Surveying machine learning methods for economic forecasting.
problem Improving accuracy of economic forecasts using machine learning.
method Nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
result Recent advances in machine learning methods enhance economic forecasting accuracy.
Model predicts real-time job applicant numbers for regional economic analysis.
problem Real-time economic analysis using alternative data.
method Mixed-Frequency Aggregate Learning (MF-AGL) model.
result Model accurately predicts regional labor market conditions and economic status changes.
Proposes a taxonomy for economic policies.
problem Lack of a standardized list of economic policies.
method Develops a tree taxonomy to categorize economic policies.
result Constructs an exhaustive list of economic policies.
Money analyzed as a multidimensional tensor for better economic policy.
problem Economic complexity and policy responsiveness.
method Tensor analysis of money dynamics.
result Enhanced economic policy design and resilience.
LLMs can memorize economic data and recall exact values before their training cutoff.
problem Evaluating the trustworthiness of LLMs' economic forecasts during their training period.
method Demonstrated through counterfactual forecasting and analysis of LLMs' recall ability.
result LLMs have memorized economic and financial data, leading to recall-level accuracy before their knowledge cutoff.
Historical economic growth in countries of the former USSR is analysed. It is shown that Unified Growth Theory is contradicted by the data, which were used, but not analysed, during the formulation of this theory. Unified Growth Theory does not explain the mechanism of economic growth. It explains the mechanism of Malt…
Data describing historical economic growth are analysed. Included in the analysis is the world and regional economic growth. The analysis demonstrates that historical economic growth had a natural tendency to follow hyperbolic distributions. Parameters describing hyperbolic distributions have been determined. A search …
Historical economic growth in Latin America is analysed using the data of Maddison. Unified Growth Theory is found to be contradicted by these data in the same way as it is contradicted by the economic growth in Africa, Asia, former USSR, Western Europe, Eastern Europe and by the world economic growth. Paradoxically, U…
Historical economic growth in Asia (excluding Japan) is analysed. It is shown that Unified Growth Theory is contradicted by the data, which were used (but not analysed) during the formulation of this theory. Unified Growth Theory does not explain the mechanism of economic growth. It explains the mechanism of Malthusian…
New framework detects time-varying economic persistence.
problem Time-varying persistence in economic shocks.
method Localized regression techniques to identify evolving heterogeneity.
result Substantial persistence variations align with macroeconomic events.
AI-driven tax policies improve economic equality and productivity.
problem Lack of appropriate economic data and limited opportunity to experiment.
method Two-level deep reinforcement learning approach to learn dynamic tax policies from observational data.
result AI-driven tax policies improve the trade-off between equality and productivity by 16%.
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
problem Economic scenario analysis using large language models and knowledge graphs.
method Proposing an RAG-based AI economist framework that utilizes knowledge graphs and LLMs.
result Improves economic coherence and traceability in generated reports.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
problem Imperfect measurement of economic outcomes by remotely sensed variables.
method Combines experimental and observational data to identify causal parameters, using satellite imagery and mobile phone activity.
result Developed a robust method for n^{-1/2} inference that does not restrict remotely sensed variable processing algorithms.
Generative Networks outperform traditional methods in PiT ESG generation.
problem Generating economic scenarios quickly and flexibly for sudden changes.
method Comparison of nonparametric, parametric, and generative models.
result Conditional Variational Autoencoder (CVAE) performs best.
Google Trends data improves economic forecasts of private consumption.
problem Improving economic forecasts of private consumption.
method Machine learning techniques applied to categorized Google search data.
result Google data can identify patterns to generate a leading indicator in real time.
The paper analyzes tech specialization and diversification at various scales.
problem Trade-offs between specialization and diversification in economic development.
method Patent data and Economic Complexity framework.
result Technological Coherence positively impacts growth at metropolitan areas but negatively at larger scales.
New framework for interpretable firm characteristics factors.
problem Creating statistically efficient and economically interpretable factors from firm characteristics.
method Grouping related characteristics and deriving one factor per group, combining economic intuition with data-driven clustering.
result Parsimonious, transparent factors outperform benchmarks in out-of-sample tests.
Examines financial risks' impact on EU-15 economic growth.
problem The impact of financial risks on economic growth in EU-15.
method Panel estimated generalized least squares method with additional control variables.
result Financial risks significantly impact economic growth in EU-15.
This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been …
Novel framework analyzes economic shifts in data-poor economies.
problem Data scarcity and unreliability in LMICs hinder accurate assessment of structural transformation.
method Bayesian hierarchical modeling, machine learning, data imputation, factor analysis.
result Framework predicts structural changes even with up to 60% missing data.
Based on the assumption that economic complexity is characterised by the interactions of economic agents (who) constantly change their actions and strategies in response to the outcome they mutually create, this paper presents how network models can be used a proxies for the mapping, quantification and analysis of Roma…
Paper introduces a new method for calibrating ESGs to both historical and forward-looking data.
problem Lack of a generally accepted methodology for calibrating ESGs to forward-looking information.
method Conditional Scenario Simulator framework for consistent calibration of economic and financial variables.
result Framework can embed various financial and macroeconomic models and demonstrate practical examples in frequentist and Bayesian settings.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
problem Investigating data science applications in economics.
method Systematic literature review using Prisma method.
result Hybrid models showed higher prediction accuracy than other algorithms.
