Estimation Of Parameter Regression Panel Data Model Using Least Square Dummy Variable Method

Authors

  • NUR AMINAH AHMAD HASANUDDIN OF UNIVERSITY
  • Raupong Raupong Hasanuddin University

DOI:

https://doi.org/10.20956/j.v20i1.27530

Keywords:

Panel Data Regression, Least Square Dummy Variable Method, Human Development Index

Abstract

Panel data regression is a set of techniques for modeling the effect of independent variable on the dependent variable of panel data. The parameter estimation in the panel data regression model used the least squares method, but the difference between the intercept and the slope could not be known between time and between cross-section. One of the methods used is the Least Square Dummy Variable method (LSDV). The LSDV method is a method that has the same stages as the least squares method, but uses dummy variable to get different intercept score. This research uses the LSDV method to explain the differences in intercept between cross-sections using balanced panel data, namely the Human Development Index (HDI) data in South Sulawesi 2011-2017 to get fixed effect panel data regression model parameters on that data and the regencies with Average Length of School (ALS) and Life Expectancy (LE) variable that has the most influence on HDI based on the coefficient of determination criteria. According to the results of this research, the score of the coefficient of determination in the panel data regression model using the fixed effect model in each cross-section (regency), there are also three regencies with the highest coefficient of determination, respectively, Gowa, Pare-pare and Bantaeng regency that ALS and LE are able to explain the HDI variables 98.942%, 98.089% and 97.444%.

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Published

2023-09-06

How to Cite

AHMAD, N. A., & Raupong, R. (2023). Estimation Of Parameter Regression Panel Data Model Using Least Square Dummy Variable Method. Jurnal Matematika, Statistika Dan Komputasi, 20(1), 221- 228. https://doi.org/10.20956/j.v20i1.27530

Issue

Vol. 20 No. 1 (2023): SEPTEMBER, 2023

Section

Research Articles

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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