore, in complex systems, events can propagate along two directions: temporally and towards other elements of the system. Financial markets offer an attractive setting for studying multi-dimensional shocks due to the abundance of available data, the frequent occurrence of financial shocks and price jumps and the inter-connectivity of markets. In fact, a recent study by Lillo et al. (26, 27) demonstrates the frequent occurrence of "co-jumps", defined as simultaneous jumps of multiple stocks (as illustrated in Fig. 1) and establishes a correlation between their prevalence and the inter-connectivity of different markets.In this paper, we address the problem of classifying financial price jumps (and co-jumps), in particular measuring their self-exciting character, by analyzing their time-series using wavelets. We introduce an unsupervised classification based on an embedding Φ(x) of each jump time-series of returns x(t) into a low dimensional-space more appropriate to clustering. Such embedding, composed of wavelet scattering coefficients (see (28) and below), relies on wavelet coefficients of the time-series at the time of the jump t = 0 and wavelet coefficients of volatility. Such coefficients are Significance StatementCascades of events and extreme occurrences have garnered significant attention across diverse domains like seismology, neuroscience, economics, finance, and other social sciences. Such events may arise from internal system dynamics (endogenous) or external shocks (exogenous). Devising rigorous methods to distinguish between them is vital for professionals and regulators to create early warning systems and effective responses. Understanding these dynamics could improve the stability and resilience of crisisprone socio-economic systems. We show how wavelets can be used for the unsupervised separation of shocks in financial time-series, based on time-asymmetry around the shock. Additionally, we highlight the significant role contagion mechanisms play in financial markets. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04735506 |
| By: | Benin, Samuel |
| Abstract: | This paper presents an Excel-based interactive decision-support tool that policymakers and development practitioners can use to evaluate policy options to achieve targeted outcomes of the Malabo Declaration at the country level. The tool is based on a partial equilibrium simulation model that allows the user to simulate different scenarios based on the desired level of change in one outcome or more. For each scenario that is created, the simulated results provide information on the level of change required in each of the policies included in the model, the level of change in the other outcomes included in the model, and the allocation of the resources provided, including reallocation of some of the existing resources. A prototype of the tool that is developed using the fourth biennial review (BR) data on Ghana, which has some quality issues, is presented to demonstrate the potential features and utility of the tool. Limitations of the model and further work that is required to develop the actual tool for reliable policy evaluation are discussed. The latter includes using accurate data on the various indicators and expanding it to cover more years, in addition to developing a web-based interactive version of the tool. |
| Keywords: | caadp; agrifood systems; decision support; policy analysis; public expenditure; Africa |
| Date: | 2024–09–12 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:152192 |
| By: | Nuur Rasyiqah Zainuddin (Faculty of Business and Economics, Universiti Malaya, 50603, Kuala Lumpur, Malaysia Author-2-Name: Chen Chen Yong Author-2-Workplace-Name: Faculty of Business and Economics, Universiti Malaya, 50603, Kuala Lumpur, Malaysia Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | " Objective - This study aims to identify Malaysia's machinery and equipment (M&E) industry local supply chain, classify its key industries, and estimate the economic effects of changes in final demand within the industry. Methodology - This study employs a quantitative input–output analysis to assess both the strength and distance of linkages between the machinery and equipment (M&E) industry and other sectors in Malaysia. Industries are classified according to normalized backward and forward linkages, and multiplier analyses are used to evaluate the economic effects of changes in final demand for the M&E industry. Findings - Results indicate that the M&E industry in Malaysia is primarily a backward linkage-oriented sector, stimulating growth in upstream industries through its demand for inputs. The analysis further identifies five key industries, including M&E, within its supply chain. Scenario-based multiplier simulations reveal negative economic impacts from reduced export demand for the M&E industry, but positive impacts from investment in the M&E sector and its related industries. The results also indicate the M&E industry's reliance on external demand. Novelty - This study demonstrates the value of systems thinking by integrating scenario analysis with supply-chain linkages, industrial classifications, and multiplier effects to produce a more comprehensive economic assessment. The approach highlights potential policy insights for improving public resource allocation in Malaysia. Type of Paper - Empirical" |
| Keywords: | Machinery and Equipment Industry; Input-Output Analysis; Supply Chain |
| JEL: | C67 D57 L60 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:jber262 |
