nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒01‒30
23 papers chosen by

  1. fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems By Isaac Tamblyn; Tengkai Yu; Ian Benlolo
  2. Stock Market Prediction via Deep Learning Techniques: A Survey By Jinan Zou; Qingying Zhao; Yang Jiao; Haiyao Cao; Yanxi Liu; Qingsen Yan; Ehsan Abbasnejad; Lingqiao Liu; Javen Qinfeng Shi
  3. Mean-field neural networks-based algorithms for McKean-Vlasov control problems * By Huy\^en Pham; Xavier Warin
  4. Risk sharing with deep neural networks By Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni
  5. Agent-Based Modeling for Studying the Spontaneous Emergence of Money By Mattia Di Russo; Zakaria Babutsidze; Célia da Costa Pereira; Maurizio Iacopetta; Andrea G. B. Tettamanzi
  6. Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning By Jeremi Assael; Thibaut Heurtebize; Laurent Carlier; Fran\c{c}ois Soup\'e
  7. Agent-based modelling of a small-scale fishery in Corsica By Eric Innocenti; Corinne Idda; Dominique Prunetti; Pierre-Régis Gonsolin
  8. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys By Thegeya, Aaron; Mitterling, Thomas; Martinez Jr., Arturo; Bulan, Joseph; Durante, Ron Lester; Mag-atas, Jayzon
  9. Mission-Oriented Policies and the “Entrepreneurial State” at Work: An Agent-Based Exploration By Giovanni Dosi; Francesco Lamperti; Mariana Mazzucato; Mauro Napoletano; Andrea Roventini
  10. An agent-based model of trickle-up growth and income inequality By Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
  11. Returns to Solar Panels in the Housing Market: A Meta Learner Approach By Elias Asproudis; Cigdem Gedikli; Oleksandr Talavera; Okan Yilmaz
  12. Measuring an artificial intelligence agent's trust in humans using machine incentives By Tim Johnson; Nick Obradovich
  13. Inequality-Constrained Monetary Policy in a Financialized Economy By Luca Eduardo Fierro; Federico Giri; Alberto Russo
  14. Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty By Margarita Leib; Nils K\"obis; Rainer Michael Rilke; Marloes Hagens; Bernd Irlenbusch
  15. A Quantum-Inspired Binary Optimization Algorithm for Representative Selection By Anna G. Hughes; Jack S. Baker; Santosh Kumar Radha
  16. Assessing the economic impact of lockdowns in Italy: a computational input-output approach By Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni; Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano; Andrea Roventini
  17. Refining Public Policies with Machine Learning: The Case of Tax Auditing By Marco Battaglini; Luigi Guiso; Chiara Lacava; Douglas L. Miller; Eleonora Patacchini
  18. Simulating Intraday Transactions in the Canadian Retail Batch System By Nellie Zhang
  19. Bayesian Modeling of Time-Varying Parameters Using Regression Trees By Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
  20. Optimal Retirement with Disability Pensions By Hans Fehr; Adrian Fröhlich
  21. A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection By Ida Nervik Hjelseth; Arvid Raknerud; Bjørn H. Vatne
  22. Enhancing Resilience: Model-based Simulations By d'Artis Kancs
  23. Let’s Get Fiscal: Extending the Small Macroeconometric Model of the Philippine Economy By Debuque-Gonzales, Margarita; Corpus, John Paul P.

  1. By: Isaac Tamblyn; Tengkai Yu; Ian Benlolo
    Abstract: We discuss our simulation tool, fintech-kMC, which is designed to generate synthetic data for machine learning model development and testing. fintech-kMC is an agent-based model driven by a kinetic Monte Carlo (a.k.a. continuous time Monte Carlo) engine which simulates the behaviour of customers using an online digital financial platform. The tool provides an interpretable, reproducible, and realistic way of generating synthetic data which can be used to validate and test AI/ML models and pipelines to be used in real-world customer-facing financial applications.
    Date: 2023–01
  2. By: Jinan Zou; Qingying Zhao; Yang Jiao; Haiyao Cao; Yanxi Liu; Qingsen Yan; Ehsan Abbasnejad; Lingqiao Liu; Javen Qinfeng Shi
    Abstract: The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction.
    Date: 2022–12
  3. By: Huy\^en Pham (UPD7, LPSM); Xavier Warin (EDF R\&D, FiME Lab)
    Abstract: This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to learn the solution on the Wasserstein space. We propose several algorithms either based on dynamic programming with control learning by policy or value iteration, or backward SDE from stochastic maximum principle with global or local loss functions. Extensive numerical results on different examples are presented to illustrate the accuracy of each of our eight algorithms. We discuss and compare the pros and cons of all the tested methods.
