AI 中文总结
研究由α稳定 Lévy 过程驱动的索洛型增长模型，通过扩展框架捕捉宏观经济波动特征，推导相关分布与函数，设计估计策略并应用于阿根廷等数据，结果表明该模型在多方面优于高斯模型，为经济预测和参数估计提供可靠工具。
AI 中文摘要
本文实证实现了一个由α稳定 Lévy 冲击驱动且资本弹性随时间变化的索洛型增长模型。通过α稳定 Lévy 过程扩展框架，捕捉宏观经济剧烈波动的三个典型事实。推导资本偏差过程的平稳分布等，设计基于目标函数的估计策略。应用于阿根廷数据，结果表明 Lévy 模型较高斯奥恩斯坦 - 乌伦贝克模型更优，跨国证据显示资本调整速度稳定，Lévy 框架在多方面表现更出色，为预测和结构参数估计提供更可靠工具。
英文摘要
This paper empirically implements a Solow-type growth model driven by $α$-stable Lévy shocks with time-varying capital elasticity. We extend the framework with an $α$-stable Lévy process, thereby capturing three stylized facts of severe macroeconomic fluctuations: heavy-tailed distributions, jump discontinuities, and infinite variance. We derive the stationary distribution of the capital deviation process, obtain its conditional characteristic function in closed form, and provide an integral representation that explicitly reveals a dual mean-reversion structure separating investment gestation lags from endogenous feedback. We design an estimation strategy based solely on well-defined objective functions that respects the probabilistic properties of Lévy-driven data and circumvents the non-existence of variance. We apply the framework to Argentine quarterly data from 2004 to 2023, with time-varying capital elasticity calibrated from Penn World Table labor shares. Our estimates show that the Lévy specification delivers structural parameters substantially closer to external PWT benchmarks than the Gaussian Ornstein-Uhlenbeck counterpart and substantially improves crisis-period tracking without sacrificing performance in tranquil periods. Cross-country evidence from Colombia and the United States confirms that the quarterly capital adjustment speed $η\approx 0.05$ exhibits striking stability across vastly different volatility regimes. Robustness checks across tail index specifications demonstrate that the Lévy framework consistently outperforms the Ornstein-Uhlenbeck benchmark for a broad range of empirically relevant tail indices. These findings establish the Lévy specification as a robust generalization of the Gaussian benchmark, offering a more credible tool for forecasting and structural parameter estimation in both emerging and advanced economies.
