Abstract

It is critical to understand the dynamic interaction between banking sector development and economic growth in transition economies, particularly in bank-based financial systems such as Kosovo. As quantitative approaches to decision-making become increasingly central to policy formulation, the statistical properties and transmission dynamics of financial variables gain particular importance.
The analysis examines the impact of key banking indicators, credit to the private sector, deposit mobilization, and interest rates on economic growth, explicitly modelling both short-run dynamics and long-run equilibrium relationships. Quarterly data covering the period 2010Q1-2023Q4 are employed. Given the presence of mixed orders of integration among variables, the Autoregressive Distributed Lag (ARDL) bounds testing approach is applied, offering a flexible and statistically robust framework for small-sample macroeconomic modelling.
Empirical findings indicate a stable and statistically significant long-run relationship between banking sector indicators and GDP. Credit and deposits exhibit a positive and persistent effect on economic growth, whereas interest rates have a negative impact, consistent with the cost of capital channel. The error correction mechanism suggests a relatively rapid convergence toward equilibrium, confirming the stability and internal consistency of the estimated model.
From a methodological perspective, the study demonstrates the suitability of ARDL modelling for capturing complex financial-growth dynamics in data-constrained environments. The findings provide actionable insights for monetary authorities and contribute to the broader statistical discourse on modelling economic systems with strong policy relevance.