| By: | Feng, Wenxiu; Alcántara Mata, Antonio; Ruiz Mora, Carlos |
| Abstract: | Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal levels, which can be modeled by advanced data-driven approaches. To this end, we use a probabilistic neural network as a surrogate that accounts for the spatiotemporal correlations of wind speed. This neural network uses ReLU activation functions so that it can be reformulated as mixed-integer linear set of constraints (constraint learning). We embed these constraints into the placement decision problem, formulated as a two-stage stochastic optimization problem. Specifically, conditional quantiles of the total electricity production are regarded as recursive decisions in the second stage. We use real high-resolution regional data from a northern region in Spain. We validate that the constraint learning approach outperforms the classical bilinear interpolation method. Numerical experiments are implemented on risk-averse investors. The results indicate that risk-averse investors concentrate on dominant sites with strong wind, while exhibiting spatial diversification and sensitive capacity spread in non-dominant sites. Furthermore, we show that if we introduce transmission line costs in the problem, risk-averse investors favor locations closer to the substations. On the contrary, risk-neutral investors are willing to move to further locations to achieve higher expected profits. Our results conclude that the proposed novel approach is able to tackle a portfolio of regional wind farm placements and further provide guidance for risk-averse investors. |
| Keywords: | Constraint learning; Optimal investment; Quantile neural network; Wind generation; Stochastic optimization |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:48103 |
| By: | Frédéric Marty (Université Côte d'Azur, GREDEG, CNRS, France); Thierry Warin (HEC Montréal; CIRANO, OBVIA, GPAI/CEIMIA) |
| Abstract: | Digital markets are increasingly dominated by entities that leverage technical specificities such as network effects, economies of scale, and scope, as well as significant advantages in data access and critical infrastructure, including computing power and cloud capacities. The advent of generative artificial intelligence (AI) marks a potential inflection point in this landscape. In this context, the primary barriers to entry are no longer merely data and open source foundation models but the availability of large, high-quality datasets and substantial computing power. This paper examines whether these barriers will entrench the dominant positions of Big Tech companies or if they will catalyze a reshuffling of competitive dynamics. By focusing on the dual challenges of data and computing power, this study identifies the key factors that will shape the future competitive landscape of the generative AI industry. This article contributes to the ongoing debate in industrial economics and strategic management regarding the potentially disruptive effects of generative AI on the market power of Big Tech firms. Can this technological shift recalibrate competitive dynamics, or will it ultimately serve to entrench existing power structures? At its core, the article seeks to interrogate a prevailing narrative - namely, the notion that innovation inherently sustains competitive processes, even in the face of short-term lock-in effects. |
| Keywords: | Generative AI, data-based advantage, digital ecosystems, Big Techs |
| JEL: | K21 L12 L13 L41 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-38 |
| By: | Nicolás Forteza (BANCO DE ESPAÑA); Sergio Puente (BANCO DE ESPAÑA) |
| Abstract: | Studying the labor market attachment (LMA) for the non-working population is crucial for several economic outcomes, such as real wages or long-term non-employment. Official statistics rely on self-reported variables and rule-based procedures to assign the labor market status of an individual. However, this classification does not take into account other individual-level characteristics, like variables related to reservation wages or the amount and type of job offers received, implying that estimates of non-worker status could be biased. In this paper, we propose a novel methodology to measure non-workers’ LMA. Using the Spanish Labor Force Survey (LFS), we define two groups (attached vs. non-attached), and estimate a probability distribution for each individual of belonging to such groups. To recover these probability distributions, we rely on unsupervised and supervised machine learning algorithms. We describe the differences between LFS unemployment, other measures of attachment in the literature, and our non-worker classification. We identify the instances in which our proposed methodology has a tighter relationship with measures like salaries, GDP and employment flows. |
| Keywords: | labor market attachment, unemployment, labor force |
| JEL: | J21 J82 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2534 |
| By: | Gallego-Moll, Carlos; Carrasco-Ribelles, Lucía A.; Casajuana, Marc; Maynou, Laia; Arocena, Pablo; Violán, Concepción; Zabaleta-Del-Olmo, Edurne |
