nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒04‒10
thirty papers chosen by

  1. Biased auctioneers By Aubry, Mathieu; Kräussl, Roman; Manso, Gustavo; Spaenjers, Christophe
  2. Many learning agents interacting with an agent-based market model By Matthew Dicks; Andrew Paskaramoothy; Tim Gebbie
  3. Machine Learning as a Tool for Hypothesis Generation By Jens Ludwig; Sendhil Mullainathan
  4. From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks By Ramos; Pablo Negri; Martín Breitkopf; María Laura Ojeda
  5. Identification-robust inference for the LATE with high-dimensional covariates By Yukun Ma
  6. Determinants of Heat Risk in an Aging Population: A Machine Learning Approach By Klauber, Hannah; Koch, Nicolas
  7. Quantum Monte Carlo simulations for financial risk analytics: scenario generation for equity, rate, and credit risk factors By Titos Matsakos; Stuart Nield
  8. Analyzing and forecasting economic crises with an agent-based model of the euro area By Cars Hommes; Sebastian Poledna
  9. A Deep Reinforcement Learning Trader without Offline Training By Boian Lazov
  10. Predicting Stock Price Movement as an Image Classification Problem By Matej Steinbacher
  11. Mr.Keynes and the... Complexity! A suggested agent-based version of the General Theory of Employment, Interest and Money By Alessio Emanuele Biondo
  12. Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning By Luke Sherman; Jonathan Proctor; Hannah Druckenmiller; Heriberto Tapia; Solomon M. Hsiang
  13. Artificial Intelligence and the Economics of Decision-Making By Naudé, Wim
  14. The changes to the Italian tax and welfare system implemented in 2022: fairness and efficiency profiles By Emanuele Dicarlo; Pasquale Recchia; Antonella Tomasi
  15. Moving beyond expectations. From cohort-component to microsimulation projections By Zhiyang Jia; Stefan Leknes; Sturla A. Løkken
  16. A portrait of AI adopters across countries: Firm characteristics, assets’ complementarities and productivity By Flavio Calvino; Luca Fontanelli
  17. Not lost in translation: The implications of machine translation technologies for language professionals and for broader society By Francesca Borgonovi; Justine Hervé; Helke Seitz
  18. Regulatory costs and market power By Singla, Shikhar
  19. On Using Proportional Representation Methods as Alternatives to Pro-Rata Based Order Matching Algorithms in Stock Exchanges By Sanjay Bhattacherjee; Palash Sarkar
  20. FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs By Vedant Choudhary; Sebastian Jaimungal; Maxime Bergeron
  21. Artificial intelligence in science: An emerging general method of invention By Stefano Bianchini; Moritz Müller; Pierre Pelletier
  22. Uniform Pessimistic Risk and Optimal Portfolio By Sungchul Hong; Jong-June Jeon
  23. Identifying Optimal Indicators and Lag Terms for Nowcasting Models By Jing Xie
  24. Artificial intelligence and unemployment: New insights By Mihai Mutascu
  25. Trade-offs in the design of financial algorithms By Alexia GAUDEUL; Caterina GIANNETTI
  26. Flexible Routing for Ridesharing By Dessouky, Maged; Mahtab, Zuhayer
  27. Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty By Minkyu Shin; Jin Kim; Bas van Opheusden; Thomas L. Griffiths
  28. Defying Gravity: The Economic Effects of Social Distancing By Alfredo García; Christopher Hartwell; Martín Andrés Szybisz
  29. Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices By Jonathan Berrisch; Florian Ziel
  30. BEMGIE: Belgian Economy in a Macro General and International Equilibrium model By Gregory de Walque; Thomas Lejeune; Ansgar Rannenberg; Magne Mogstad

  1. By: Aubry, Mathieu; Kräussl, Roman; Manso, Gustavo; Spaenjers, Christophe
    Abstract: We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
    Keywords: art, auctions, experts, asset valuation, biases, machine learning, computer vision
    JEL: C50 D44 G12 Z11
    Date: 2023
  2. By: Matthew Dicks; Andrew Paskaramoothy; Tim Gebbie
    Abstract: We consider the dynamics and the interactions of multiple reinforcement learning optimal execution trading agents interacting with a reactive Agent-Based Model (ABM) of a financial market in event time. The model represents a market ecology with 3-trophic levels represented by: optimal execution learning agents, minimally intelligent liquidity takers, and fast electronic liquidity providers. The optimal execution agent classes include buying and selling agents that can either use a combination of limit orders and market orders, or only trade using market orders. The reward function explicitly balances trade execution slippage against the penalty of not executing the order timeously. This work demonstrates how multiple competing learning agents impact a minimally intelligent market simulation as functions of the number of agents, the size of agents' initial orders, and the state spaces used for learning. We use phase space plots to examine the dynamics of the ABM, when various specifications of learning agents are included. Further, we examine whether the inclusion of optimal execution agents that can learn is able to produce dynamics with the same complexity as empirical data. We find that the inclusion of optimal execution agents changes the stylised facts produced by ABM to conform more with empirical data, and are a necessary inclusion for ABMs investigating market micro-structure. However, including execution agents to chartist-fundamentalist-noise ABMs is insufficient to recover the complexity observed in empirical data.
