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Item type:Publication, When expectations matter: The role of fiscal foresight in government spending shocks in Peru(Elsevier BV, 2026-01-01)This paper estimates the macroeconomic effects of government spending in Peru while explicitly accounting for fiscal foresight—the possibility that economic agents anticipate future fiscal actions before they are implemented. We use a standard vector autoregressive (VAR) framework with recursive identification and incorporate quarterly government spending projections published by the Central Reserve Bank of Peru to isolate the anticipated component of fiscal policy. Unanticipated government spending shocks raise real GDP; however, their effects are systematically overstated when anticipated fiscal information is omitted. Anticipated spending shocks – captured through government spending projections – also generate positive and statistically significant effects on output. These findings remain robust to alternative measures of fiscal foresight, different recursive orderings, and the inclusion of additional control variables, including private consumption and terms of trade. The results highlight the importance of distinguishing between anticipated and unanticipated fiscal actions for credible empirical assessment of fiscal multipliers in emerging economies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A bibliometric literature review of integrated data and model based diagnosis approaches for the industry 4.0(Taylor & Francis, 2026-01-01)The increasing presence of Cyber-Physical Systems and the Internet of Things has accelerated the digital transformation of industrial environments, commonly known as Industry 4.0. In this context, Artificial Intelligence techniques are increasingly used to support automatic diagnostic tasks. This paper presents a systematic literature review of hybrid diagnostic systems that combine Model-Based Diagnosis (MBD), which relies on physical models to detect abnormal behaviour, and Data-Based Diagnosis (DBD), which uses machine learning to identify faults from data. The review has two objectives: (i) to examine how MBD and DBD methods have been combined to improve diagnostic performance, and (ii) to identify integration opportunities through existing machine learning frameworks to support reusable and adaptive solutions. A bibliometric analysis was conducted following a simplified PRISMA 2020 methodology. From over 1300 records, 75 articles were selected and analysed. Most hybrid systems adopt a serial architecture where DBD classifiers analyze residuals from MBD for fault detection and isolation. The most common applications are found in smart manufacturing and energy systems, and as for the most used machine learning techniques. Challenges remain regarding real-time scalability, interpretability, and standardisation. This review provides a structured foundation for designing explainable, efficient, and reusable diagnostic solutions for Industry 4.0.HighlightsThe literature review of hybrid data-based diagnosis (DBD) and model-based diagnosis (MBD) systems in Industry 4.0 reveals key trends and promising future directions.Hybrid diagnostic systems facilitate online monitoring, improve explainability, and enable intelligent feature selection.The most widely used hybrid architecture for integrating MBD and DBD techniques is serial integration.Serial integration uses MBD for residual generation, and DBDs for fault isolation and detection.There is growing interest in integrating hybrid models with explainability and online learning.The most common applications are in the automotive and energy sectors, in predictive maintenance and smart monitoring.Current hybrid diagnostic systems lack real-time scalability and lack interpretability and reusability.Scopus© Citations 2 2