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on Computational Economics |
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Issue of 2024‒02‒05
seventeen papers chosen by |
| By: | Zhouzhou Gu (Princeton University); Mathieu Laurière (NYU Shanghai, NYU-ECNU Institute of Mathematical Sciences); Sebastian Merkel (University of Exeter); Jonathan Payne (Princeton University) |
| Abstract: | We propose a new global solution algorithm for continuous time heterogeneous agent economies with aggregate shocks. First, we approximate the state space so that equilibrium in the economy can be characterized by one high, but finite, dimensional partial differential equation. Second, we approximate the value function using neural networks and solve the differential equation using deep learning tools. We refer to the solution as an Economic Model Informed Neural Network (EMINN). The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We demonstrate our algorithm by solving two canonical models in the macroeconomics literature: the Aiyagari (1994) model and the Krusell and Smith (1998) model. |
| Keywords: | Heterogeneous agents, computational methods, deep learning, inequality, mean field games, continuous time methods, aggregate shocks, global solution |
| JEL: | C70 |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:pri:econom:2023-19&r=cmp |
| By: | Ramaharo, Franck M.; Rasolofomanana, Gerzhino H. |
| Abstract: | We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1-2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making. |
| Keywords: | nowcasting; gross domestic product; machine learning; Madagascar |
| JEL: | C02 C53 C63 E17 |
| Date: | 2023–12–23 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119574&r=cmp |
| By: | Bingduo Yang (School of Finance, Guangdong University of Finance and Economics, Guangzhou 510320, China); Wei Long (Department of Economics, Tulane University, New Orleans, LA 70118, USA); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA) |
| Abstract: | We examine nonparametric panel data regression models with fixed effects and cross-sectional dependence through a diverse collection of machine learning techniques. We add cross-sectional averages and time averages as regressors to the model to account for unobserved common factors and fixed effects respectively. Additionally, we utilize the debiased machine learning method by Chernozhukov et al. (2018) to estimate parametric coefficients followed by the nonparametric component. We comprehensively investigate three commonly used machine learning techniques - LASSO, random forests, and neural network - in finite samples. Simulation results demonstrate the effectiveness of our proposed method across different combinations of the number of cross-sectional units, time dimension sample size, and the number of regressors, irrespective of the presence of fixed effects and cross-sectional dependence. In the empirical part, we employ the proposed machine learning-based panel data model to estimate the total factor productivity (TFP) of public companies of Chinese mainland and find that the proposed machine learning methods are comparable to other competitive methods. |
| Keywords: | Machine learning; panel data model; cross-sectional dependence; debiased machine learning. |
| JEL: | C12 C22 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202402&r=cmp |
| By: | Georgios Fatouros; Konstantinos Metaxas; John Soldatos; Dimosthenis Kyriazis |
| Abstract: | In the dynamic and data-driven landscape of financial markets, this paper introduces MarketSenseAI, a novel AI-driven framework leveraging the advanced reasoning capabilities of GPT-4 for scalable stock selection. MarketSenseAI incorporates Chain of Thought and In-Context Learning methodologies to analyze a wide array of data sources, including market price dynamics, financial news, company fundamentals, and macroeconomic reports emulating the decision making process of prominent financial investment teams. The development, implementation, and empirical validation of MarketSenseAI are detailed, with a focus on its ability to provide actionable investment signals (buy, hold, sell) backed by cogent explanations. A notable aspect of this study is the use of GPT-4 not only as a predictive tool but also as an evaluator, revealing the significant impact of the AI-generated explanations on the reliability and acceptance of the suggested investment signals. In an extensive empirical evaluation with S&P 100 stocks, MarketSenseAI outperformed the benchmark index by 13%, achieving returns up to 40%, while maintaining a risk profile comparable to the market. These results demonstrate the efficacy of Large Language Models in complex financial decision-making and mark a significant advancement in the integration of AI into financial analysis and investment strategies. This research contributes to the financial AI field, presenting an innovative approach and underscoring the transformative potential of AI in revolutionizing traditional financial analysis investment methodologies. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.03737&r=cmp |
| By: | Shovon Sengupta; Tanujit Chakraborty; Sunny Kumar Singh |