    Date: 2023–03
  3. By: Jens Ludwig; Sendhil Mullainathan
    Abstract: While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about who to jail. We begin with a striking fact: The defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mugshot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: They are not explained by demographics (e.g. race) or existing psychology research; nor are they already known (even if tacitly) to people or even experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional dataset (e.g. cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our paper is that hypothesis generation is in and of itself a valuable activity, and hope this encourages future work in this largely “pre-scientific” stage of science.
    JEL: B4 C01
    Date: 2023–03
  4. By: Ramos; Pablo Negri; Martín Breitkopf; María Laura Ojeda
    Keywords: Recurrent Neural Networks, Regional Commodities Prices, Shock Simulations
    JEL: C45 Q11
    Date: 2021–11
  5. By: Yukun Ma
    Abstract: This paper investigates the local average treatment effect (LATE) with high-dimensional covariates, regardless of the strength of identification. We propose a novel test statistic for the high-dimensional LATE, and show that our test has uniformly correct asymptotic size. Applying the double/debiased machine learning (DML) method to estimate nuisance parameters, we develop easy-to-implement algorithms for inference/confidence interval of the high-dimensional LATE. Simulations indicate that our test is efficient in the strongly identified LATE model.
    Date: 2023–02
  6. By: Klauber, Hannah (Mercator Research Institute on Global Commons and Climate Change (MCC)); Koch, Nicolas (Mercator Research Institute on Global Commons and Climate Change (MCC))
    Abstract: This paper identifies individual and regional risk factors for hospitalizations caused by heat within the German population over 65 years of age. Using administrative insurance claims data and a machine-learning-based regression model, we causally estimate heterogeneous heat effects and explore the geographic, morbidity, and socioeconomic correlates of heat vulnerability. Our results indicate that health effects distribute highly unevenly across the population. The most vulnerable are more likely to suffer from chronic diseases such as dementia and Alzheimer's disease and live in rural areas with more old-age poverty and less nursing care. We project that unabated climate change might bring heat to areas with particularly vulnerable populations, which could lead to a five-fold increase in heat-related hospitalization by 2100.
    Keywords: heat, climate change, hospitalization, risk factors, adaptation, machine learning
    JEL: I14 I18 Q51 Q54 Q58
    Date: 2023–03
  7. By: Titos Matsakos; Stuart Nield
    Abstract: Monte Carlo (MC) simulations are widely used in financial risk management, from estimating value-at-risk (VaR) to pricing over-the-counter derivatives. However, they come at a significant computational cost due to the number of scenarios required for convergence. Quantum MC (QMC) algorithms are a promising alternative: they provide a quadratic speed-up as compared to their classical counterparts. Recent studies have explored the calculation of common risk measures and the optimisation of QMC algorithms by initialising the input quantum states with pre-computed probability distributions. In this paper, we focus on incorporating scenario generation into the quantum computation by simulating the evolution of risk factors over time. Specifically, we assemble quantum circuits that implement stochastic models for equity (geometric Brownian motion), interest rate (mean-reversion models), and credit (structural and reduced-form credit models) risk factors. We then feed these scenarios to QMC simulations to provide end-to-end examples for both market and credit risk use cases.
