Both economic and demographic factors can play a role in the fluctuation of the fertility rate; in fact, there are many factors that lead to either the reduction or the increase in the fertility rate. Our primary objective is to determine the regression coefficients of the variables (factors) on which the fertility rate depends and on the basis of which parameters estimates are made for the purpose of predicting the future value of the dependent variable. The accuracy of the prediction is contingent on the selection of an adequate model as well as the utilization of an effective estimation process to estimate the regression coefficients. It is common practice to employ the method of ordinary least squares (OLS) when attempting to determine the values of the parameters of a linear model with a single equation. This is because this method is strongly dependent on the primary quantitative approach utilized in economics. By utilizing the Gauss-Markov theorem, it is able to provide the best linear unbiased estimates (BLUE) of the parameters when spherical assumptions are taken into consideration. Yet, the vast majority of economic theories, such as the fertility model, require a collection of relationships; hence, the application of single equations cannot be justified in these kinds of circumstances. Because single equation models in economics do not provide an accurate representation of the issues facing the economy, we need to investigate the connections between the variables that make up simultaneous equation models.

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