Toward a general framework for constructing and evaluating core inflation measures

There is no unifying framework for evaluating core inflation measures, so we propose a general framework to close this gap. Our methodology uses disaggregated data of consumer price index, and hinges on a standard quadratic loss function. We show that the usual indicator that excludes food and energy, which is the most widespread measure of core inflation for Central Banks, performs poorly across the five countries analyzed, due to substantial bias, low persistence, high volatility, and low forecasting power. Therefore, our recommendation is to revise its use. By optimally selecting the CPI components to be excluded with our methodology, the properties of core inflation measures can be significantly improved. Finally, we argue that when there is a preference regarding the use of fixed exclusion measures, nothing is lost and much can be gained by optimally selecting the excluded items, instead of sticking with the usual adhoc criteria.