    Date: 2023–03
  8. By: Cars Hommes (University of Amsterdam); Sebastian Poledna (International Institute for Applied Systems Analysis)
    Abstract: We develop an agent-based model for the euro area that fulfils widely recommended requirements for nextgeneration macroeconomic models by i) incorporating financial frictions, ii) relaxing the requirement of rational expectations, and iii) including heterogeneous agents. Using macroeconomic and sectoral data, the model includes all sectors (financial, non-financial, household, and a general government) and connects financial flows and balance sheets with stock-flow consistency. The model, moreover, incorporates many features considered essential for future policy models, such as a financial accelerator with debt-financed investment and a complete GDP identity, and allows for non-linear responses. We first show that the agent-based model outperforms dynamic stochastic general equilibrium and vector autoregression models in out-of-sample forecasting. We then demonstrate that the model can help make sense of extreme macroeconomic movements and apply the model to the three recent major economic crises of the euro area: the Financial crisis of 2007-2008 and the subsequent Great Recession, the European sovereign debt crisis, and the COVID-19 recession. We show that the model, due to non-linear responses, is capable of predicting a severe crisis arising endogenously around the most intense phase of the Great Recession in the euro area without any exogenous shocks. By analysing the COVID-19 recession, we further demonstrate the model for scenario analysis with exogenous shocks. Here we show that the model reproduces the observed deep recession followed by a swift recovery and also captures the persistent rise in inflation following the COVID-19 recession
    Keywords: agent-based models, behavioural macro, macroeconomic forecasting, microdata, financial crisis, inflation and prices, coronavirus disease (COVID- 19).
    JEL: E70 E32 E37
    Date: 2023–03–15
  9. By: Boian Lazov
    Abstract: In this paper we pursue the question of a fully online trading algorithm (i.e. one that does not need offline training on previously gathered data). For this task we use Double Deep $Q$-learning in the episodic setting with Fast Learning Networks approximating the expected reward $Q$. Additionally, we define the possible terminal states of an episode in such a way as to introduce a mechanism to conserve some of the money in the trading pool when market conditions are seen as unfavourable. Some of these money are taken as profit and some are reused at a later time according to certain criteria. After describing the algorithm, we test it using the 1-minute-tick data for Cardano's price on Binance. We see that the agent performs better than trading with randomly chosen actions on each timestep. And it does so when tested on the whole dataset as well as on different subsets, capturing different market trends.
    Date: 2023–03
  10. By: Matej Steinbacher
    Abstract: The paper studies intraday price movement of stocks that is considered as an image classification problem. Using a CNN-based model we make a compelling case for the high-level relationship between the first hour of trading and the close. The algorithm managed to adequately separate between the two opposing classes and investing according to the algorithm's predictions outperformed all alternative constructs but the theoretical maximum. To support the thesis, we ran several additional tests. The findings in the paper highlight the suitability of computer vision techniques for studying financial markets and in particular prediction of stock price movements.
    Date: 2023–03
  11. By: Alessio Emanuele Biondo
    Abstract: This paper presents an agent-based model with the aim to follow, as closely as possible, the rationale of the macroeconomic model advanced by J.M. Keynes in his famous book entitled The General Theory of Unemployment, Interest and Money. Since the task is admittedly ambitious, it has been divided over more than one single paper. In the present one, the modelling choices are described and the main objective of the General Theory will be provided, i.e., to determine the level of income and employment starting from the interest rate, the marginal efficiency of capital, and the marginal propensity to consume. In the forthcoming companion paper, results from a more articulated set of simulations - referred to some exercises of monetary and fiscal policy - will be reported. The description of the elements of the model is provided with several supporting parts of the original text.
    Date: 2023–03
  12. By: Luke Sherman; Jonathan Proctor; Hannah Druckenmiller; Heriberto Tapia; Solomon M. Hsiang
    Abstract: The United Nations Human Development Index (HDI) is arguably the most widely used alternative to gross domestic product for measuring national development. This is in large part due to its multidimensional nature, as it incorporates not only income, but also education and health. However, the low country-level resolution of the global HDI data released by the Human Development Report Office of the United Nations Development Programme (N=191 countries) has limited its use at the local level. Recent efforts used labor-intensive survey data to produce HDI estimates for first-level administrative units (e.g., states/provinces). Here, we build on recent advances in machine learning and satellite imagery to develop the first global estimates of HDI for second-level administrative units (e.g., municipalities/counties, N = 61, 591) and for a global 0.1 × 0.1 degree grid (N=806, 361). To accomplish this we develop and validate a generalizable downscaling technique based on satellite imagery that allows for training and prediction with observations of arbitrary shape and size. This enables us to train a model using provincial administrative data and generate HDI estimates at the municipality and grid levels. Our results indicate that more than half of the global population was previously assigned to the incorrect HDI quintile within each country, due to aggregation bias resulting from lower resolution estimates. We also illustrate how these data can improve decision-making. We make these high resolution HDI estimates publicly available in the hope that they increase understanding of human wellbeing globally and improve the effectiveness of policies supporting sustainable development. We also make available the satellite features and software necessary to increase the spatial resolution of any other global-scale administrative data that is detectable via imagery.
