ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS - Halistin, Halistin; Ratnasari, Vita; Rahayu, Santi Puteri; Patih, Tandri (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2020)
One model that may explain the pattern of the relationship between the categorical dependent variable and the independent variables is probit regression. In the probit regression, the independent variable can be categorical or continuous. Probit regression is using the link function of the standard normal distribution. If the probit regression modeling involves a cross-section data and time series data, it is called probit data panel model. Parameter estimation of random effect probit data panel model is using the maximum likelihood estimation (MLE) method with Gauss Hermite Quadrature approach. Iterative procedure by using BFGS method. BFGS method used to obtain the close form value of the parameter estimates.