Probability forecasts are gaining popularity in the macroeconomic discipline as point forecasts lack the ability to capture the level of uncertainty in fundamental variables like inflation, growth, exchange rate, or unemployment. This paper explores the use of probability forecasts to predict inflation in Argentina. Scoring rules are used to evaluate several autoregressive models relative to a benchmark. Results show that parsimonious univariate models have a relatively similar performance to that of the multivariate models around central scenarios but fail to capture tail risks, particularly at longer horizons.

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