| Abstract: | Objectives: To broadly map the research landscape to identify trends, gaps, and opportunities in data sets, methodologies, outcomes, and reporting standards for artificial intelligence (AI)-based healthcare utilization prediction. Methods: We conducted a scoping review following the Joanna Briggs Institute methodology. We searched 3 major international databases (from inception to January 2025) for studies applying AI in predictive healthcare utilization. Extracted data were categorized into data sets characteristics, AI methods and performance metrics, predicted outcomes, and adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) + AI reporting guidelines. Results: Among 1116 records, 121 met inclusion criteria. Most were conducted in the United States (62%). No study incorporated all 6 relevant variable groups: demographic, socioeconomic, health status, perceived need, provider characteristics, and prior utilization. Only 7 studies included 5 of these groups. The main data sources were electronic health records (60%) and claims (28%). Ensemble models were the most frequently used (66.9%), whereas deep learning models were less common (16.5%). AI methods were primarily used to predict future events (90.1%), with hospitalizations (57.9%) and visits (33.1%) being the most predicted outcomes. Adherence to general reporting standards was moderate; however, compliance with AI-specific TRIPOD + AI items was limited. Conclusions: Future research should broaden predicted outcomes to include process- and logistics-oriented events, extend applications beyond prediction—such as cohort selection and matching—and explore underused AI methods, including distance-based algorithms and deep neural networks. Strengthening adherence to TRIPOD-AI reporting guidelines is also essential to enhance the reliability and impact of AI in healthcare planning and economic evaluation. |
| Keywords: | artificial intelligence; health economics; healthcare utilisation outcomes; resource allocation; review |
| JEL: | J1 |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129293 |
| By: | Nobuhiro Abe (Bank of Japan); Yuto Ishikuro (Bank of Japan); Koki Nakayama (Bank of Japan); Yutaro Takano (Bank of Japan) |
| Abstract: | Do heterogeneity and competition among banks matter for the macroeconomy? To address this question, we develop a Heterogeneous Bank New Keynesian (HBANK) model that incorporates oligopolistic competition among banks in both loan and deposit markets into an otherwise canonical New Keynesian model. We calibrate model parameters for the cost structure and demand for loans and deposits using data of the 170 largest banks in the U.S. Differences in the parameter values reflect differences among banks in the size of duration risk they take, markups of loan rates, and markdowns of deposit rates. Based on simulation exercises, we show that aggregate lending becomes more responsive to monetary and productivity shocks in our HBANK model than in a Representative Bank New Keynesian model (RBANK), primarily because of heterogeneity in duration risk and the responsiveness of loan markups among banks. |
| Keywords: | banking, business cycles |
| JEL: | E32 E43 E44 E52 G21 |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:boj:bojwps:wp25e09 |
| By: | Monica Bonacina (Fondazione Eni Enrico Mattei, Università degli Studi di Milano); Mert Demir (Fondazione Eni Enrico Mattei); Antonio Sileo (Fondazione Eni Enrico Mattei, GREEN – Università Bocconi); Angela Zanoni (Fondazione Eni Enrico Mattei, Università di Roma La Sapienza, Research Institute for Sustainable Economic Growth – National Research Council) |
| Abstract: | The transition to a zero-emission vehicle fleet represents a pivotal element of Europe’s decarbonization strategy, with Italy’s participation being particularly significant given the size of its automotive market. This study investigates the potential for battery electric cars (BEVs) to drive decarbonization of Italy’s passenger vehicle fleet, focusing on the feasibility of targets set in the National Integrated Plan for Energy and Climate (PNIEC). Leveraging artificial neural networks, we integrate macroeconomic indicators, market-specific variables, and policy instruments to predict fleet dynamics and identify key factors influencing BEV adoption. We forecast that while BEV registrations will continue growing through 2030, the growth rate is projected to decelerate, presenting challenges for meeting ambitious policy targets. Our feature importance analysis demonstrates that BEV adoption is driven by an interconnected set of economic, infrastructural, and behavioral factors. Specifically, our model highlights that hybrid vehicle registrations and the vehicle purchase index exert the strongest influence on BEV registrations, suggesting that policy interventions should prioritize these areas to maximize impact. By offering data-driven insights and methodological innovations, our findings contribute to more effective policy design for accelerating sustainable mobility adoption while accounting for market realities and consumer behavior. |
| Keywords: | sustainable mobility, electric vehicle, neural networks, shap interpretation |
| JEL: | N74 Q55 Q58 R40 C45 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.16 |
| By: | Yantuan Yu (Guangdong University of Foreign Studies); Ning Zhang (Yonsei University) |
