nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒05‒15
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial By Gutierrez-Lythgoe, Antonio
  2. Teletrabajo en Twitter: Análisis mediante Deep Learning By Gutierrez-Lythgoe, Antonio
  3. GDP nowcasting with artificial neural networks: How much does long-term memory matter? By Krist\'of N\'emeth; D\'aniel Hadh\'azi
  4. Quantitative Trading using Deep Q Learning By Soumyadip Sarkar
  5. Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models By Fantazzini, Dean
  6. Parameterized Neural Networks for Finance By Daniel Oeltz; Jan Hamaekers; Kay F. Pilz
  7. Artificial neural networks and time series of counts: A class of nonlinear INGARCH models By Malte Jahn
  8. Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow By Mingyu Hao; Artem Lenskiy
  9. Automated Function Implementation via Conditional Parameterized Quantum Circuits with Applications to Finance By Mark-Oliver Wolf; Tom Ewen; Ivica Turkalj
  10. Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams By Mahsa Tavakoli; Rohitash Chandra; Fengrui Tian; Cristi\'an Bravo
  11. Constrained optimization in Random Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (Revision of CentER DP 2022-022) By Angun, Ebru; Kleijnen, Jack
  12. Artificial Intelligence: Opportunities and Managerial Challenges By Frédéric Marty
  13. Optimal Trading in Automatic Market Makers with Deep Learning By Sebastian Jaimungal; Yuri F. Saporito; Max O. Souza; Yuri Thamsten
  14. From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements By Frédéric Marty
  15. Economic Origins of the Sicilian Mafia: A Simulation Feedback Model By Oleg V. Pavlov; Jason M. Sardell
  16. A North-South agent based model of segmented labour markets. The role of education and trade asymmetries By Lucrezia Fanti; Marcelo C. Pereira; Maria Enrica Virgillito
  17. Neural Design for Genetic Perturbation Experiments By Pacchiano, Aldo; Wulsin, Drausin; Barton, Robert A.; Voloch, Luis
  18. Riview Literatur Dengan Menggunakan ChatGPT By Purnomo, Andronius
  19. AI Knowledge: Improving AI Delegation through Human Enablement By Pinski, Marc; Adam, Martin; Benlian, Alexander
  20. Measuring the Temporal Dimension of Text: An Application to Policymaker Speeches By Byrne, David; Goodhead, Robert; McMahon, Michael; Parle, Conor
  21. From Euclidean Distance to Spatial Classification: Unraveling the Technology behind GPT Models By Alfredo B. Roisenzvit
  22. Constrained optimization in Random Simulation : Efficient Global Optimization and Karush-Kuhn-Tucker Conditions (Revision of CentER DP 2022-022) By Angun, Ebru; Kleijnen, Jack
  23. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models By Alejandro Lopez-Lira; Yuehua Tang
  24. Is There Cross-fertilization in Macroeconomics? A Quantitative Exploration of the Interactions between DSGE and Macro Agent-Based Models By Muriel Dal Pont Legrand; Martina Cioni; Eugenio Petrovich; Alberto Baccini
  25. Simulating Gaussian vectors via randomized dimension reduction and PCA By Nabil Kahale
  26. Egyptian Ratscrew: Discovering Dominant Strategies with Computational Game Theory By Justin Diamond; Ben Garcia
  27. Exploring economic activity from outer space: A Python notebook for processing and analyzing satellite nighttime lights By Carlos Mendez; Ayush Patnaik

  1. By: Gutierrez-Lythgoe, Antonio
    Abstract: The evolution of cities has led to changes in urban mobility patterns, including an increased number of trips, longer and more dispersed routes. Therefore, it is crucial to study urban mobility efficiently to promote sustainability and well-being. In this context, we reviewed the existing literature on the applications of artificial intelligence (AI) in urban mobility research, specifically focusing on Deep Learning techniques such as CNN and LSTM models. These AI tools are being used to address the challenges of urban mobility research and offer new possibilities for tackling the pressing issues faced by cities, such as sustainability in transportation. AI can contribute to improving sustainability by predicting real-time traffic, optimizing transportation efficiency, and informing public policies that promote sustainable modes of transportation. In this study, we propose a Random Forest model for predicting demand for sustainable urban mobility based on machine learning, achieving accurate and consistent predictions. Overall, the application of AI in urban mobility research presents a unique opportunity to advance towards more sustainable, livable cities and resilient societies.
