Many developing countries are adopting inflation targeting regimes to guide monetary policy decisions. In such countries the share of food in the consumption basket is high and policy makers often employ total inflation (as opposed to core inflation) to set inflationary targets. Therefore, central banks need to develop reliable models to forecast food inflation. Our literature review suggests that little has been done in the construction of models to forecast short-run food inflation in developing countries. We develop a model to improve short-run food inflation forecasts in Colombia. The model disaggregates food items according to economic theory and employs Flexible Least Squares given the presence of structural changes in the inflation series. We compare the performance of this new model to current models employed by the central bank. Next, we apply econometric methods to combine forecasts from alternative models and test whether such combination outperforms individual models. Our results indicate that forecasts can be improved by classifying food basket items according to unprocessed, processed and food away from home and by employing forecast combination techniques.

许多发展中国家正在采用通胀目标制来指导货币政策决策。在这些国家中，食品在消费篮子中所占比例很高，政策制定者经常使用总通胀(而不是核心通胀)来设定通胀目标。因此，各国央行需要开发可靠的模型来预测食品通胀。我们的文献综述表明，在构建预测发展中国家短期食品通胀的模型方面几乎没有做什么。我们开发了一个模型来改善对哥伦比亚短期食品通胀的预测。该模型根据经济理论对食品项目进行分解，并在通胀序列中存在结构变化的情况下使用灵活的最小二乘法。我们将这个新模型的表现与中央银行目前使用的模型进行比较。接下来，我们运用计量经济学的方法结合来自其他模型的预测，并检验这种组合是否优于单个模型。研究结果表明，通过对菜篮子中未加工食品、已加工食品和离家食品进行分类，并采用预测组合技术，可以提高预测效果。