Food inflation is a macroeconomic agent of notable impact on the cost of living of citizens and in the approach to decision-making of entities that have an impact onmonetary policy. Therefore, it is of interest to have the ability, with some degree of certainty, to obtain relevant information on the variation in food prices. On the other hand, The SIPSA (DANE unit) is the government entity responsible for collecting the prices with which the products are sold in the country’s supply centers. In this paper, the problem of contributing to the prediction of food inflation based on a SIPSA index proposed by the authors, is addressed. Three methodologies are used for this purpose. Namely: integrated auto-regressive averagemean with exogenous variables models (SARIMAX), vectorial auto-regressive structural models (SVAR) and vector error correction model (VECM). Our results indicate the variations of SIPSA explain about 40 % of the variability of food inflation, in addition to the relationship in the short and long term. The creation and use of indicator stories can become ideal tools for the planning and management of portfolio investment strategies with a view to the technology of investment proposals in the capitalmarket.