Predicting inflation component drivers in Nigeria: a stacked ensemble approach

Abstract

Our study examined the disaggregation of inflation components in Nigeria using the stacked ensemble approach, a machine learning algorithm capable of compensating the weakness of an ensemble and a base learner with the strength of another. This approach gives flexibility of a synergistic performance of stacking each base learner and produces a formidable model that yields a high level of accuracy and predictive ability. We analyzed the test data, out-of-sample, and our analyses reveals a robust inflation prediction results. In particular, we show that food CPI is the most important driver for headline urban, and rural inflation while bread and cereals is the most important driver for food inflation in Nigeria. Also, biscuits, agric rice, garri white were found to be among the top main drivers of bread and cereal inflation. Our study further shows that some components of the CPI baskets that majorly drive inflation were assigned lower weights. Hence, attention to CPI weights only, without recourse to understanding the tipping source, may undermined a successful control of inflation in Nigeria. Tracing and tracking the source of inflation to the least sub-component will help resolve inflation problem.