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Economies
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Next-Generation Macroeconomics: Data-Driven and Artificial Intelligence Approaches
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Next-Generation Macroeconomics: Data-Driven and Artificial Intelligence Approaches

A special issue of Economies (ISSN 2227-7099). This special issue belongs to the section "Economic Development".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1386

Editor

Dr. Carlos Poza

E-Mail Website
Guest Editor
Faculty of Law, Business and Governance, Universidad Francisco de Vitoria, 28223 Madrid, Spain
Interests: applied economics; economic analysis; statistical analysis; applied econometrics; strategy; descriptive statistics; economic growth; economics analysis

Special Issue Information

Dear Colleagues,

The increasing availability of high-frequency and unconventional datasets, together with advanced computational tools, offers unprecedented opportunities to improve forecasting, modeling, and the evaluation of economic policy. Given this background, this Special Issue of Economies explores how big data, machine learning, and artificial intelligence are reshaping macroeconomic analysis and policy. We invite submissions that apply or critically assess data-driven and AI-based approaches to macroeconomic phenomena, including growth, inflation, employment, interest rates, trade balances, and systemic risk. Contributions may also focus on hybrid frameworks combining structural models (e.g., DSGE, VAR) with AI methods, or address methodological and ethical challenges such as data quality, algorithmic bias, and transparency. We welcome a broad range of contributions, including empirical research, methodological studies, review articles, policy-oriented papers, and technical notes. Our aim is to provide a platform for advancing next-generation macroeconomic research at the intersection of economics, data science, and public policy.

Dr. Carlos Poza
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Economies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • macroeconomic analysis and policy
  • inflation
  • employment
  • interest rates
  • trade balances
  • systemic risk

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Published Papers (2 papers)

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Research

29 pages, 4697 KB  
Article
Vehicle Registrations as a Leading Indicator of the Spanish Business Cycle: Machine Learning and Classical Forecasting Approaches
by Rodrigo Gragera, Rufino Prieto and Carlos Poza
Economies 2026, 14(8), 330; https://doi.org/10.3390/economies14080330 - 9 Aug 2026
Viewed by 288
Abstract
Timely identification of business-cycle turning points is essential for policymakers, firms and investors, yet traditional macroeconomic indicators are often published with considerable delays. This study examines whether vehicle registrations constitute an effective Leading Economic Indicator (LEI) for the Spanish economy and proposes an [...] Read more.
Timely identification of business-cycle turning points is essential for policymakers, firms and investors, yet traditional macroeconomic indicators are often published with considerable delays. This study examines whether vehicle registrations constitute an effective Leading Economic Indicator (LEI) for the Spanish economy and proposes an integrated framework combining business-cycle analysis, monthly forecasting and daily nowcasting. The cyclical properties of registrations are analyzed using the Christiano–Fitzgerald band-pass filter, cross-correlation analysis and the Bry–Boschan dating algorithm. Monthly forecasting performance is evaluated by comparing TBATS and Prophet through rolling-origin cross-validation, while a novel machine learning-inspired distribution model (MD) is developed to transform monthly forecasts into daily estimates. The results show that passenger-car registrations anticipate industrial production by three to five months, with a ninety per cent bootstrap interval of one to six. TBATS consistently outperforms Prophet in one-step-ahead forecasting. An out-of-sample test confirms that lagged registrations reduce the forecast error of industrial production without recourse to two-sided filtering, although the reduction is modest in magnitude and is not found for quarterly GDP. The proposed MD-shallow model achieves the highest distributional accuracy, surpassing both Prophet and a persistence benchmark. The findings demonstrate that vehicle registrations are a valuable standalone Leading Economic Indicator and that integrating traditional economic indicators with advanced forecasting techniques improves real-time monitoring of automotive demand and business-cycle dynamics. The proposed framework provides a practical tool for policymakers, manufacturers, and researchers seeking earlier and more reliable assessments of economic activity. Full article
(This article belongs to the Special Issue Next-Generation Macroeconomics: Data-Driven and Artificial Intelligence Approaches)
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22 pages, 7452 KB  
Article
From Regime Detection to Decision Rules: A Data-Driven Macro-Financial CVaR Framework for European Multi-Asset Portfolios
by Jorge Grube Martín-Lunas, Ana Lazcano and Julio E. Sandubete
Economies 2026, 14(7), 268; https://doi.org/10.3390/economies14070268 - 9 Jul 2026
Viewed by 601
Abstract
Weekly macro-financial and financial market data, combined with machine learning methods, offer new possibilities for identifying latent economic states in real time, but the portfolio value of regime detection depends critically on how detected states are translated into allocation rules. This paper develops [...] Read more.
Weekly macro-financial and financial market data, combined with machine learning methods, offer new possibilities for identifying latent economic states in real time, but the portfolio value of regime detection depends critically on how detected states are translated into allocation rules. This paper develops and evaluates a data-driven macro-financial framework that combines a four-state Gaussian Hidden Markov Model (HMM), estimated on eight weekly macro-financial features, with Conditional Value-at-Risk (CVaR) portfolio optimization across European multi-asset portfolios from January 2000 to April 2026. Using a strictly out-of-sample walk-forward design, we show that naive regime-conditional CVaR allocation generates excessive turnover (approximately 226% per year) that erodes net performance below a simple benchmark under any realistic transaction cost, whereas implementation-aware alternatives recover the gap substantially: regime-constrained weight bands attain a net Sharpe ratio within 0.009 of the static benchmark at roughly 29% annual turnover. Expanding the universe to include sovereign bonds improves drawdown control but introduces duration risk that materializes in rate-hiking episodes. These findings demonstrate that, in data-driven macro-financial systems, the bottleneck is not regime detection but transparent, stable, and cost-aware decision-rule design, with implications for next-generation, AI-assisted macro-financial monitoring and policy surveillance systems. Full article
(This article belongs to the Special Issue Next-Generation Macroeconomics: Data-Driven and Artificial Intelligence Approaches)
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