PENERAPAN SEEMINGLY UNRELATED REGRESSION (SUR) MENGGUNAKAN METODE ESTIMASI GENERALIZED LEAST SQUARE (GLS) PADA DATA INDEKS HARGA KONSUMEN (IHK) DI KOTA CILACAP, PURWOKERTO DAN KUDUS PROVINSI JAWA TENGAH TAHUN 2020-2022

Anindya Hestyning Sekar, NIM.: 20106010015 (2024) PENERAPAN SEEMINGLY UNRELATED REGRESSION (SUR) MENGGUNAKAN METODE ESTIMASI GENERALIZED LEAST SQUARE (GLS) PADA DATA INDEKS HARGA KONSUMEN (IHK) DI KOTA CILACAP, PURWOKERTO DAN KUDUS PROVINSI JAWA TENGAH TAHUN 2020-2022. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Regression analysis is a statistical method that aims to analyze the effect of one or more independent variables (X) on a dependent variable (Y). One example is the multivariate regression model, which in its modeling often uses panel data. Panel data is a combination of cross-section data and time-series data. However, in multivariate models the data used often violates classical assumption tests, such as autocorrelation. This can be solved using a linear regression model known as Seemingly Unrelated Regression (SUR). The SUR model has its own parameter values and it looks like each equation is Seemingly Unrelated, but between the equations there is a correlation between errors that can be solved by the Generalized Least Square (GLS) method. This study aims to conduct SUR modeling with the GLS method on the value of the Consumer Price Index (CPI) in the Cilacap, Purwokerto and Kudus Cities in 2020-2022 by considering the factors that affect the CPI, namely the Food Commodity, the Health Commodity and the Transportation Commodity. The three commodities contribute quite a lot, namely the SUR model produces a coefficient of determination (

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