    Keywords: Artificial Intelligence, Urban mobility, Deep Learning, Machine Learning , sustainability
    JEL: C45 C53 Q56 R41 R42
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117103&r=cmp
  2. By: Gutierrez-Lythgoe, Antonio
    Abstract: In this article we analyse Twitter users’ perceptions on remote working. To do so, we use artificial intelligence techniques of natural language processing. Specifically, we run a Sentiment Analysis and Latent Dirichlet Allocation (LDA) on a sample of 12, 986 tweets related to remote working published in Spanish. Our results show that 21.2% of the tweets present a positive sentiment, 43.5% a negative sentiment and 35.3% a neutral connotation. This article contributes to the application of Machine learning and Deep learning techniques in the study of social sciences.
    Keywords: Artificial Intelligence, Sentiment analysis, Big Data, remote working, telework
    JEL: C88 D83 J22 J23
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117101&r=cmp
  3. By: Krist\'of N\'emeth; D\'aniel Hadh\'azi
    Abstract: In our study, we apply different statistical models to nowcast quarterly GDP growth for the US economy. Using the monthly FRED-MD database, we compare the nowcasting performance of the dynamic factor model (DFM) and four artificial neural networks (ANNs): the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU). The empirical analysis presents the results from two distinctively different evaluation periods. The first (2010:Q1 -- 2019:Q4) is characterized by balanced economic growth, while the second (2010:Q1 -- 2022:Q3) also includes periods of the COVID-19 recession. According to our results, longer input sequences result in more accurate nowcasts in periods of balanced economic growth. However, this effect ceases above a relatively low threshold value of around six quarters (eighteen months). During periods of economic turbulence (e.g., during the COVID-19 recession), longer training sequences do not help the models' predictive performance; instead, they seem to weaken their generalization capability. Our results show that 1D CNN, with the same parameters, generates accurate nowcasts in both of our evaluation periods. Consequently, first in the literature, we propose the use of this specific neural network architecture for economic nowcasting.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.05805&r=cmp
  4. By: Soumyadip Sarkar
    Abstract: Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. This paper explores the use of RL in quantitative trading and presents a case study of a RL-based trading algorithm. The results show that RL can be a powerful tool for quantitative trading, and that it has the potential to outperform traditional trading algorithms. The use of reinforcement learning in quantitative trading represents a promising area of research that can potentially lead to the development of more sophisticated and effective trading systems. Future work could explore the use of alternative reinforcement learning algorithms, incorporate additional data sources, and test the system on different asset classes. Overall, our research demonstrates the potential of using reinforcement learning in quantitative trading and highlights the importance of continued research and development in this area. By developing more sophisticated and effective trading systems, we can potentially improve the efficiency of financial markets and generate greater returns for investors.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.06037&r=cmp
  5. By: Fantazzini, Dean
    Abstract: In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of ``dead coins'' and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022.
