Open AccessConstraint and Unconstraint of Vector Autoregressive Model; Using GDP Growth Rate of Agriculture, Industries, Building/Construction, Whole-Sale/Retail and Services in Nigeria
Author(s) -
Aniedi. Moses Ekpenyong,
Didi Essi Isaac
Publication year - 2021
Publication title -
asian journal of probability and statistics
Language(s) - English
Resource type - Journals
ISSN - 2582-0230
DOI - 10.9734/ajpas/2021/v12i230284
Subject(s) - akaike information criterion , autoregressive model , econometrics , autoregressive integrated moving average , statistics , distributed lag , mathematics , multivariate statistics , constraint (computer aided design) , time series , geometry
In this research, multivariate Time Series was adopted to model the Gross Domestics Product (GDP) growth rate of Nigeria on five (5) variables namely: Agriculture, Industries, Building/construction, Wholesales/Retails trade and Services The data was collected from National Bureau of Statistics, range quarterly from 1985 to 2017, a total of 33years. Real (R) software was used as a tool to analyze the model. The data were grouped into 10 pairs of 2 parameter variables, 10 pairs of 3 parameters variables, 5 pairs of 4 parameters variables, and the complete 5 parameter variables. In each group, the best model was selected and Lag's using Akaike Information Criteria, then the unconstrained (vector autoregressive) AIC of the model was compared with that of constrained (simplified vector autoregressive) AIC model. The unconstraint models with AIC values (-11.973, -17.1111, -22.1823, and 25.8996) at lag (5) was compared with that of constraint models with AIC values of (-12.5116, -17.5298, -22.2894 and -25.9916), the outcome showed that constraint models performed better than unconstraint models.