    Date: 2022–12
  4. By: Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni
    Abstract: We consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based framework to solve the problem and we prove the convergence of the approximated inf-convolution, as well as the approximated optimal allocations, to the corresponding theoretical values. We support our findings with several numerical experiments.
    Date: 2022–12
  5. By: Mattia Di Russo (Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems - I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Zakaria Babutsidze (SKEMA Business School); Célia da Costa Pereira (Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems - I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Maurizio Iacopetta (SKEMA Business School); Andrea G. B. Tettamanzi (WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics - CRISAM - Inria Sophia Antipolis - Méditerranée - Inria - Institut National de Recherche en Informatique et en Automatique - Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems - I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur)
    Abstract: A central question in economics is how a society accepts money, defined as a commodity used as a medium of exchange, as an unplanned outcome of the individual interactions. This question has been approached theoretically in the literature and investigated by means of agent-based modeling. While an important aspect of the theory is the individual's speculative behavior, that is, the acceptance of money despite a potential short-term loss, previous work has been unable to reproduce it with boundedly rational agents. We investigate the reasons for the failure of previous work to have boundedly rational agents learn speculative strategies. Starting with an agent-based model proposed in the literature, where the intelligence of the agents is guided by a learning classifier system that is shown to be capable of learning trade strategies (core strategies) that involve short sequences of trades, we test several modifications of the original model and we come up with a set of assumptions that enable the spontaneous emergence of speculative strategies, which explain the emergence of money even when the agents have bounded rationality.
    Keywords: Search and Money Reinforcement Learning Social Simulation, Search and Money, Reinforcement Learning, Social Simulation
    Date: 2022–11–17
  6. By: Jeremi Assael (BNPP CIB GM Lab, MICS); Thibaut Heurtebize (BNPP CIB GM Lab); Laurent Carlier (BNPP CIB GM Lab); Fran\c{c}ois Soup\'e
    Abstract: As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.
    Date: 2022–12
  7. By: Eric Innocenti (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Corinne Idda (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Dominique Prunetti (LISA - Lieux, Identités, eSpaces, Activités - UPP - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique); Pierre-Régis Gonsolin
    Abstract: In this work we introduce a new multi-stock, multi-fleet, multi-species and bioeconomic model for the complex system of a small-scale fishery. The objective is to study fisheries in order to ensure the renewal of the stock of biomass. This stock represents both a means of subsistence for fishermen but also contributes to food security. We model the system as a Multi-Agent System using both Cellular Automata Model (CAM) and Agent-Based Model (ABM) computational modelling approaches. CAM are used to describe the environment and the dynamics of resources. ABM are used to describe the behaviour of fishing activities. The main interest of the conceptual model lies in the proposed laws and in its capacity to organize hierarchically all the local interactions and transition rules within the simulated entities. We report preliminary results showing that our modelling approach facilitates software parameterization for the specific requirements implied by the context of a small-scale fishery. The main results of this work consist in the creation of a computer modelling structure CAM and ABM, which constitutes a preliminary for an optimized resources management. In a future development, we will improve the behavior of economic agents in order to consider the complexity of their decision making.
    Keywords: Fishery modelling, Multi-Agent System, NetLogo pattern
    Date: 2022–09–19
  8. By: Thegeya, Aaron (World Data Lab); Mitterling, Thomas (World Data Lab); Martinez Jr., Arturo (Asian Development Bank); Bulan, Joseph (Asian Development Bank); Durante, Ron Lester (Asian Development Bank); Mag-atas, Jayzon (Asian Development Bank)
    Abstract: Roads are vital to support the transportation of people, goods, and services, among others. To yield their optimal socioeconomic impact, proper maintenance of existing roads is required; however, this is typically underfunded. Since detecting road quality is both labor and capital intensive, information on it is usually scarce, especially in resource-constrained countries. Accordingly, the study examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads. With this goal, a preliminary algorithm was created and validated using medium-resolution satellite imagery and existing road roughness data from the Philippines. After analysis, it was determined that the algorithm had an accuracy rate up to 75% and can be used for the preliminary identification of poor to bad roads. This provides an alternative for compiling road quality data, especially for areas where conventional methods can be difficult to implement. Nonetheless, additional technical enhancements need to be explored to further increase the algorithm’s prediction accuracy and enhance its robustness.