    Keywords: daily range; bitcoin; crypto-assets; cryptocurrencies; credit risk; default probability; probability of death; ZPP; cauchit; random forests
    JEL: C32 C35 C51 C53 C58 G12 G17 G32 G33
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117141&r=cmp
  6. By: Daniel Oeltz; Jan Hamaekers; Kay F. Pilz
    Abstract: We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specific data sample. In this sense, it may help to reduce the overfitting problem, since, after learning the model class over a larger data sample consisting of such different data sets, just a few parameters need to be adjusted for modeling a new, specific problem. After analyzing the method theoretically and by regression examples for different one-dimensional problems, we finally apply the approach to one of the standard problems asset managers and banks are facing: the calibration of spread curves. The presented results clearly show the potential that lies within this method. Furthermore, this application is of particular interest to financial practitioners, since nearly all asset managers and banks which are having solutions in place may need to adapt or even change their current methodologies when ESG ratings additionally affect the bond spreads.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.08883&r=cmp
  7. By: Malte Jahn
    Abstract: Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.01025&r=cmp
  8. By: Mingyu Hao; Artem Lenskiy
    Abstract: As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.02472&r=cmp
  9. By: Mark-Oliver Wolf; Tom Ewen; Ivica Turkalj
    Abstract: Classical Monte Carlo algorithms can theoretically be sped up on a quantum computer by employing amplitude estimation (AE). To realize this, an efficient implementation of state-dependent functions is crucial. We develop a straightforward approach based on pre-training parameterized quantum circuits, and show how they can be transformed into their conditional variant, making them usable as a subroutine in an AE algorithm. To identify a suitable circuit, we propose a genetic optimization approach that combines variable ansatzes and data encoding. We apply our algorithm to the problem of pricing financial derivatives. At the expense of a costly pre-training process, this results in a quantum circuit implementing the derivatives' payoff function more efficiently than previously existing quantum algorithms. In particular, we compare the performance for European vanilla and basket options.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.08793&r=cmp
  10. By: Mahsa Tavakoli; Rohitash Chandra; Fengrui Tian; Cristi\'an Bravo
    Abstract: Knowing which factors are significant in credit rating assignment leads to better decision-making. However, the focus of the literature thus far has been mostly on structured data, and fewer studies have addressed unstructured or multi-modal datasets. In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types. In these models, we tested different combinations of fusion strategies with different deep learning models, including CNN, LSTM, GRU, and BERT. We studied data fusion strategies in terms of level (including early and intermediate fusion) and techniques (including concatenation and cross-attention). Our results show that a CNN-based multi-modal model with two fusion strategies outperformed other multi-modal techniques. In addition, by comparing simple architectures with more complex ones, we found that more sophisticated deep learning models do not necessarily produce the highest performance; however, if attention-based models are producing the best results, cross-attention is necessary as a fusion strategy. Finally, our comparison of rating agencies on short-, medium-, and long-term performance shows that Moody's credit ratings outperform those of other agencies like Standard & Poor's and Fitch Ratings.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.10740&r=cmp
  11. By: Angun, Ebru; Kleijnen, Jack (Tilburg University, Center For Economic Research)
    Keywords: Simulation; design of experiments; simulation; statistical analysis; artificial intelligence; computational experiments; inventory-production
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:tiu:tiucen:ae1ec947-12e1-4bf7-89fb-7ccefb61c6f1&r=cmp
  12. By: Frédéric Marty (Université Côte d'Azur, France; GREDEG CNRS)
    Abstract: While the use of artificial intelligence for pricing, search or matching algorithms generates efficiency gains that primarily benefit consumers, firms must be aware that these algorithms can generate situations of non-compliance with competition and consumer protection rules, and that they can expose them to significant reputational risks if their results are perceived as restricting or manipulating consumer choices or even as leading to discriminatory practices. This contribution aims to characterize these risks and insists on the need for companies to implement compliance policies to prevent these damages or to put an end to them quickly and efficiently through algorithmic audits.
