Sustainable Futures· 2026Q1
Structural signals of speculative technology cycles: a retrospective multidomain diagnostic for the MENA region
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- Q1SCImago
- 2026year
Short summary
A new framework integrating economic, labor, startup, policy, and behavioral indicators retrospectively identified speculative technology cycles in the MENA region, co-moving with past tech bubbles but failing to predict future ones.
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Key points
- A hybrid econometric-machine learning framework was developed to diagnose speculative technology cycles in MENA economies.
- The framework integrates five domains: economic fundamentals, labor markets, startup ecosystems, policy governance, and behavioral indicators.
- A Speculative Technology Regime Index, derived from error-correction terms, retrospectively co-moved with the dot-com episode and a mid-2010s turning point.
- Machine learning models (Logistic Regression, Random Forest, XGBoost) failed to predict future regime transitions using the developed index.
AI-generated from the title and abstract; the full text is not read.
Abstract
This study develops a foresight-oriented structural diagnostic for detecting speculative technology dynamics in emerging digital economies, focusing on the Middle East and North Africa (MENA) region. Moving beyond conventional macroeconomic monitoring, the framework integrates long-run structural relationships across five domains: economic fundamentals, labor markets, startup ecosystems, policy governance, and behavioral indicators. Only three met the stationarity and coverage requirements for equilibrium modelling; the exclusion rule and its sensitivity to aggregation are stated explicitly. Using 26 series from 24 macroeconomic indicators over 2000 to 2022, Johansen cointegration analysis identifies long-run equilibria within each domain. These are estimated on a regional aggregate of 23 annual observations, with one lagged difference and an unrestricted constant, under a finite-sample correction. Deviations from equilibrium, captured as error-correction terms (ECTs), signal structural stress. They are combined into a composite Speculative Technology Regime Index distinguishing build-up, overheating, and collapse phases. Logistic Regression, Random Forest, and XGBoost models then test whether these signals predict regime transitions. The index co-moves retrospectively with the dot-com episode and a mid-2010s turning point. However, it shows no predictive generalization: all three models misclassify both held-out observations. The study contributes a hybrid econometric–machine learning approach to technology foresight and socio-technical transitions research, offering policymakers a retrospective lens on structural stress. Prospective early warning remains unresolved. Achieving it would require panel-level estimation, independent regime dating, real-time signal construction, and rolling-origin validation against an explicit benchmark.
The authors' abstract, as published at the source. Sustainable Futures, 2026 · DOI ↗
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Field: General Economics, Econometrics and Finance
General Economics, Econometrics and FinanceEconomics, Econometrics and Finance