Maldives Macroeconomic Forecasting: A Component-Driven Quarterly Bayesian Vector Autoregression Approach
This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives using Bayesian vector autoregression estimations to circumvent the "curse of dimensionality" and constraints to analyzing and organizing data.
Due to significant limitations in data availability, empirical economic modeling for Maldives can be problematic. In the paper, Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives, in particular for macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
- Related Literature
- Model Specification
- Dataset, Patterns, and Relationships
- Conclusions and REcommendations