    Keywords: algorithms, artificial intelligence, consumer manipulation, anticompetitive practices, compliance programmes, algorithmic audits
    JEL: K21 K13
    Date: 2022–07
    URL: http://d.repec.org/n?u=RePEc:gre:wpaper:2022-23&r=cmp
  13. By: Sebastian Jaimungal; Yuri F. Saporito; Max O. Souza; Yuri Thamsten
    Abstract: This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms na\"ive strategies.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.02180&r=cmp
  14. By: Frédéric Marty (Université Côte d'Azur, France; GREDEG CNRS)
    Abstract: This contribution considers how anti-competitive agreements can be impacted by algorithms, especially those that use artificial intelligence, and by the use of blockchains. In both cases, the aim is to analyze how these technical devices can contribute to the consolidation of agreements, how they can hinder the supervision exercised by -competition authorities and the effectiveness of their tools to unravel cartels, and finally how they can be used to augment this oversight.
    Keywords: algorithms, blockchains, cartels, collusive agreements, leniency programs, facilitating practices
    JEL: K21 L41 L42
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:gre:wpaper:2022-32&r=cmp
  15. By: Oleg V. Pavlov; Jason M. Sardell
    Abstract: This chapter develops a feedback economic model that explains the rise of the Sicilian mafia in the 19th century. Grounded in economic theory, the model incorporates causal relationships between the mafia activities, predation, law enforcement, and the profitability of local businesses. Using computational experiments with the model, we explore how different factors and feedback effects impact the mafia activity levels. The model explains important historical observations such as the emergence of the mafia in wealthier regions and its absence in the poorer districts despite the greater levels of banditry.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.07975&r=cmp
  16. By: Lucrezia Fanti; Marcelo C. Pereira; Maria Enrica Virgillito
    Abstract: Drawing on the labour-augmented K+S agent-based model, this paper develops a two-country North-South ABM wherein the leader and the laggard country interact through the international trade of capital goods. The model aims to address sources of asymmetries and possible converge patterns between two advanced economies that are initially differentiated in terms of the education level they are able to provide. Education is modeled as a national-level policy differently targeting the three usual levels, that is primary, secondary and tertiary. After being educated and entering the labour force, workers face a segmented market, divided into three types of job qualification, and the resulting position levels inside firms, i.e., elementary, technical and professional occupations. The three resulting labour market segments are heterogeneous in terms of both requested education level and offered wages. To address the role of trade and education, we experiment with different education-policy and trade settings. Ultimately, we are interested in understanding the coupling effects of asymmetries in education, which reverberate in segmented labour markets and differentiated growth patterns. Notably, our focus on capital-goods trade, rather than consumption goods, allows us to assess a direct link between productive capabilities in producing complex products and country growth prospects.
    Keywords: Agent-Based Model; Education; International Trade; Technology Gap; Labour Market.
    Date: 2023–04–27
    URL: http://d.repec.org/n?u=RePEc:ssa:lemwps:2023/17&r=cmp
  17. By: Pacchiano, Aldo (Microsoft Research NYC); Wulsin, Drausin (Immunai); Barton, Robert A. (Immunai); Voloch, Luis
    Abstract: The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genetic edits (perturbations) or combinations thereof is infeasible due to cost and experimental limitations. This work provides a theoretically sound framework for iteratively exploring the space of perturbations in pooled batches in order to maximize a target phenotype under an experimental budget. Inspired by this application domain, we study the problem of batch query bandit optimization and introduce the Optimistic Arm Elimination (OAE) principle designed to find an almost optimal arm under different functional relationships between the queries (arms) and the outputs (rewards). We analyze the convergence properties of OAE by relating it to the Eluder dimension of the algorithm’s function class and validate that OAE outperforms other strategies in finding optimal actions in experiments on simulated problems, public datasets well-studied in bandit contexts, and in genetic perturbation datasets when the regression model is a deep neural network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in the GeneDisco experimental planning challenge.
