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Double-Hopf bifurcation in an extended Goodwin model with Mechanization, Independent Investment, and Disequilibrium: Toward a Marxian-Keynesian Synthesis
No full textThis paper proposes an extended Goodwin model that synthesizes Marxian and Keynesian dynamics into a unified four-dimensional framework. The model integrates endogenous technical change via mechanization, investment behavior driven by effective demand, and goods market disequilibrium. We develop two three-dimensional closures–a Classical-Marxian and a Keynesian-Kaleckian formulation–each capable of generating persistent endogenous cycles through Hopf bifurcations. These are then combined into a Marxian-Keynesian (MK) system, which exhibits complex dynamics including quasi-periodicity and, under specific parameter values, a double-Hopf bifurcation. This result, to our knowledge not previously identified in extended Goodwin models, points to the potential for interacting oscillatory modes and long-run fluctuations even with relatively simple behavioral rules. Numerical simulations suggest that the MK synthesis captures rich endogenous fluctuations without relying on exogenous shocks and may exhibit chaotic dynamics under future extensions. These findings lay the groundwork for a more comprehensive Mars-Keynes-Schumpeter synthesis of capital instability, as suggested in the conclusion section
Productivity and Productive Capital: Metaphysical Perspectives
No full textThe research examines productivity and productive capital formation and the dynamic interplay between various factors of productivity. We attempt to derive a metaphysical perspective on the theory of productivity in relation to human capital formation. A simple model of productivity function has been designed to explain the underlying principles
Strategic stockpiling reduces the geopolitical risk to the supply chain of copper and lithium
No full textCopper and lithium are essential to the global energy transition, each playing distinct roles in enabling low-carbon technologies. However, their supply chains are highly vulnerable to geopolitical risks, posing a threat to the stability and resilience of future clean energy systems.This study proposes strategic stockpiling as a cost-effective instrument to mitigate supply disruptions due to geopolitical risks in copper and lithium supply chains. First, we develop and apply novel, stage-specific, measures of geopolitical risk for copper and lithium for each of the four key phases of their supply chain: proven reserves, extraction, refining and end-use
consumption. Second, we construct forward-looking stockpiling scenarios for both minerals, grounded in projected demand under the International Energy Agency’s Announced Pledges
(APS) and Net Zero Scenario (NZS) pathways. Our estimates indicate substantial supply shortfalls by 2040 when strategic stockpiling is incorporated. Specifically, we project the
shortfall in lithium supply to increase by a factor of 7.8 under APS and 9.8 under NZS, while copper shortages are projected to grow by 4.6 and 6.1 times, respectively. We consider Artificial Intelligence (AI)-driven productivity gains and recycling as alternative ways to alleviate shortages in both copper and lithium markets. We show that while enhanced recycling can significantly contribute to closing the supply gap for copper, its impact remains limited in the case of lithium due to technological, geological, and geographical constraints. We conclude that AI-driven productivity gains are essential to close the supply gap for both critical minerals
GARCH-FX: A Modular Framework for Stochastic and Regime-Aware GARCH Forecasting
No full textTraditional GARCH models, while robust, are deterministic and their long-horizon forecasts converge to a static mean, failing to capture the dynamic nature of real markets. Conversely, classical stochastic volatility models often introduce significant implementation and calibration complexity. This paper introduces GARCH-FX (GARCH Forecasting eXtension), a novel and accessible framework that augments the classic GARCH model to generate realistic, stochastic volatility paths without this prohibitive complexity.
GARCH-FX is built upon the core strength of GARCH—its ability to estimate long-run variance—but replaces the deterministic multi-step forecast with a stochastic simulation engine. It injects controlled randomness through a Gamma-distributed process, ensuring the forecast path is non-smooth and jagged. Furthermore, it incorporates a modular regime-switching multiplier, providing a flexible interface to inject external views or systematic signals into the forecast’s mean level.
