Year: 2022
Type: review
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 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 the highest level of accuracy and best predictive ability. We analyzed the test data, out-of-sample, and our results show a strong accuracy in predicting inflation. Our results further 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. However, biscuits, agric rice, garri white are among the top main drivers of bread and cereal inflation. We note that some CPI items that mostly drive inflation have lower weights while others have higher weights therefore, focusing entirely on CPI weights as a policy guide will stymied a successful control of inflation in Nigeria. In addition, ignoring CPI items with lower weights in policy intervention will make inflation difficult to control. Above all, adequate trace of the source of inflation to the least sub-component of each component will help address or formulates an appropriate policy to confront inflation problems in Nigeria.JEL: C53, E37
Source: SN Business & Economics
Language: English
Domain: Social Sciences
Open Access status: bronze
Awards: Post-Quantum Cryptography: a Cryptanalysis Approach, G1846950360, Bone Bricks: Cost effective modular osseointegrated prosthetics for large bone loss surgical procedures, Design Patterns for Inclusive Collaboration (DePIC), G2820559442, The Ethical Demand: Loegstrup's Ethics and Its Implications, Visual Modelling of Semantic Services by Graph Transformation, Isogeny-based cryptography: from theory to practice, TASCC: Human Interaction: Designing Autonomy in Vehicles (HI:DAV), ESRC Centre for Corpus Approaches to Social Science (CASS), Laymen To The Help Of Experts: Crowdsourcing To Aid The Reassembly Of Ancient Frescoes, Advancing Bayesian network algorithms for inferring gene regulation using an integrative computational-biological approach in a yeast model system, G8157293820, G8406077424
OpenAlex ID: W4312010708