    Date: 2023–02
    URL: http://d.repec.org/n?u=RePEc:ecl:stabus:4087&r=cmp
  18. By: Purnomo, Andronius
    Abstract: Penulisan artikel ini bertujuan untuk memanfaatkan kecanggihan tekonologi artificial intelligence (AI) dalam melakukan riview jurnal. Pada artikel ini, Jurnal-jurnal yang akan di riview adalah jurnal dalam 3 tahun terakhir (2020-2022) dengan topik finance yang ditulis oleh Wijaya dan Levine. Artikel ini akan membahas hasil temuan tentang ilmu keuangan, tata kelola perusahaan, pasar modal, dan kinerja perusahaan yang terdaftar di BEI. Tetapi perlu disadari bahwa riview yang dilakukan AI tidak sepenuhnya akurat dan perlu adanya penelusuan secara mandiri dalam melakukan riview.
    Date: 2023–04–06
    URL: http://d.repec.org/n?u=RePEc:osf:osfxxx:fhr52&r=cmp
  19. By: Pinski, Marc; Adam, Martin; Benlian, Alexander
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:137544&r=cmp
  20. By: Byrne, David (Central Bank of Ireland); Goodhead, Robert (Central Bank of Ireland); McMahon, Michael (University of Oxford); Parle, Conor (European Central Bank and Trinity College Dublin)
    Abstract: Discussions of time are central to many questions in the social sciences and to official announcements of policy. Despite the growing popularity of applying Natural Language Processing (NLP) techniques to social science research questions, before now there have been few attempts to measure expressions of time. This paper provides a methodology to measure the “third T of Text”: the Time dimension. We also survey the techniques used to measure the other Ts, namely Topic and Tone. We document key stylised facts relating to temporal information in a corpus of policymaker speeches.
    Keywords: Textual analysis, Machine Learning, Communication.
    JEL: C55 C80 E58
    Date: 2023–02
    URL: http://d.repec.org/n?u=RePEc:cbi:wpaper:2/rt/23&r=cmp
  21. By: Alfredo B. Roisenzvit
    Abstract: In this paper, we present a comprehensive analysis of the technology underpinning Generative Pre-trained Transformer (GPT) models, with a particular emphasis on the interrelationships between Euclidean distance, spatial classification, and the functioning of GPT models. Our investigation begins with a thorough examination of Euclidean distance, elucidating its role as a fundamental metric for quantifying the proximity between points in a multi-dimensional space. Following this, we provide an overview of spatial classification techniques, explicating their utility in discerning patterns and relationships within complex data structures. With this foundation, we delve into the inner workings of GPT models, outlining their architectural components, such as the self-attention mechanism and positional encoding. We then explore the process of training GPT models, detailing the significance of tokenization and embeddings. Additionally, we scrutinize the role of Euclidean distance and spatial classification in enabling GPT models to effectively process input sequences and generate coherent output in a wide array of natural language processing tasks. Ultimately, this paper aims to provide a comprehensive understanding of the intricate connections between Euclidean distance, spatial classification, and GPT models, fostering a deeper appreciation of their collective impact on the advancements in artificial intelligence and natural language processing.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:cem:doctra:853&r=cmp
  22. By: Angun, Ebru; Kleijnen, Jack (Tilburg University, School of Economics and Management)
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:tiu:tiutis:ae1ec947-12e1-4bf7-89fb-7ccefb61c6f1&r=cmp
  23. By: Alejandro Lopez-Lira; Yuehua Tang
    Abstract: We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ``ChatGPT scores'' and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.07619&r=cmp
  24. By: Muriel Dal Pont Legrand (Université Côte d'Azur, CNRS, GREDEG, France); Martina Cioni (Università degli Studi di Siena); Eugenio Petrovich (Università degli Studi di Siena); Alberto Baccini (Università degli Studi di Siena)