| Abstract: | Forecasting a key macroeconomic variable, consumer price index (CPI) inflation, for BRIC countries using economic policy uncertainty and geopolitical risk is a difficult proposition for policymakers at the central banks. This study proposes a novel filtered ensemble wavelet neural network (FEWNet) that can produce reliable long-term forecasts for CPI inflation. The proposal applies a maximum overlapping discrete wavelet transform to the CPI inflation series to obtain high-frequency and low-frequency signals. All the wavelet-transformed series and filtered exogenous variables are fed into downstream autoregressive neural networks to make the final ensemble forecast. Theoretically, we show that FEWNet reduces the empirical risk compared to single, fully connected neural networks. We also demonstrate that the rolling-window real-time forecasts obtained from the proposed algorithm are significantly more accurate than benchmark forecasting methods. Additionally, we use conformal prediction intervals to quantify the uncertainty associated with the forecasts generated by the proposed approach. The excellent performance of FEWNet can be attributed to its capacity to effectively capture non-linearities and long-range dependencies in the data through its adaptable architecture. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.00249&r=cmp |
| By: | Patel, Rajesh; Khan, Fatima; Silva, Buddhika; Shaturaev, Jakhongir |
| Abstract: | This research paper examines the impact of Artificial Intelligence (AI) on the financial audit process and explores how it enhances auditing practices. The integration of AI technology in financial audits has the potential to revolutionize the profession by automating tasks, providing real-time analysis, enhancing risk assessment capabilities, and offering valuable insights. This research investigates the implications, benefits, challenges, and ethical considerations associated with AI integration in the audit process. The literature review reveals that AI improves audit efficiency by automating manual processes and reducing the time required for data analysis. AI-powered tools enable real-time analysis, enhancing risk assessment by detecting anomalies and potential fraud indicators promptly. AI algorithms also contribute to more accurate and informed decision-making by analyzing complex datasets and identifying patterns. Ethical considerations, such as fairness, transparency, and unbiased decision-making, must be addressed when integrating AI technology into audits. Based on the literature review, hypotheses are developed to test the relationships between AI and audit efficiency, risk assessment, audit quality, and decision-making. These hypotheses propose that AI integration improves audit efficiency, enhances risk assessment capabilities, facilitates more informed decision-making, and requires ethical considerations and collaboration with IT professionals for successful implementation. The findings and discussion emphasize that AI technology has significant potential implications for audit quality, efficiency, risk assessment, and decision-making. By leveraging AI's analytical capabilities, auditors can improve audit quality, proactively address risks, and make more accurate decisions. However, further empirical research is needed to validate these findings and address ethical considerations. Future research should focus on the long-term effects of AI on audit quality, explore ethical frameworks for AI integration, and examine auditors' technological skills and collaboration with IT professionals. |
| Keywords: | Artificial Intelligence; Audit; Transparency; Fraud; Financial Accounting |
| JEL: | M4 M42 M48 O3 O33 |
| Date: | 2023–08–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119616&r=cmp |
| By: | David E., Bloom; Prettner, Klaus; Saadaoui, Jamel; Veruete, Mario |
| Abstract: | What will likely be the effect of the emergence of ChatGPT and other forms of artificial intelligence (AI) on the skill premium? To address this question, we develop a nested constant elasticity of substitution production function that distinguishes between industrial robots and AI. Industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers. |
| Keywords: | Automation; Artificial Intelligence; ChatGPT; Skill Premium; Wages; Productivity |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus005:59342195&r=cmp |
| By: | Giraldo, Iader (FLAR); Giraldo, Iader (FLAR); Gomez-Gonzalez, Jose E (Department of Finance, Information Systems, and Economics, City University of New York – Lehman College, Bronx); Uribe, Jorge M (Faculty of Economics and Business, Universitat Oberta de Catalunya) |
| Abstract: | In this study, we revisit the debate regarding the effects of financial openness on financial stability. In contrast to previous studies, our approach involves measuring the direct influences of openness on stability through a varied set of proxies used to capture the diverse dimensions of both of these concepts within a unified estimation framework. Employing state-of-the-art machine learning techniques, our estimates enable us to isolate the focal effects while controlling for a comprehensive set of macroeconomic, political, and institutional variables. Covering the period spanning 2010 to 2020 across 45 countries, our results indicate that, in the majority of cases, increased financial openness is beneficial for financial stability. Greater levels of integration tends to reduce the ratio of nonperforming loans to total loans, concurrently improving capital adequacy ratios and the ratio of provisions to nonperforming loans. Additionally, heightened openness leads to an increase in the levels of bank liquidity. Importantly, these enhancements to financial stability occur without any adverse effects on bank profitability. This suggests that policies aimed at fostering greater integration with global financial markets and promoting increased bank competition can exert positive impacts on financial stability without compromising bank profitability. |
