DETECTION OF ABERRANT OBSERVATIONS AND MODELLING OF CONSUMER PRICE INDEX AND INFLATION RATE IN NIGERIA FROM 1960 - 2014: THE WAVELET APPROACH
DOI:
https://doi.org/10.53555/nnms.v1i1.1495Keywords:
Wavelets, Aberrant observation, Resolution, Modelling, DistributionsAbstract
Aberrant observations (AO) are data usually inconsistent with the rest of the series and have the tendency to render statistical inference invalid. The spectral method is widely used for detecting AOs but this is restricted to when series is stationary and periodic. Wavelet shrinkage is a mutually exclusive choice to the spectral method which shrinks the size of the series into multi-resolutions without losing the properties of the series. In this research paper, we present aberrant observation detection and modelling approach based on wavelet analysis in the detection of aberrant observations and modelling using Gaussian and Non-Gaussian distributions in the frequency domain with promising results, on inflation rate in Nigeria between 1960 to 2014. In the method of detecting AO, the inflation rate was highest in 1995 followed by 1993, 1994, 1988, 1989 and 1992. These inflation rates were all observed during the military era. Normal distribution has highest values in all the resolutions from the Loglikelihood calculated, hence, it is the best of the three for detecting AO from the data. Akaike Information Criteria (AIC) confirms that at higher resolutions, cauchy distribution has lowest AIC which confirms it as the best method of modelling data at such resolutions.
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