    JEL: C1 C8 I32 R1
    Date: 2023–03
  13. By: Naudé, Wim (RWTH Aachen University)
    Abstract: Artificial Intelligence (AI) scientists are challenged to create intelligent, autonomous agents that can make rational decisions. In this challenge, they confront two questions: what decision theory to follow and how to implement it in AI systems. This paper provides answers to these questions and makes three contributions. The first is to discuss how economic decision theory – Expected Utility Theory (EUT) – can help AI systems with utility functions to deal with the problem of instrumental goals, the possibility of utility function instability, and coordination challenges in multi-actor and human-agent collectives settings. The second contribution is to show that using EUT restricts AI systems to narrow applications, which are "small worlds" where concerns about AI alignment may lose urgency and be better labelled as safety issues. This papers third contribution points to several areas where economists may learn from AI scientists as they implement EUT. These include consideration of procedural rationality, overcoming computational difficulties, and understanding decision-making in disequilibrium situations.
    Keywords: economics, artificial intelligence, expected utility theory, decision-theory
    JEL: D01 C60 C45 O33
    Date: 2023–03
  14. By: Emanuele Dicarlo (Bank of Italy); Pasquale Recchia (Bank of Italy); Antonella Tomasi (Bank of Italy)
    Abstract: The paper presents the effects of two revision interventions of the Italian tax and benefits system implemented in 2022: (i) the introduction of the single and universal allowance for children (AUU); (ii) changes to the structure of personal income tax (IRPEF). Using BIMic, the static microsimulation model of the Bank of Italy, the analysis shows how the combined effect of the two interventions increases the progressivity of the system and reduces inequality (these effects are mainly attributable to the introduction of the AUU). The two changes - and in particular the intervention on personal income tax - also contribute to reducing the monetary disincentives to the supply of labour both at the extensive margin and at the intensive margin and contribute to mitigating the irregular trend of the effective marginal rates.
    Keywords: family policies, personal income tax, redistribution, efficiency, microsimulation
    JEL: H22 H23 H24 H31 C15 C63 H2 D31
    Date: 2023–03
  15. By: Zhiyang Jia; Stefan Leknes; Sturla A. Løkken (Statistics Norway)
    Abstract: Population projections are predominantly made using the cohort-component method (CCM). The opportunities for further development within that framework are limited. Lately, with advances in technical and computational capacity, the microsimulation framework has become a serious contender. In contrast to CCM, it allows for rich com-plexity of behavior and provides insights on projection uncertainty. Still, demographers have been reluctant to apply this framework, which may be due to lack of guidance. We contribute by clarifying underlying CCM assumptions, translating a multi-regional version of the model into a dynamic spatial microsimulation model, and discuss the usefulness of prediction intervals for planning. Using data for Norway, we demonstrate that the re-sults for the two models are equivalent, even for very small subgroups, and converge with relatively few simulations. The model can easily be amended with additional indi-vidual heterogeneity, facilitating more accurate representations of population dynamics.
    Keywords: Population projections; microsimulation; cohort-component method; uncertainty; multi-regional; small area
    JEL: J11 C15 C63 C81
    Date: 2023–03
  16. By: Flavio Calvino; Luca Fontanelli
    Abstract: This report analyses the use of artificial intelligence (AI) in firms across 11 countries. Based on harmonised statistical code (AI diffuse) applied to official firm-level surveys, it finds that the use of AI is prevalent in ICT and Professional Services and more widespread across large – and to some extent across young – firms. AI users tend to be more productive, especially the largest ones. Complementary assets, including ICT skills, high-speed digital infrastructure, and the use of other digital technologies, which are significantly related to the use of AI, appear to play a critical role in the productivity advantages of AI users.