DRL enhances economic modeling with deep learning methods.
problem Complex dynamic business environments in economics.
method Comprehensive review of DL, RL, and deep RL methods applied to economics.
result DRL provides better performance and higher accuracy in economic applications.
The study examines tail dependence between global economic uncertainty and BRICS currencies using high-frequency data.
problem Understanding the tail dependence between exchange rates and economic uncertainty.
method Daily Twitter Uncertainty Index and BRICS exchange rates analyzed using time-varying copula framework.
result Indian, Russian, and South African currencies exhibit elliptical copulas, while Brazilian and Chinese currencies show upward trending tail dependence.
A scalable method for econometric inference using machine learning for big data.
problem Interpreting large, often black-box, economic data.
method Variational Bayesian Inference for time-varying parameter auto-regressive models.
result The model can handle large datasets and is scalable for big data.
Current economic theories miss most of economic dynamics.
problem Accuracy of economic theories and policies depend on economic variables and processes.
method Identify and analyze overlooked economic variables and processes.
result Many economic variables and processes not accounted for in current theories.
Cohort analysis speeds up Bitcoin blockchain data queries.
problem Efficiently querying Bitcoin blockchain data for economic insights.
method Cohort analysis applied to Bitcoin transaction data.
result Creation of datasets and visualizations for key Bitcoin transaction indicators.
Proposes a machine learning framework for more efficient economic dispatch.
problem Temporal and spatial correlations between system cost and load prediction errors.
method End-to-end machine learning approach with task-specific learning criteria and an efficient optimization kernel.
result Demonstrates the effectiveness and efficiency of the proposed learning framework.
Peru's abundant natural resources and friendly trade policies has made the country a major economic player in both South America and the global community. Consequently, exports are playing an increasingly important role in Peru's national economy. Indeed, growing from 13.1% as of 1994, exports now contribute approximat…
Quantifying the improvement in human living standard, as well as the city growth in developing countries, is a challenging problem due to the lack of reliable economic data. Therefore, there is a fundamental need for alternate, largely unsupervised, computational methods that can estimate the economic conditions in the…
Study improves stock return prediction by switching between economic states, outperforming traditional methods.
problem Improving stock return prediction across economic regimes.
method State-switching specification using the slope of the yield curve, with an Aligned Economic Index.
result The Aligned Economic Index outperforms traditional predictors, especially during market turbulence.
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
problem Effect of COVID-19 and crude oil prices on US economic policy uncertainty.
method Used ARDL model with daily data from January 21-March 13, 2020.
result Crude oil price dynamics increase US economic policy uncertainty, while COVID-19 cases have mixed effects.
We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on the indexing of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic con…
Analyzes how economic policies affect wealth distribution in Bitcoin token economy.
problem Impact of economic policies on wealth distribution in token economies.
method Eliminated noise in wealth distribution data using macroeconomic and microeconomic time series. Causality analysis between BIPs and wealth distribution data.
result Proposed a structure for economic policy taxonomy in token economies.
I argue that the current financial crisis highlights the crucial need of a change of mindset in economics and financial engineering, that should move away from dogmatic axioms and focus more on data, orders of magnitudes, and plausible, albeit non rigorous, arguments.
In the same way as the Hilbert Program was a response to the foundational crisis of mathematics, this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent fina…
A simple but useful method of reciprocal values is introduced, explained and illustrated. This method simplifies the analysis of hyperbolic distributions, which are causing serious problems in the demographic and economic research. It allows for a unique identification of hyperbolic distributions and for unravelling co…
The optimal approach is to theorize after examining data, not before.
problem Optimal sequencing of theory and empirical analysis for economic questions.
method Formalized a Bayesian model to trade off Darwinian and Statistical Learning.
result Post hoc theorizing is typically optimal in modern economics.
Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers propo…
We develop a complexity measure for large-scale economic systems based on Shannon's concept of entropy. By adopting Leontief's perspective of the production process as a circular flow, we formulate the process as a Markov chain. Then we derive a measure of economic complexity as the average number of bits required to e…
Modeling business cycles via collective risk fluctuations in economic agents' risk space.
problem Understanding and predicting business cycles through economic agents' risk dynamics.
method Continuous numerical risk grades for economic agents, modeling collective economic variables and flows as functions of risk coordinates, deriving equations for their evolution.
result Business and credit cycles are explained as fluctuations of collective economic variables and their mean risks in the risk space of economic agents.
We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual…
The paper uses machine learning to predict the impact of the Ukraine crisis on financial markets.
problem Quantifying the impact of the Ukraine crisis on financial markets.
method Selected economic indexes, created datasets, and used machine learning (Linear Regression) for forecasting.
result The model accurately predicted the effects of the Ukraine crisis on financial markets.
How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite …
The paper argues for a social-economic approach to AI development.
problem The current AI development overlooks social and cultural aspects of intelligence.
method A blend of economic and social concepts with computational and inferential concepts in algorithm design.
result A social-economic approach is necessary for proper AI development.
The paper gives picture of enrichment to economic and financial system analysis using agent-based models as a form of advanced study for financial economic data post-statistical-data analysis and micro-simulation analysis. Theoretical exploration is carried out by using comparisons of some usual financial economy syste…
FinML-Chain integrates blockchain data for financial machine learning.
problem Challenges in financial machine learning, including missing data, lack of transparency, and incompatible data sources.
method Blockchain technology integrated with machine learning techniques to address financial market challenges.
result Framework generates datasets for analyzing economic mechanisms, advancing financial research.