| Abstract: | The effect of market-based climate policy instruments on a just transition cannot be underestimated, especially for developing economies. In this study, we provide rigorous empirical evidence on how China’s Energy Quota Trading System(EQTS) can drive green technology innovation and support an equitable, low-carbon transition. Specifically, based on a quasi-experimental modeling framework, we use a Double Debiased Machine Learning method to estimate the casual effect of China’s EQTS on energy productivity. Further, we explore the mechanisms of impact and examine heterogeneity effects from regional, resource endowment, and environmental regulation stringency perspectives. The empirical findings show that EQTS significantly improves energy productivity, exhibiting an average marginal effect of 13.2%. Robustness checks confirm the validity of the results after controlling for potential confounders. Green technology innovation and energy transition function as critical pathways through which the policy enhances energy productivity. This study presents empirical evidence on how effective market-based regulatory mechanism are in the energy sector and offers practical policy recommendations for integrating innovation-driven strategies within national carbon mitigation frameworks. |
| Keywords: | Energy Quota Trading System; Energy Productivity; Natural-Experiment Modeling; Green Technology Innovation; Energy Transition |
| JEL: | O13 O47 Q43 R11 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2025rwp-258 |
| By: | Kevin He (University of Pennsylvania); Ran Shorrer (Pennsylvania State University); Mengjia Xia (University of Pennsylvania) |
| Abstract: | We conduct an incentivized laboratory experiment to study people’s perception of generative artificial intelligence (GenAI) alignment in the context of economic decisionmaking. Using a panel of economic problems spanning the domains of risk, time preference, social preference, and strategic interactions, we ask human subjects to make choices for themselves and to predict the choices made by GenAI on behalf of a human user. We find that people overestimate the degree of alignment between GenAI’s choices and human choices. In every problem, human subjects’ average prediction about GenAI’s choice is substantially closer to the average human-subject choice than it is to the GenAI choice. At the individual level, different subjects’ predictions about GenAI’s choice in a given problem are highly correlated with their own choices in the same problem. We explore the implications of people overestimating GenAI alignment in a simple theoretical model. |
| Date: | 2025–04–06 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-019 |
| By: | Marcelo Veracierto |
| Abstract: | This paper introduces a general method for computing aggregate fluctuations in economies with private information. Instead of the cross-sectional distribution of agents across individual states, the method uses as a state variable a vector of spline coefficients describing a long history of past individual decision rules. The model is then linearized with respect to that vector. Applying the computational method to a Mirrlees RBC economy with known analytical solution recovers the solution perfectly well. This test provides significant confidence on the accuracy of the method. |
| Keywords: | Computational methods; Heterogeneous agent; Business cycle; Private information |
| JEL: | C63 D82 E32 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:101803 |
| By: | Bach, Ruben L.; Klamm, Christopher; Heyne, Stefanie; Kogan, Irena; Kononykhina, Olga; Jarck, Jana |
| Abstract: | Accurate occupational classification from open-ended survey responses is vital for research in sociology, economics, and political science, yet manual coding remains resource-intensive and difficult to scale. We propose a novel pipeline that leverages large language models (LLMs) augmented with retrieval (RAG) to automate the assignment of International Standard Classification of Occupations (ISCO) codes. Drawing on survey data from a sample of recently arrived Afghan and Syrian refugees in Germany, we preprocess noisy occupational descriptions using LLMs and apply vector-based similarity search to retrieve candidate ISCO codes. The final classification is selected by LLMs, constrained to the retrieved candidates and accompanied by interpretable justifications. We evaluate the system’s performance against expert-coded labels, demonstrating high agreement and robustness across languages. Our findings suggest that RAG-powered LLMs can substantially improve the accuracy, scalability, and accessibility of occupational classification, with particular benefits for multilingual and resource-constrained research settings. In addition, we describe a prototypical pipeline that other researchers can readily adapt for applying LLMs to similar classification tasks, facilitating transparency, reproducibility, and broader adoption. |
| Date: | 2025–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ge56f_v1 |
| By: | Liudmila Alekseeva; Silvia Dalla Fontana; Caroline Genc; Lin Peng |
| Abstract: | This study presents the first large-scale analysis of face-based impression factors in the venture capital (VC) industry. Using machine learning to extract key impression factors from founders’ photos, we find that perceived trustworthiness, dominance, and youthfulness significantly predict VCs’ initial funding decisions, with relative importance varying by founder gender, team composition, and industry. These factors also predict the funding amount, follow-on financing, and longer-term outcomes, such as unicorn status and acquisitions. Therefore, even experienced investors rely on facial cues when evaluating founders, and such cues serve as imperfect but informative signals of venture success. |
| Keywords: | venture capital, investment selection, impressions, facial recognition, trustworthiness, dominance, attractiveness |
| Date: | 2025–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:ete:msiper:772779 |
| By: | Nathalie Picard; André de Palma |