The result is a powerful and intuitive framework for generating dynamic long-term volatility scenarios. By separating the drivers of mean-level shifts from local stochastic behavior, GARCHFX aims to provide a practical tool for applications requiring realistic market simulations, such as stress-testing, risk analysis, and synthetic data generation
Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence
No full textThis paper analyses the dynamics of the natural rate of interest (r-star) in the US using a score-driven state-space model within the Laubach–Williams structural framework. Compared to standard score-driven specifications, the proposed model enhances flexibility in variance adjustment by assigning time-varying weights to both the conditional likelihood score and the inertia coefficient in the volatility updating equations. The improved state dependence of volatility dynamics effectively accounts for sudden shifts in volatility persistence induced by highly volatile unexpected events. In addition, allowing time variation in the IS and Phillips curve relationships enables the analysis of structural changes in the US economy that are relevant to monetary policy. The results indicate that the advanced models improve the precision of r-star estimates by responding more effectively to changes in macroeconomic conditions
Наукастинг и прогнозирование ВВП России и его компонентов с помощью квантильных моделей
No full textThe paper examines the quality of probabilistic nowcasts and short-term forecasts of the Russian GDP and its components in constant prices (consumption, investment, exports and imports) based on the standard quantile regression model and its shrinkage modifications, aimed at reducing the risk of overfitting (averages of quantile forecasts, partial quantile regression, regressions with regularization, Bayesian quantile regression). We find that quantile models with predictors are superior to autoregressive and OLS models in terms of CRPS (Continuous Ranked Probability Score) metrics in nowcasting exercises for investment and consumption. When forecasting 1-4 quarters ahead, shrinkage models yield the most accurate forecasts of GDP and consumption distributions at all horizons. For investment and imports, shrinkage methods turn out to be the best performers at three forecast horizons out of four. There is no single shrinkage model, which would provide the best probabilistic forecasts of macroeconomic variables much more often than others
Totalitarian Accounting and the Trumpian Risks
No full textThe research takes a discourse analysis approach to the bilateral information of mainland China and the United States, with a focus on how the economic game will lead to in relation to geopolitics. With the law of supply and demand and the characteristics of the Chinese socialist economy, the research analyzes the totalitarian regime’s possible future impacts on the free economy that consists of the majority of the globalized economy. The research concludes that Donald Trump’s policy agendas pose a tremendous risk for global security with the key game played out in the futures field
Inflationary and Deflationary Pressures: A Directional Decomposition of U.S. Inflation Dynamics
No full textThis paper develops a pressure decomposition of inflation as the net outcome of two competing forces: inflationary pressure, defined by the frequency and magnitude of price increases, and deflationary pressure, determined by corresponding price decreases. Using 245 PCE sub-indices spanning 1959-2024, we construct an exact bottom-up inflation measure that transparently maps sectoral pricing decisions into macroeconomic aggregates. Our decomposition reveals fundamental asymmetries in inflation formation: inflationary pressure exhibits dramatic variation (2.35%-12.68%) while deflationary pressure remains remarkably stable (0.72%-5.18%), indicating inflation episodes are primarily driven by surges in upward pricing momentum rather than retreats of downward movements. Historical analysis shows distinct pressure regimes across major macroeconomic episodes: the Great Inflation featured extreme inflationary pressure volatility, the Great Moderation achieved balanced dynamics, the 2008-2009 crisis uniquely witnessed deflationary pressure dominance creating deflation risk, while COVID-19 saw dramatic inflationary pressure resurgence. We reassess the price puzzle using Bayesian local projections with alternative monetary policy shock identifications. Conventional narrative shocks generate sustained inflationary pressure increases with minimal deflationary response, while informationally robust shocks resolve the puzzle completely through both increased deflationary pressure and reduced inflationary pressure, with the deflationary channel providing the dominant contribution consistent with demand-channel transmission. Extensive robustness checks across specifications and estimation methods confirm these findings while revealing the diagnostic value of pressure decomposition for evaluating shock quality. Results demonstrate that the price puzzle reflects informational frictions rather than genuine economic phenomena, and suggest successful monetary policy operates through managing pressure balance with important implications for real-time policy diagnosis and central bank communication
L'Aide Publique au Développement et la croissance économique dans l'UEMOA : Entre efficacité et dépendance structurelle
No full textThis study examines the impact of Official Development Assistance (ODA) on economic growth in the West African Economic and Monetary Union (WAEMU) over the period 2000-2022. Using a FMOLS econometric approach applied to panel data covering the eight member countries, the analysis reveals a complex and conditional relationship between development aid and economic growth. The results partially confirm the positive effect of ODA on growth, but demonstrate that this effectiveness crucially depends on specific internal conditions, particularly the level of domestic savings and governance quality. The study highlights a significant leverage effect between external and internal resources, with the ODA-savings interaction being positive and statistically significant. Regarding aid dependency, the analysis reveals the existence of a non-linear inverted U-shaped relationship, confirming the existence of an optimal threshold beyond which dependency becomes counterproductive for growth. Gross Fixed Capital Formation emerges as the most robust factor across all models, confirming the central role of domestic investment. These results advocate for a strategic reorientation of aid policies towards strengthening internal capacities, improving governance, and diversifying development financing sources from a perspective of economic sovereignty and endogenous growth
Financial markets stress indicator for Slovenia (FIMSIS)
No full textThe Global Financial Crisis (GFC) highlighted the importance of early identification of systemic financial stress and timely macroprudential policy responses. In this context, financial stress indices have become essential tools for monitoring systemic risk in real time. While composite indicators exist for the euro area and several member states, Slovenia has lacked such a measure, primarily due to limited financial market depth and data constraints. This paper introduces the Financial Markets Stress Indicator for Slovenia (FIMSIS), the first composite financial stress indicator developed specifically for the Slovenian financial system. FIMSIS aggregates volatility-based indicators across market segments using three alternative approaches - exponentially weighted moving average (EWMA), multivariate GARCH (BEKK) and principal component analysis (PCA) - allowing for a comparative evaluation of aggregation techniques. The indicator captures both the intensity and systemic dimension of financial stress and is evaluated through robustness checks and regime classification using a Markov-switching model. To assess predictive performance, we apply a Growth-at-Risk framework with Adaptive LASSO and non-crossing constraints. Results confirm FIMSIS's relevance for signalling downside macroeconomic risk