ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS

  • Halistin Halistin IAIN Kendari
  • Vita Ratnasari IAIN Kendari
  • Santi Puteri Rahayu IAIN Kendari
  • Tandri Patih IAIN Kendari
DOI: https://doi.org/10.30598/barekengvol14iss2pp167-180
Keywords: Probit, Data Panel, Gauss Hermite Quadrature, BFGS

Abstract

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.

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Published
2020-06-01
How to Cite
[1]
H. Halistin, V. Ratnasari, S. Rahayu, and T. Patih, “ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS”, BAREKENG: J. Math. & App., vol. 14, no. 2, pp. 167-174, Jun. 2020.
Issue
Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

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