Structural Vector Autoregressive (SVAR) Analysis on Malaria Incidence in Gombe, Nigeria
M. B. Mohammed, A. U. Kinafa and B. Nata’ala
Vector autoregressive (VAR) models are capable of capturing the dynamic structure of many time series variables. Granger causality test, Impulse response functions and variance decomposition are typically used to investigate the relationships between the variables included in such models. In this context the relevant impulses or innovations or shocks to be traced out in an impulse response analysis have to be specified by imposing appropriate identifying restrictions. Taking into account the cointegration structure of the variables offers interesting possibilities for imposing identifying restrictions. Therefore VAR models which explicitly take into account the cointegration structure of the variables, so-called vector error correction models were considered in the previous work.
Granger causality test showed that Female group Granger cause Pregnant group (i.e Female group is helpful in predicting the future Pregnant malaria cases) while all other pairs were not significant. The results of impulse response functions revealed that almost all the groups had positiveand/or negative effects on othergroups. Finally variance decomposition analysis conducted indicatesthat all the groups were largely explained by their own innovations and slightly by the shocks of other groups.
Keywords: Vector Error-correction Model; Granger Causality Test; Impulse Response Function; Variance decomposition.
Pages: 217 – 233 | Full PDF Paper