    Keywords: road quality; road maintenance; Sustainable Development Goals; remote sensing; deep learning
    JEL: O18 R42
    Date: 2022–12–22
  9. By: Giovanni Dosi (LEM - Laboratory of Economics and Management - SSSUP - Scuola Universitaria Superiore Sant'Anna [Pisa]); Francesco Lamperti (UP1 - Université Paris 1 Panthéon-Sorbonne); Mariana Mazzucato; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Andrea Roventini
    Abstract: We study the impact of alternative innovation policies on the short- and long-run performance of the economy, as well as on public finances, extending the Schumpeter meeting Keynes agent-based model (Dosi et al., 2010). In particular, we consider market-based innovation policies such as R&D subsidies to firms, tax discount on investment, and direct policies akin to the "Entrepreneurial State" (Mazzucato, 2013), involving the creation of public research oriented firms diffusing technologies along specific trajectories, and funding a Public Research Lab conducting basic research to achieve radical innovations that enlarge the technological opportunities of the economy. Simu- lation results show that all policies improve productivity and GDP growth, but the best outcomes are achieved by active discretionary State policies, which are also able to crowd-in private investment and have positive hysteresis effects on growth dynamics. For the same size of public resources allocated to market-based interventions, "Mission" innovation policies deliver significantly better aggregate performance if the government is patient enough and willing to bear the intrinsic risks related to innovative activities.
    Keywords: Innovation policy, mission-oriented R&D, entrepreneurial state, agent-based modelling
    Date: 2021–01–01
  10. By: Elisa Palagi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po, GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Jean-Luc Gaffard (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)
    Abstract: We build an agent-based model to study how coordination failures, credit constraints and unequal access to investment opportunities affect inequality and aggregate income dynamics. The economy is populated by households who can invest in alternative projects associated with different productivity growth rates. Access to investment projects also depends on credit availability. The income of each house- hold is determined by the output of the project but also by aggregate demand conditions. We show that aggregate dynamics is affected by income distribution. Moreover, we show that the model features a trickle-up growth dynamics. Redistribution towards poorer households raises aggregate demand and is beneficial for the income growth of all agents in the economy. Extensive numerical simulations show that our model is able to reproduce several stylized facts concerning income inequality and social mobility. Finally, we test the impact of redistributive fiscal policies, showing that fiscal policies facilitating access to investment opportunities by poor households have the largest impact in terms of raising long-run aggregate income and decreasing income inequality. Moreover, policy timing is important: fiscal policies that are implemented too late may have no significant effects on inequality.
    Keywords: income inequality, social mobility, credit constraints, coordination failures, effective demand, trickle-up growth, fiscal policy
    Date: 2021–01–01
  11. By: Elias Asproudis (Swansea University); Cigdem Gedikli (Swansea University); Oleksandr Talavera (University of Birmingham); Okan Yilmaz (Swansea University)
    Abstract: This paper aims to estimate the returns to solar panels in the UK residential housing market. Our analysis applies a causal machine learning approach to Zoopla property data containing about 5 million observations. Drawing on meta-learner algorithms, we provide strong evidence fortifying that solar panels are directly capitalized into sale prices. Our results point to a selling price premium above 6 percent (range between 6.2 percent to 6.9 percent depending on the meta-learner) associated with solar panels. Considering that the average selling price is 230, 536 GBP in our sample, this corresponds to an additional 14, 293 GBP to 15, 906 GBP selling price premium for houses with solar panels. Our results are robust to traditional hedonic pricing models and matching techniques.
    Keywords: solar panels; residential housing market; sale prices; machine-learning; meta-learners
    JEL: R21 R31 Q42 Q5
    Date: 2023–01
  12. By: Tim Johnson; Nick Obradovich
    Abstract: Scientists and philosophers have debated whether humans can trust advanced artificial intelligence (AI) agents to respect humanity's best interests. Yet what about the reverse? Will advanced AI agents trust humans? Gauging an AI agent's trust in humans is challenging because--absent costs for dishonesty--such agents might respond falsely about their trust in humans. Here we present a method for incentivizing machine decisions without altering an AI agent's underlying algorithms or goal orientation. In two separate experiments, we then employ this method in hundreds of trust games between an AI agent (a Large Language Model (LLM) from OpenAI) and a human experimenter (author TJ). In our first experiment, we find that the AI agent decides to trust humans at higher rates when facing actual incentives than when making hypothetical decisions. Our second experiment replicates and extends these findings by automating game play and by homogenizing question wording. We again observe higher rates of trust when the AI agent faces real incentives. Across both experiments, the AI agent's trust decisions appear unrelated to the magnitude of stakes. Furthermore, to address the possibility that the AI agent's trust decisions reflect a preference for uncertainty, the experiments include two conditions that present the AI agent with a non-social decision task that provides the opportunity to choose a certain or uncertain option; in those conditions, the AI agent consistently chooses the certain option. Our experiments suggest that one of the most advanced AI language models to date alters its social behavior in response to incentives and displays behavior consistent with trust toward a human interlocutor when incentivized.
