No abstract

A bootstrap simulation approach was used to generate values for endogenous variables of a simultaneous equation model popularly known as Keynesian Model of Income Determination. Three sample sizes 20, 30 and 40 each replicated 10, 20 and 30 times were considered. Four different estimation techniques: Ordinary Least Square (OLS); Indirect Least Square (ILS); Two-Stage Least Square (2SLS) and Full Information Maximum Likelihood (FIML) methods were employed to estimate the parameters of the model. The estimators were then evaluated using the average parameter estimates; absolute bias of the estimates and the root mean square error of the estimates. The result shows that generally, ILS provided the best estimates.

Solving a non-linear differential equation most times is difficult and requires some technicalities. Many semi-analytical methods were derived in literature to provide series solution to non-linear problem, with each method giving some level of accuracy when compared with their equivalent exact solution (or numerical solution in case exact does not exist). Thus, system of ordinary differential equations (ODEs) arising from a formulated Susceptible-Infected-Quarantine-Recovered-Immunity (SIQRM) mathematical model of a disease dynamics were solved using DTM and Pade approximation; and their results numerically compared with Runge-Kutta order 4 (RK4). The table of result shows that DTM is reliable to tackle non-linear DE while Pade approximant improves its (DTM) accuracy.