Savitri, Nadia (2018) Pemodelan Data Panel Pengangguran Di Jawa Timur Menggunakan Generalized Linear Mixed Models. Magister thesis, Universitas Brawijaya.
Abstract
Pengangguran adalah suatu kondisi di mana seseorang yang berada pada angkatan kerja namun tidak bekerja dan masih mencari pekerjaan. Angka pengangguran di Jawa Timur dapat dipandang sebagai data panel. Hasil kombinasi data cross-section dan data deret waktu. Akan tetapi data yang diamati pada selang waktu tertentu secara berulang, maka antar waktu akan memiliki data yang berkorelasi atau saling tak bebas. Pengangguran diamati dari tahun ke tahun, maka antar data juga saling berkorelasi. Jika peubah respon menyatakan banyak pengangguran pada kota atau kabupaten di Jawa Timur pada tahun 2006 sampai dengan 2015 mengikuti sebaran yang termasuk dalam keluarga eksponensial dan mengandung autokorelasi. Verbekke dan Molenberghs (2005) mengusulkan Generalized Linear Mixed Models (GLMM) untuk mengatasi data yang mengandung autokorelasi dan peubah respon tidak menyebar normal. Penelitian ini bertujuan untuk menganalisis data panel pengangguran di Jawa Timur dengan Generalized Linear Mixed Model, meramalkan jumlah pengangguran dan identifikasi pengaruh peubah prediktor terhadap jumlah pengangguran. Pada Generalized Linear Mixed Models terdapat efek tetap dan efek acak. Pendugaan terhadap parameter model menggunakan metode Maximum Likelihood (ML) untuk pendugaan efek tetap dan Restricted Maximum Likelihood (REML) untuk pendugaan efek acak. Pada pemodelan banyak angkatan kerja, besar UMK, pertumbuhan ekonomi, PDRB , inflasi dan Kabupaten atau Kota merupakan efek tetap dan tahun merupakan efek acak. Peubah banyak angkatan kerja, UMK, pertumbuhan ekonomi, dan inflasi berpengaruh terhadap banyak pengangguran di Jawa Timur. Hasil analisis menunjukkan bahwa GLMM dapat digunakan untuk menganalisis angka pengangguran di Jawa Timur.
English Abstract
Unemployment is a condition in which a person is in the labor force but does not work and is still looking for a job. Unemployment data in East Java can be viewed as panel data. The result of a combination of cross-section data and time series data. However, the observed data at certain intervals repeatedly, then between the times will have correlated or mutually independent data. Unemployment is observed from year to year, then inter-data also correlate each other. If the response variable states that many unemployed cities or districts in East Java from 2006 to 2015 follow the distribution included in the exponential family and contain autocorrelation. Verbekke and Molenberghs (2005) proposed Generalized Linear Mixed Models (GLMM) to overcome data containing autocorrelation and the response variable did not spread normally. This study aims to analyze unemployment panel data in East Java with the Generalized Linear Mixed Model, forecasting the number of unemployed and identifying the influence of predictor variables on the number of unemployed. In Generalized Linear Mixed Models there are fixed effects and random effects. Estimation of model parameters using Maximum Likelihood (ML) method for estimating fixed effect and Restricted Maximum Likelihood (REML) for estimating random effects. In the modeling of many labor forces, large MSEs, economic growth, GRDP, inflation and District or City are fixed and yearly effects are random effects. The variable of many labor force, MSE, economic growth, and inflation affect many unemployment in East Java. The analysis results show that GLMM can be used to analyze unemployment in East Java.
| Item Type: | Thesis (Magister) |
|---|---|
| Identification Number: | ES/519.536/SAV/p/2018/041802368 |
| Uncontrolled Keywords: | UNEMPLOYMENT, UNEMPLOYMENT - STATISTICAL METHODS, LINESR MODELS (statistics) |
| Subjects: | 500 Natural sciences and mathematics > 519 Probabilities and applied mathematics > 519.5 Statistical mathematics > 519.53 Descriptive statistics, multivariate analysis, analysis of variance and covariance > 519.536 Regression analysis |
| Divisions: | S2/S3 > Magister Statistika, Fakultas MIPA |
| Depositing User: | Nur Cholis |
| Date Deposited: | 14 May 2018 07:03 |
| Last Modified: | 21 Oct 2021 07:31 |
| URI: | http://repository.ub.ac.id/id/eprint/10485 |
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