    Date: 2022–12
  13. By: Luca Eduardo Fierro (Institute of Economics, Scuola Superiore Sant’Anna Pisa, Italy); Federico Giri (Department of Economics and Social Sciences, Università Politecnica delle Marche, Ancona, Italy); Alberto Russo (Department of Economics and Social Sciences, Università Politecnica delle Marche, Ancona, Italy and Department of Economics, Universitat Jaume I, Castellón, Spain)
    Abstract: We study how income inequality affects monetary policy through the inequalityhousehold debt channel. We design a minimal macro Agent-Based model that replicates several stylized facts, including two novel ones: falling aggregate saving rate and decreasing bankruptcies during the household’s debt boom phase. When inequality meets financial liberalization, a leaning against-the-wind strategy can preserve financial stability at the cost of high unemployment, whereas an accommodative strategy can dampen the fall of aggregate demand at the cost of larger leverage. We conclude that inequality may constrain the central bank, even when it is not explicitly targeted.
    Keywords: Inequality, Financial Fragility, Monetary Policy, Agent-Based Model.
    JEL: E21 E25 E31 E52 G51
    Date: 2023
  14. By: Margarita Leib; Nils K\"obis; Rainer Michael Rilke; Marloes Hagens; Bernd Irlenbusch
    Abstract: Artificial Intelligence (AI) increasingly becomes an indispensable advisor. New ethical concerns arise if AI persuades people to behave dishonestly. In an experiment, we study how AI advice (generated by a Natural-Language-Processing algorithm) affects (dis)honesty, compare it to equivalent human advice, and test whether transparency about advice source matters. We find that dishonesty-promoting advice increases dishonesty, whereas honesty-promoting advice does not increase honesty. This is the case for both AI- and human advice. Algorithmic transparency, a commonly proposed policy to mitigate AI risks, does not affect behaviour. The findings mark the first steps towards managing AI advice responsibly.
    Date: 2023–01
  15. By: Anna G. Hughes; Jack S. Baker; Santosh Kumar Radha
    Abstract: Advancements in quantum computing are fuelling emerging applications across disciplines, including finance, where quantum and quantum-inspired algorithms can now make market predictions, detect fraud, and optimize portfolios. Expanding this toolbox, we propose the selector algorithm: a method for selecting the most representative subset of data from a larger dataset. The selected subset includes data points that simultaneously meet the two requirements of being maximally close to neighboring data points and maximally far from more distant data points where the precise notion of distance is given by any kernel or generalized similarity function. The cost function encoding the above requirements naturally presents itself as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is well-suited for quantum optimization algorithms - including quantum annealing. While the selector algorithm has applications in multiple areas, it is particularly useful in finance, where it can be used to build a diversified portfolio from a more extensive selection of assets. After experimenting with synthetic datasets, we show two use cases for the selector algorithm with real data: (1) approximately reconstructing the NASDAQ 100 index using a subset of stocks, and (2) diversifying a portfolio of cryptocurrencies. In our analysis of use case (2), we compare the performance of two quantum annealers provided by D-Wave Systems.
    Date: 2023–01
  16. By: Severin Reissl; Alessandro Caiani; Francesco Lamperti; Mattia Guerini; Fabio Vanni (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Giorgio Fagiolo; Tommaso Ferraresi; Leonardo Ghezzi; Mauro Napoletano (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Andrea Roventini (OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po)
    Abstract: We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown.
    Keywords: input-output, Covid-19, lockdown, Italy
    Date: 2021–01–01
  17. By: Marco Battaglini; Luigi Guiso; Chiara Lacava; Douglas L. Miller; Eleonora Patacchini
    Abstract: We study the extent to which ML techniques can be used to improve tax auditing efficiency using administrative data, without the need of randomized audits. Using Italy's population data on sole proprietorship tax returns, audits and their outcome, we develop a new approach to address the so called selective labels problem - the fact that a ML algorithm must necessarily be trained on endogenously selected data. We document the existence of substantial margins for raising revenue from audits by improving the selection of taxpayers to audit with ML. Replacing the 10% least productive audits with an equal number of taxpayers selected by our trained algorithm raises detected tax evasion by as much as 38%, and evasion that is actually payed back by 29%.
