- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
In this article a novel methodology for building core inflation measures is proposed based on the k-means clustering machine learning algorithm. This new methodology is explored using Mexican CPI data in the spirit of getting a clear signal and having good predictions of the inflationary process based on selecting items with low volatility and assigning them to clusters. The results show that the core inflation built captures better the inflation signal and also outperforms the short-term inflation forecasts obtained by the trimmed means method and the core inflation excluding food and energy.
This study explores the k-means machine learning algorithm for building core inflations measures. As an empirical example, a core inflation measure, the k-vol core, is built based on reducing volatility. This indicator is evaluated on two criteria: its ability to grasp the inflation signal and its capacity to forecast future inflation. The results show that the k-vol core outperforms trimmed mean indicators and core inflation excluding food and energy. The study aims to contribute to the inflation literature and to provide a useful tool to policy makers when taking monetary policy decisions.