    Keywords: AI, artificial intelligence, productivity, technology adoption
    Date: 2023–04–11
  17. By: Francesca Borgonovi; Justine Hervé; Helke Seitz
    Abstract: The paper discusses the implications of recent advances in artificial intelligence for knowledge workers, focusing on possible complementarities and substitution between machine translation tools and language professionals. The emergence of machine translation tools could enhance social welfare through enhanced opportunities for inter-language communication but also create new threats because of persisting low levels of accuracy and quality in the translation output. The paper uses data on online job vacancies to map the evolution of the demand for language professionals between 2015 and 2019 in 10 countries and illustrates the set of skills that are considered important by employers seeking to hire language professionals through job vacancies posted on line.
    JEL: J21 J23 J28 Z13
    Date: 2023–03–30
  18. By: Singla, Shikhar
    Abstract: Industry concentration and markups in the US have been rising over the last 3- 4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period. We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in concentration is associated with lower elasticity of entry with respect to Tobin's Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms.
    Keywords: Market Power, Competition, Concentration, Machine Learning, Regulations
    JEL: L51 L11 C45 D4
    Date: 2023
  19. By: Sanjay Bhattacherjee; Palash Sarkar
    Abstract: The main observation of this short note is that methods for determining proportional representation in electoral systems may be suitable as alternatives to the pro-rata order matching algorithm used in stock exchanges. Our simulation studies provide strong evidence that the Jefferson/D'Hondt and the Webster/Saint-Lagu\"{e} proportional representation methods provide order allocations which are closer to proportionality than the order allocations obtained from the pro-rata algorithm.
    Date: 2023–03
  20. By: Vedant Choudhary; Sebastian Jaimungal; Maxime Bergeron
    Abstract: This paper introduces a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability integral transform penalty to reduce model misspecification. We demonstrate that learning the joint dynamics of IV surfaces and prices produces market scenarios that are consistent with historical features and lie within the sub-manifold of surfaces that are free of static arbitrage.
    Date: 2023–03
  21. By: Stefano Bianchini (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Moritz Müller (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Pierre Pelletier (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: This paper offers insights into the diffusion and impact of artificial intelligence in science. More specifically, we show that neural network-based technology meets the essential properties of emerging technologies in the scientific realm. It is novel, because it shows discontinuous innovations in the originating domain and is put to new uses in many application domains; it is quick growing, its dimensions being subject to rapid change; it is coherent, because it detaches from its technological parents, and integrates and is accepted in different scientific communities; and it has a prominent impact on scientific discovery, but a high degree of uncertainty and ambiguity associated with this impact. Our findings suggest that intelligent machines diffuse in the sciences, reshape the nature of the discovery process and affect the organization of science. We propose a new conceptual framework that considers artificial intelligence as an emerging general method of invention and, on this basis, derive its policy implications.
    Keywords: Artificial intelligence, Emerging technologies, Method of invention, Scientific discovery, Novelty
    Date: 2022–12
  22. By: Sungchul Hong; Jong-June Jeon
    Abstract: The optimality of allocating assets has been widely discussed with the theoretical analysis of risk measures. Pessimism is one of the most attractive approaches beyond the conventional optimal portfolio model, and the $\alpha$-risk plays a crucial role in deriving a broad class of pessimistic optimal portfolios. However, estimating an optimal portfolio assessed by a pessimistic risk is still challenging due to the absence of an available estimation model and a computational algorithm. In this study, we propose a version of integrated $\alpha$-risk called the uniform pessimistic risk and the computational algorithm to obtain an optimal portfolio based on the risk. Further, we investigate the theoretical properties of the proposed risk in view of three different approaches: multiple quantile regression, the proper scoring rule, and distributionally robust optimization. Also, the uniform pessimistic risk is applied to estimate the pessimistic optimal portfolio models for the Korean stock market and compare the result of the real data analysis. It is empirically confirmed that the proposed pessimistic portfolio presents a more robust performance than others when the stock market is unstable.
