Estimasi Model Fixed Effect Pada Analisis Regresi Data Panel Dengan Metode Least Square Dummy Variable (LSDV)
Abstract
Data panel merupakan gabungan antara data cross section dan data time series. Salah satu model analisis regresi data panel adalah model fixed effect. Model fixed effect mempunyai asumsi bahw intersep berbeda untuk setiap individu, tetapi koefisien slope konstan. Estimasi dilakukan dengan menggunakan variabel dummy untuk menjelaskan adanya perbedaan intersep antar individu. Penelitian ini bertujuan untuk mengestimasi model fixed effect pada analisis regresi data panel dengan metode least square dummy variable dan menerapkannya pada data upah minimum provinsi di Indonesia tahun 2014-2017. Berdasarkan hasil penelitian yang telah dilakukan dengan menggunakan estimasi parameter = untuk model fixed effect pada analisis regresi data panel upah minimum provinsi di Indonesia diperoleh model sebagai berikut, = 5.248452+ + 0.007415 + 0.002882 + 1.63E-07dengan, = upah minimum provinsi, = indeks harga konsumen, = tingkat partisipasi angkatan kerja, = produk domestik regional bruto dan = variabel dummy, k = 1,2, ...,33 (provinsi).
Panel data is a combination of cross section data and time series data. One of panel data regression analysis model is the fixed effect model. The fixed effect model has the assumption that intercepts are different for each individual, but the slope coefficient is constant. Estimation is done by using dummy variables to explain the existence of intercept differences between individuals. This study aims to estimate the fixed effect model in panel data regression analysis using the least square dummy variable method and apply it to the provincial minimum wage data in Indonesia in 2014-2017. Based on the results of the research that has been done by using paremeter estimator = for fixed effect model in the panel regression analysis on provincial minimum wage data in Indonesia, we obtained as follows, = 5.248452+ + 0.007415 + 0.002882 + 1.63E-07 with, = provincial minimum wage, = consumer price index, = labor force participation rate, = regional gross domestic product, = dummy variable , k = 1,2, ...,33 (province).
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