ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS

Halistin; Vita Ratnasari; Santi Puteri Rahayu; Tandri Patih

doi:10.30598/barekengvol14iss2pp167-174

ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS

Translated title of the contribution: Parameter Estimation of Probit Model on Panel Data Using BFGS Optimization

Halistin^*
, Vita Ratnasari
, Santi Puteri Rahayu
, Tandri Patih

^*Corresponding author for this work

IAIN Kendari

Research output: Contribution to journal › Article › peer-review

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.

Translated title of the contribution	Parameter Estimation of Probit Model on Panel Data Using BFGS Optimization
Original language	Indonesian
Pages (from-to)	167-174
Number of pages	8
Journal	Barekeng
Volume	14
Issue number	2
DOIs	https://doi.org/10.30598/barekengvol14iss2pp167-174
Publication status	Published - 1 Jun 2020
Externally published	Yes

Keywords

BFGS
Gauss Hermite Quadrature
Panel Data
Probit

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10.30598/barekengvol14iss2pp167-174

Cite this

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title = "ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI 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.",

keywords = "BFGS, Gauss Hermite Quadrature, Panel Data, Probit",

author = "Halistin and Vita Ratnasari and Rahayu, \{Santi Puteri\} and Tandri Patih",

year = "2020",

month = jun,

day = "1",

doi = "10.30598/barekengvol14iss2pp167-174",

language = "Indonesian",

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publisher = "Universitas Pattimura",

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AU - Halistin,

AU - Ratnasari, Vita

AU - Rahayu, Santi Puteri

AU - Patih, Tandri

PY - 2020/6/1

Y1 - 2020/6/1

N2 - 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.

AB - 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.

KW - BFGS

KW - Gauss Hermite Quadrature

KW - Panel Data

KW - Probit

UR - https://www.scopus.com/pages/publications/105014196857

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DO - 10.30598/barekengvol14iss2pp167-174

M3 - Article

AN - SCOPUS:105014196857

SN - 1978-7227

VL - 14

SP - 167

EP - 174

JO - Barekeng

JF - Barekeng

IS - 2

ER -

ESTIMASI PARAMETER MODEL PROBIT PADA DATA PANEL MENGGUNAKAN OPTIMASI BFGS

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Keywords

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