    Date: 2023–03
  23. By: Jing Xie
    Abstract: Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p, q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
    Keywords: Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global
    Date: 2023–03–03
  24. By: Mihai Mutascu (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)
    Date: 2021–03
  25. By: Alexia GAUDEUL; Caterina GIANNETTI
    Abstract: We investigate trade-offs when trying to encourage adoption of stock-trading algorithms. We organize an artificial stock market experiment over three weeks where investors experience trading on their own and with the help of a financial algorithm.They then choose whether to adopt it. We vary the algorithm in terms of its trading strategy and whether its decisions can be overriden or not. We find that adoption rates are low, but investors are more likely to adopt an algorithm that trades actively and that they can override. The investor’s trading preferences, as revealed by their own trading decisions, does not consistently affect algorithm take-up. Rather, algorithm adoption depends mainly on how succesful a trader was when trading on their own vs. when an algorithm was trading in their place. Analysis of an exit questionnaire matches those observations with the reasons given by individuals for rejecting or adopting a financial algorithm.
    Keywords: algorithm aversion, disposition effect, robo-advisers, sophisticated investors, stocktrading
    JEL: G11 G40
    Date: 2023–03–01
  26. By: Dessouky, Maged; Mahtab, Zuhayer
    Abstract: Traffic congestion is a significant problem in major metropolitan areas in the United States. According to the Urban Mobility Report, in 2019 commuters on average lost about 54 hours in traffic congestion. To combat this, major infrastructure projects have been undertaken. However, expansion projects cannot keep up with the increase in usage of personal vehicles and thus fail to address the traffic congestion problem. Carpool ridesharing has shown some promise in combatting this traffic congestion problem. In this system, the drivers are regular commuters who take detours to pick up and drop off passengers to decrease their transportation costs. This system increases the efficiency of the transportation system by providing flexible commutes to people, thus reducing the need for each commuter to use their own personal vehicle. The researchers developed three approaches to rideshare routing. The researchers conducted a computational study using a San Francisco taxicab dataset to determine the effectiveness of the three approaches. To show the impact of flexible meeting points, the researchers also conducted experimental simulations with and without walking and performed sensitivity analyses. View the NCST Project Webpage
    Keywords: Engineering, Dynamic programming, Mixed integer programming, Origin and destination, Ridesharing, Routing, Travel time, Waiting time
    Date: 2023–03–01
  27. By: Minkyu Shin; Jin Kim; Bas van Opheusden; Thomas L. Griffiths
    Abstract: How will superhuman artificial intelligence (AI) affect human decision making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 years (1950-2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.
    Date: 2023–03
  28. By: Alfredo García; Christopher Hartwell; Martín Andrés Szybisz
    Keywords: COVID-19, social distancing, GDP, Economic Dynamics, Simulation
    JEL: C61 O38
    Date: 2021–11
  29. By: Jonathan Berrisch; Florian Ziel
    Abstract: This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, taking into account dependencies between quantiles and covariates through a smoothing procedure that allows for online learning. Two smoothing methods are discussed: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS learning framework into multivariate dimensions. It is based on Bernstein Online Aggregation (BOA) and yields optimal asymptotic learning properties. We provide an in-depth discussion on possible extensions of the algorithm and several nested cases related to the existing literature on online forecast combination. The methodology is applied to forecasting day-ahead electricity prices, which are 24-dimensional distributional forecasts. The proposed method yields significant improvements over uniform combination in terms of continuous ranked probability score (CRPS). We discuss the temporal evolution of the weights and hyperparameters and present the results of reduced versions of the preferred model. A fast C++ implementation of all discussed methods is provided in the R-Package profoc.
    Date: 2023–03
  30. By: Gregory de Walque (Economics and Research Department, National Bank of Belgium); Thomas Lejeune (Economics and Research Department, National Bank of Belgium); Ansgar Rannenberg (Economics and Research Department, National Bank of Belgium); Magne Mogstad (Economics and Research Department, National Bank of Belgium)
    Abstract: This paper outlines the three-country New Keynesian Dynamic Stochastic General Equilibrium model of the National Bank of Belgium. The model is named BEMGIE for Belgian Economy in a Macro General and International Equilibrium model. It features imperfect market competition, standard real and nominal rigidities, local currency pricing, energy in consumption and oil and foreign inputs in production. The model is estimated using Bayesian econometric techniques on Belgian, euro area and US data. BEMGIE is designed to provide quantitative simulations of macroeconomic shocks and policies, and to be used in the context of the Eurosystem projection exercises.
    Keywords: production networks, endogenous formation, fixed costsDSGE model, Open economy model, Multi-country model, International spillovers, Monetary policy, Exchange rate pass-through, Bayesian estimation.
    JEL: E10 E17 E30 E40 E52 F41 F45 F47 C11 C32 C51
    Date: 2023–03

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