Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)
This study addresses industrial investment dynamics for policymakers in emerging economies like Uruguay, but it is incremental as it applies existing methods to a specific historical dataset.
This paper tackled the problem of understanding how fiscal incentives affect industrial investment in Uruguay from 1974 to 2010, finding that fiscal incentives significantly drive long-term industrial growth and that machine learning models reveal nonlinear interactions with factors like exchange rates.
This paper examines the impact of fiscal incentives on industrial investment in Uruguay from 1974 to 2010. Using a mixed-method approach that combines econometric models with machine learning techniques, the study investigates both the short-term and long-term effects of fiscal benefits on industrial investment. The results confirm the significant role of fiscal incentives in driving long-term industrial growth, while also highlighting the importance of a stable macroeconomic environment, public investment, and access to credit. Machine learning models provide additional insights into nonlinear interactions between fiscal benefits and other macroeconomic factors, such as exchange rates, emphasizing the need for tailored fiscal policies. The findings have important policy implications, suggesting that fiscal incentives, when combined with broader economic reforms, can effectively promote industrial development in emerging economies.