    Abstract: This paper compares Dynamic Stochastic General Equilibrium (DSGE) and Macro Agent-Based Models (MABMs) by adopting mainly a distant reading perspective. A set of 2, 299 papers is retrieved from Scopus by using keywords related to MABM and DSGE domains. The interactions between the two streams of DSGE and MABM literature are explored by considering a social axis (co-authorship network), and an intellectual axis (cited references and bibliographic coupling). The analysis gave results that are neither consistent with a unitary structure of macroeconomics, nor with a simple dichotomic structure of alternative paradigms and separated academics communities. Indeed, the co-authorship network shows that DSGE and MABM form fragmented communities still belonging to two different larger MABM and DSGE communities rather neatly separated. Collaboration insists mainly inside the smaller groups and inside each of the two larger DSGE and MABM communities. Moreover, the co-authorship network analysis does not show evidence of systematic collaboration between MABM and DSGE authors. From an intellectual point of view, data show that DSGE and MABM articles refer to two different sets of bibliographic references. When a measure of paper-similarity is adopted, it appears that DSGE literature is fragmented in 4 groups while the MABM articles are clustered together in a unique group. Hence, DSGE approach is less monolithic than at the time of the New Synthesis: indeed, a large and a growing literature has developed at the margins of the core DSGE approach which includes elements of heterogeneous agent modelling, social interactions, experiments, expectations formation, learning etc. The analysis gave no evidence of cross-fertilization between DSGE and MABM literature whilst it rather suggests a totally dissymmetric influence of DSGE over MABM literature, i.e., only MABM modelers look at DSGE but not vice-versa. The paper questions the capacity of the current dominant approach to benefit from cross-fertilization.
    Keywords: Macroeconomics, DSGE, macro agent-based models, heterogeneity, New Synthesis, cross-fertilization, hybrid models, co-authorship network, co-citation analysis, bibliographic coupling, paper similarity
    Date: 2022–07
    URL: http://d.repec.org/n?u=RePEc:gre:wpaper:2022-25&r=cmp
  25. By: Nabil Kahale
    Abstract: We study the problem of estimating E(g(X)), where g is a real-valued function of d variables and X is a d-dimensional Gaussian vector with a given covariance matrix. We present a new unbiased estimator for E(g(X)) that combines the randomized dimension reduction technique with principal components analysis. Under suitable conditions, we prove that our algorithm outperforms the standard Monte Carlo method by a factor of order d.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.07377&r=cmp
  26. By: Justin Diamond; Ben Garcia
    Abstract: "Egyptian Ratscrew" (ERS) is a modern American card game enjoyed by millions of players worldwide. A game of ERS is won by collecting all of the cards in the deck. Typically this game is won by the player with the fastest reflexes, since the most common strategy for collecting cards is being the first to slap the pile in the center whenever legal combinations of cards are placed down. Most players assume that the dominant strategy is to develop a faster reaction time than your opponents, and no academic inquiry has been levied against this assumption. This thesis investigates the hypothesis that a "risk slapping" strategist who relies on practical economic decision making will win an overwhelming majority of games against players who rely on quick reflexes alone. It is theorized that this can be done by exploiting the "burn rule, " a penalty that is too low-cost to effectively dissuade players from slapping illegally when it benefits them. Using the Ruby programming language, we construct an Egyptian Ratscrew simulator from scratch. Our model allows us to simulate the behavior of 8 strategically unique players within easily adjustable parameters including simulation type, player count, and burn amount. We simulate 100k iterations of 67 different ERS games, totaling 6.7 million games of ERS, and use win percentage data in order to determine which strategies are dominant under each set of parameters. We then confirm our hypothesis that risk slapping is a dominant strategy, discover that there is no strictly dominant approach to risk slapping, and elucidate a deeper understanding of different ERS mechanics such as the burn rule. Finally, we assess the implications of our findings and suggest potential improvements to the rules of the game. We also touch on the real-world applications of our research and make recommendations for the future of Egyptian Ratscrew modeling.
    Date: 2023–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.01007&r=cmp
  27. By: Carlos Mendez (Nagoya University); Ayush Patnaik (xKDR Forum)
    Abstract: Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.
    JEL: Y9
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:anf:wpaper:21&r=cmp

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