Abstract
MSMEs, also known as micro, small, and medium-sized enterprises, are the backbone of the economy in developing countries. Empirical studies indicate that SMEs generally face obstacles, particularly in financing. This study focuses on two main aspects: indexing financial inclusion using principal component analysis (PCA), and analyzing credit and financial inclusion using vector autoregression (VAR) for forecasting. Through a two-stage indexing methodology, the study emphasizes the importance of geographical reach in financial inclusion availability compared to demographic reach, with availability being the most crucial dimension compared to accessibility and usage. VAR models and forecasting were developed for the period from March 2012 to July 2022 in Indonesia, incorporating other variables, such as access to credit, credit risk, and real GDP. The use of VAR demonstrates consistency, accuracy, and reliability in producing predictions that closely approximate reality, providing a critical basis for policymakers.