DEVELOPMENT OF LOGIT SKEWED EXPONENTIAL POWER (LSEP) DISTRIBUTION FOR MODELLING INFLATION RATE

Abstract

This study focuses on the development and application of the Logit Skewed Exponential Power (LSEP) distribution to model rates and proportion. The data considered were the Nigeria's inflation rate from 2008 to 2024. The study employs a systematic methodology that involves parameter estimation, graphical analysis using histograms, P-P plots, Q-Q plots, and model adequacy evaluation through information criteria such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The estimated parameters for the LSEP model—Alpha (0.3001), Beta (1.2915), Miu (-0.7066), and Sigma (0.0197)—demonstrated its ability to address the skewness and scale of the inflation rate distribution. The Beta distribution's parameters—Alpha (1.367) and Beta (6.678)—revealed a simpler structure that was less capable of capturing the data's complexity. The evaluation of model adequacy through information criteria showed that the LSEP model had a lower log-likelihood value (66.26637) and lower AIC (-124.5327) and BIC (-115.9602) compared to the Beta distribution log-likelihood value (52.8371) and higher AIC (-101.6742) and BIC (-97.38794). The LSEP model’s parameter estimates indicate its robustness in addressing the complexities of the inflation rate, with lower AIC and BIC values compared to the Beta distribution.