In the context of the Bank of Albania’s primary objective of achieving and maintaining price stability, generating accurate and reliable forecasts for the future rate of inflation is a necessity for its successful realization. This paper aims to enrich the Bank’s portfolio of short-term inflation forecasting tools through the construction of a Bayesian vector autoregressive (BVAR) model, which unlike standard autoregressive vector (VAR) models, addresses the overparameterization problem, allowing for the inclusion of more endogenous variables, and in this way enabling a more comprehensive explanation of inflation. Several univariate models are estimated to forecast short-term inflation, such as: unconditional mean, random walk, autoregressive integrated moving average (ARIMA) models, and the best performing among them is used as a benchmark to evaluate the forecast performance of the BVAR model. In addition, an unrestricted VAR - the most commonly used tool to obtain projections of the main economic indicators - is constructed as an additional benchmark, based solely on the information that the data series provides. The results show that the BVAR approach, which incorporates more economic information, outperforms the benchmark univariate and the unrestricted VAR models in the different time horizons of the forecast sample, but the differences between models in terms of their forecast performance are not statistically significant.