| Keywords: | Openness; integration; Financial stability; Double-Debiased Machine Learning |
| JEL: | F21 F32 G21 G28 |
| Date: | 2024–01–17 |
| URL: | http://d.repec.org/n?u=RePEc:col:000566:020926&r=cmp |
| By: | Shivam Gupta (NEOMA - Neoma Business School); Sachin Modgil (IMI Kolkata - International Management Institute); Ajay Kumar (EM - emlyon business school); Uthayasankar Sivarajah (University of Bradford); Zahir Irani (University of Bradford) |
| Abstract: | "Natural disasters are often unpredictable and therefore there is a need for quick and effective response to save lives and infrastructure. Hence, this study is aimed at achieving timely, anticipated and effective response throughout the cycle of a disaster, extreme weather and emergency operations management with the help of advanced technologies. This study proposes a novel, evidence-based framework (4-AIDE) that highlights the role of artificial intelligence (AI) and cloud-based collaborative platforms in disaster, extreme weather and emergency situations. A qualitative approach underpinned by organizational information processing theory (OIPT) is employed to design, develop and conduct semi-structured interviews with 33 respondents having experience in AI and cloud computing industries during emergency and extreme weather situations. For analysing the collected data, axial, open and selective coding is used that further develop themes, propositions and an evidence-based framework. The study findings indicate that AI and cloud-based collaborative platforms offer a structured and logical approach to enable two-way, algorithm-based communication to collect, analyse and design effective management strategies for disaster and extreme weather situations. Managers of public systems or businesses can collect and analyse data to predict possible outcomes and take necessary actions in an extreme weather situation. Communities and societies can be more resilient by transmitting and receiving data to AI and cloud-based collaborative platforms. These actions can also help policymakers identify critical pockets and guide administration for their necessary preparation for unexpected, extreme weather, and emergency events." |
| Keywords: | Artificial intelligence, Cloud technologies, Disaster management, Extreme weather, Organizational information processing theory |
| Date: | 2022–09–22 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04325638&r=cmp |
| By: | David E. Bloom (Harvard TH Chan School of Public Health); Klaus Prettner (Department of Economics, Vienna University of Economics and Business); Jamel Saadaoui (University of Strasbourg); Mario Veruete (Quantum DataLab) |
| Abstract: | What will likely be the effect of the emergence of ChatGPT and other forms of artificial intelligence (AI) on the skill premium? To address this question, we develop a nested constant elasticity of substitution production function that distinguishes between industrial robots and AI. Industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers. |
| Keywords: | Automation, Artificial Intelligence, ChatGPT, Skill Premium, Wages, Productivity |
| JEL: | J30 O14 O15 O33 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp353&r=cmp |
| By: | Jules H. van Binsbergen; Svetlana Bryzgalova; Mayukh Mukhopadhyay; Varun Sharma |
| Abstract: | Using text from 200 million pages of 13, 000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our measure predicts GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors, as well as monetary policy decisions. Our measure is distinct from the information in expert forecasts and leads its consensus value. Interestingly, news coverage has become increasingly negative across all states in the past half-century. |
| JEL: | E2 E3 E4 E40 E43 E44 G01 G1 G10 G14 G17 G18 G40 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32026&r=cmp |
| By: | Yu, Chen |
| Abstract: | The article "AI Revolution: Reshaping Global Value Chains for the Future" explores the transformative impact of artificial intelligence (AI) on global value chains (GVCs). It provides an in-depth analysis of the current landscape of traditional GVCs, the role of AI in reshaping value chains, implications and challenges arising from AI adoption, and future outlook and predictions. The article emphasizes the importance of adaptability, innovation, and responsible AI adoption in navigating the evolving landscape of AI-driven value chains. |
| Date: | 2023–12–29 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:n6hb2&r=cmp |
| By: | Yu, Chen |