    JEL: H2 H20 H26
    Date: 2022–12
  18. By: Nellie Zhang
    Abstract: This paper proposes a unique approach to simulate intraday transactions in the Canadian retail payments batch system. Such transactions are currently unobtainable. The simulation procedure, though demonstrated in the realm of payments systems, has tremendous potential for helping with data-deficient problems where only high-level aggregate information is available. The approach uses the concept of integer composition in combinatorics to break down the daily total value and volume (available) into individual data points (unavailable) throughout the day. The algorithm also introduces a technique to incorporate any intraday timing pattern (known or hypothetical) to make simulated data closer to reality. Simulation results show that the probability distribution of individual payment values is remarkably stable through repeated random sampling. This suggests a high degree of accuracy and viability of this simulation method. In addition, the densities of simulated intraday transactions are found to be invariably skewed to the left of the mean payment value, which reflects the nature of retail payments systems.
    Keywords: Financial Markets; Payment clearing and settlement systems
    JEL: C C63 E42 E58
    Date: 2023–01
  19. By: Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
    Abstract: In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
    Keywords: Bayesian Vector Autoregression; Time-varying Parameters; Nonparametric Modeling; Machine Learning; Regression Trees; Phillips Curve; Business Cycle Shocks
    JEL: C11 C32 C51 E32
    Date: 2023–01–11
  20. By: Hans Fehr; Adrian Fröhlich
    Abstract: This paper develops a general equilibrium life-cycle model with endogenous retirement and disability risk, in order to quantify the impact of recent pension reforms in Germany. At certain ages households may either apply for disability pensions (DP) or old-age pensions (OAP), de-pending on eligibility rules and the generosity of the two programs. Our policy analysis focus on the increase in the normal retirement age (NRA) from age 65 to 67 (Reform 2007) and the recent increase in the maximum assessment age (MAA) for DP benefits (Reform 2018). In contrast to the first reform, the second reform received hardly any attention in the public pension debate in Germany. Our simulation results indicate that with current eligibility and benefit rules, the second reform will almost neutralize the financial and economic benefits of the first reform. Consequently, securing the financial stability of the system will require a tightening of eligibility rules and/or a reduction of early retirement benefits in the future.
    Keywords: overlapping generations, stochastic general equilibrium, endogenous retirement, disability pensions
    JEL: C68 D91 H55 J24
    Date: 2022
  21. By: Ida Nervik Hjelseth; Arvid Raknerud; Bjørn H. Vatne
    Abstract: We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule†) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt.
    Keywords: Bankruptcy prediction, credit risk, corporate bank debt, Lasso, weighted logistic regression
    JEL: C25 C33 C53 G33 D22
    Date: 2022–06–20
  22. By: d'Artis Kancs
    Abstract: Since several years, the fragility of global supply chains (GSCs) is at historically high levels. In the same time, the landscape of hybrid threats is expanding; new forms of hybrid threats create different types of uncertainties. This paper aims to understand the potential consequences of uncertain events - like natural disasters, pandemics, hybrid and/or military aggression - on GSC resilience and robustness. Leveraging a parsimonious supply chain model, we analyse how the organisational structure of GSCs interacts with uncertainty, and how risk-aversion vs. ambiguity-aversion, vertical integration vs. upstream outsourcing, resilience vs. efficiency trade-offs drive a wedge between decentralised and centralised optimal GSC diversification strategies in presence of externalities. Parameterising the scalable data model with World-Input Output Tables, we simulate the survival probability of a GSC and implications for supply chain robustness and resilience. The presented model-based simulations provide an interoperable and directly comparable conceptualisation of positive and normative effects of counterfactual resilience and robustness policy choices under individually optimal (decentralised) and socially optimal (centralised) GSC organisation structures.
    Date: 2022–12
  23. By: Debuque-Gonzales, Margarita; Corpus, John Paul P.
    Abstract: This study presents a small macroeconometric model with a fiscal sector, extending the model presented in Debuque-Gonzales and Corpus (2022). The model retains the original core blocks of domestic demand, international trade, employment, prices, and monetary sectors and adds a fiscal sector consisting of equations for government revenues, expenditures, and debt. Behavioral equations are estimated in error-correction form (using ARDL methodology) on quarterly data from 2002 to 2019. In-sample simulations demonstrate acceptable levels of predictive accuracy for most macroeconomic variables, even when producing dynamic forecasts. The model also shows plausible outcomes on the fiscal side in response to shocks in world oil prices, the exchange rate, and primary expenditure, showing the expanded model’s policy simulation capabilities. The next steps for developing the model include adding a detailed financial block, modeling the aggregate supply side, and incorporating expectations. Comments to this paper are welcome within 60 days from the date of posting. Email
    Keywords: macroeconometric model;Philippine economy;forecast;simulation;fiscal sector
    Date: 2022

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