Dataset Information

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.

Supplementary information

The online version contains supplementary material available at 10.1007/s43546-022-00384-2.

SUBMITTER: Akande EO

PROVIDER: S-EPMC9734342 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Predicting inflation component drivers in Nigeria: a stacked ensemble approach.

Akande Emmanuel O EO Akanni Elijah O EO Taiwo Oyedamola F OF Joshua Jeremiah D JD Anthony Abel A

SN business & economics 20221209 1

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 ...[more]

PMID: 36531599

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Project description:Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate-related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) "published regional reports" of oak woodland area; and an "NFI map" of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from "NFI survey" sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.

Dataset Information

Predicting inflation component drivers in Nigeria: a stacked ensemble approach.

Supplementary information

Publications

Predicting inflation component drivers in Nigeria: a stacked ensemble approach.

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