| Abstract: | This chapter explores residential location models through a comprehensive review of the literature, key facts, theoretical frameworks, estimation methods, and simulation techniques. It focuses on the factors driving residential segregation using a standard individual discrete choice model, specifically a nested logit framework. This model incorporates household preferences for local amenities, dwelling types, and homeownership. The analysis is extended by introducing borrowing constraints that restrict some households' ability to purchase property. To illustrate, the framework is applied to the Paris region. By relaxing borrowing constraints, we simulate a hypothetical redistribution of socio-demographic characteristics across the region and demonstrate how this tool can be employed for policy analysis. A comparison of actual and simulated distributions reveals that easing credit constraints encourages households to relocate farther from the city center. However, if only poor households benefit, they are less likely to integrate with wealthier households, thereby intensifying segregation. This finding highlights those policies designed to support low-income households might inadvertently increase segregation citywide, necessitating careful re-evaluation. |
| Keywords: | Housing choice, financial constraints, borrowing, segregation, suburban areas, urban sprawl, endogenous choice sets. |
| JEL: | R21 R23 R31 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ulp:sbbeta:2025-33 |
| By: | Charles Taragin; Benjamin Wallace; Eddie Watkins |
| Abstract: | We study how corporate debt influences the competitive outcomes of horizontal and conglomerate mergers. In contrast to standard models where debt does not affect pricing, our framework shows that mergers can spread fixed debt obligations across a broader product portfolio, creating an "insurance effect" against adverse demand shocks. This effect interacts with the traditional recapture effect from reduced competition. Using numerical simulations and a case study of a major casino merger, we find that debt can either dampen or amplify post-merger price increases, depending on the merger's structure and the market environment. |
| Keywords: | Financial structure; Merger simulation; Horizontal markets |
| JEL: | L41 L13 K21 G32 G34 |
| Date: | 2025–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-80 |
| By: | Gouriéroux, Christian; Monfort, Alain |
| Abstract: | The aim of this paper is to link the machine learning method of multilayer perceptron (MLP) neural network with the classical analysis of stochastic state space models. We consider a special class of state space models with multiple layers based on affine conditional Laplace transforms. This new class of Affine Feedforward Stochastic (AFS) neural network provides closed form recursive formulas for recursive filtering of the state variables of different layers. This approach is suitable for online inference by stochastic gradient ascent optimization and for recursive computation of scores such as backpropagation. The approach is extended to recurrent neural networks and identification issues are discussed. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130941 |
| By: | Oscar Becerra (Universidad de los Andes) |
| Abstract: | This article presents the CEDE Pension Model, a microsimulation tool designed to project key variables of Colombia’s pension system. The model integrates administrative data, household surveys, and institutional parameters to simulate labor histories using Markov chains and to project the number of older adults who will receive pension benefits through 2100. Its design enables the evaluation of policy options for old-age economic protection, highlighting trade-offs across coverage, equity and progressivity, adequacy, and fiscal sustainability. |
| Keywords: | Social protection, pensions, simulation, Colombia |
| JEL: | H55 J26 J14 C15 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:col:000089:021633 |
| By: | Mukashov, Askar; Thurlow, James; Dorosh, Paul A.; Jones, Eleanor |
| Abstract: | This study explores Nepal’s vulnerability to economic shocks and identifies those contributing most to economic uncertainty. Our analysis is based on an empirically based estimation of the probability distribution of these shocks and a machine learning summary of several thousand simulations of their impacts using a Computable General Equilibrium (CGE) model for Nepal. In this way, we are able to quantify the contribution of each shock to the uncer-tainty of economic outcomes (gross domestic product [GDP], private consumption, poverty, and undernourishment). Our findings indicate that, given the very high import intensity of the economy, world market price and foreign exchange (FX) flow volatility have the largest impact on household welfare (consumption, poverty and undernour-ishment). However, domestic yield volatility, especially cereal yield volatility, is the most important risk to Nepal’s GDP. However, Overall, these findings suggest that risk mitigation strategies, such as increasing average crop yields, adopting technologies and practices that narrow yield uncertainties, or diversifying production away from risky crops and sectors, can have major benefits for Nepal’s households and the overall economy. |
| Keywords: | risk assessment; climate; shock; economic shock; market prices; computable general equilibrium models; machine learning; agriculture; crop yield; Nepal; Asia; Southern Asia |
| Date: | 2024–12–30 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ewracb:168723 |