Doi.org/10.52340/eab.2026.18.02.05

In the economic literature, considerable attention is devoted to the study of inflation. Rising price levels can negatively affect a country’s socio-economic conditions. Nevertheless, moderate inflation is often regarded as one of the guarantors of stability. To achieve this stability, inflationary shocks are managed through state monetary policy. The effectiveness of such policy largely depends on the specific characteristics of the country, public trust in the central bank, and the broader economic situation.
The Monetary Conditions Index (MCI), developed by the Bank of Canada in the late 1980s, is a weighted average of interest rates and exchange rates. It provides a means of assessing the stance of monetary policy, as both indicators – interest rates and exchange rates – serve as transmission mechanisms and influence inflation levels (Memon and Jabeen, 2018). MCI is frequently used for short-term operational assessments of monetary policy due to its many advantages. Its motivation is straightforward: the exchange rate strongly affects aggregate demand, particularly in small open economies. Alongside the interest rate, the exchange rate is a crucial component in analyzing economic behavior (Ericsson et al., 1998).
When conducting monetary policy, reliance on a single transmission channel is insufficient. In today’s economy, multiple channels are necessary to properly evaluate policy conditions. Consequently, the MCI has become an important tool for central banks for two main reasons:
1. It incorporates variables from two primary channels of monetary policy – interest rates and exchange rates.
2. The signals derived from these base variables, when combined into a composite index, are clearer and easier to interpret.
As noted, the MCI is widely applied. Central banks in Canada, New Zealand, Norway, and Sweden employ it in their policy frameworks. International institutions such as the IMF and OECD also use MCI to assess monetary policy across countries (Ericsson et al., 1998). While transmission mechanisms vary depending on national economic structures, the principle remains the same: interest rates influence aggregate demand, requiring central banks to decide whether to adopt restrictive or accommodative policies. Exchange rates, in turn, affect domestic price levels. Accordingly, MCI serves as a valuable instrument for evaluating monetary policy (Firdous et al., 2023).
The objective of this study is to assess Georgia’s monetary policy between 2012 and 2025 using MCI. To achieve this, existing research and relevant literature must first be examined, followed by the construction of an MCI tailored to the specific features of the Georgian economy.
The innovation of this study lies in the development of a composite index that evaluates Georgia’s monetary policy using both traditional economic models and machine learning algorithms. The methodological framework combines theoretical and empirical elements. The theoretical section draws on economic theory, scholarly papers, and the author’s own perspectives. The empirical section employs traditional econometric models – Ordinary Least Squares (OLS) and Autoregressive Distributed Lag (ARDL) – as well as machine learning techniques, particularly Principal Component Analysis (PCA).
The paper is structured as follows: the first part reviews existing studies; the second outlines the methodology for constructing the MCI; the third presents the empirical analysis based on Georgian data; and the final section discusses the results and explores future directions, including the application of more complex models and advanced techniques.

Keywords: Monetary Condition Index, Monetary Policy, Machine Learning, Principal Component Analysis, Econometrics
JEL Codes: E31, E52, E58, C32, C45

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