| Abstract: | The advent of artificial intelligence (AI) has ushered in a new epoch in the realm of economic governance, challenging the traditional balance between government intervention (the visible hand) and market forces (the invisible hand). This article delves into the transformative potential of AI in reshaping economic policy and market dynamics. It begins by elucidating the historical roles of the visible and invisible hands in economic theory, setting the stage for an exploration of AI's burgeoning influence. The potential impacts of AI on central planning and resource allocation are examined, highlighting the opportunities for enhanced decision-making and the challenges posed by privacy concerns and automation bias. In the market sphere, AI's effect on consumer behavior, competition, and price mechanisms is scrutinized, alongside ethical considerations and the need for robust regulatory frameworks. The article then navigates the convergence of AI with economic governance, advocating for a balanced approach to government intervention and market freedom. Policy implications are discussed, proposing strategies for governments to leverage AI while upholding economic principles. The future outlook section anticipates AI's trajectory in economic decision-making and offers recommendations for stakeholders. The article concludes by emphasizing the importance of a responsible and informed engagement with AI in economic policymaking, calling for collaborative efforts to ensure AI's ethical integration into economic systems. |
| Date: | 2024–01–03 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:tvchw&r=cmp |
| By: | De Petris, Caterina; Drechsler, Martin |
| Abstract: | Payments for Ecosystem Services (PES) are an incentive-based policy instrument encouraging landowners to adopt conservation practices that enhance ecosystem services in exchange for a compensation payment. PES schemes vary considerably in their design, yielding important implications for their conservation outcome and their cost-effectiveness. Given that a landowner’s probability of re-enrolling in a PES scheme is significantly influenced by social norms, this article explores whether the cost-effectiveness of PES schemes could be increased by leveraging on social norms. In particular, we explore whether designing dynamic PES schemes in which a homogenous PES payment is reduced in subsequent contracts would be more cost-effective than static schemes under the assumption that some landowners will enrol or re-enrol in the scheme encouraged by the behaviours of neighbouring landowners. We analyse whether, by initially setting a high payment so as to build a partially conserved landscape, it would be possible to leverage on social norms and reduce the PES payment without losing much conservation engagement. For this purpose, a conceptual agent-based simulation model entailing social norms and bounded rationality as well as other-regarding preferences has been developed. |
| Keywords: | Payment for Ecosystem Services (PES); agri-environment schemes (AES); social norms; bounded rationality; ecological-economic modelling; agent-based modelling (ABM) |
| JEL: | C6 Q57 Q58 |
| Date: | 2023–12–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:119534&r=cmp |
| By: | Mauro Cazzaniga; Ms. Florence Jaumotte; Longji Li; Mr. Giovanni Melina; Augustus J Panton; Carlo Pizzinelli; Emma J Rockall; Ms. Marina Mendes Tavares |
| Abstract: | Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills |
| Keywords: | Artificial Intelligence; Labor Market; Job Displacement; Income Inequality; Advanced Economies; Emerging Market Economies; Low-Income Developing Countries; AI preparedness index; AI benefit; AI exposure; ICT employment share; AI adoption; Emerging and frontier financial markets; Income; Global; Africa |
| Date: | 2024–01–14 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfsdn:2024/001&r=cmp |
| By: | Yue Fang; Junyi Liu; Jong-Shi Pang |
| Abstract: | This paper proposes a Heaviside composite optimization approach and presents a progressive (mixed) integer programming (PIP) method for solving multi-class classification and multi-action treatment problems with constraints. A Heaviside composite function is a composite of a Heaviside function (i.e., the indicator function of either the open $( \, 0, \infty )$ or closed $[ \, 0, \infty \, )$ interval) with a possibly nondifferentiable function. Modeling-wise, we show how Heaviside composite optimization provides a unified formulation for learning the optimal multi-class classification and multi-action treatment rules, subject to rule-dependent constraints stipulating a variety of domain restrictions. A Heaviside composite function has an equivalent discrete formulation %in terms of integer variables, and the resulting optimization problem can in principle be solved by integer programming (IP) methods. Nevertheless, for constrained learning problems with large data sets, %of modest or large sizes, a straightforward application of off-the-shelf IP solvers is usually ineffective in achieving global optimality. To alleviate such a computational burden, our major contribution is the proposal of the PIP method by leveraging the effectiveness of state-of-the-art IP solvers for problems of modest sizes. We provide the theoretical advantage of the PIP method with the connection to continuous optimization and show that the computed solution is locally optimal for a broad class of Heaviside composite optimization problems. The numerical performance of the PIP method is demonstrated by extensive computational experimentation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.01565&r=cmp |
| By: | Vahidin Jeleskovic; Stephen Mackay |
| Abstract: | Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.00603&r=cmp |