scholarly journals ANALISIS KETERKAITAN A NTAR KOMODITAS PROTEIN DENGAN MENGGUNAKAN MODEL ALMOST IDEAL DEMAND SYSTEM (AIDS)

2013 ◽  
Vol 2 (3) ◽  
pp. 162
Author(s):  
Cesa Febri Desti ◽  
Dodi Devianto ◽  
Izzati Rahmi HG
Keyword(s):  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Demand System ◽  
Almost Ideal Demand System ◽  
Unrelated Regression

Penelitian ini bertujuan untuk melihat keterkaitan antar harga komoditas protein dengan menggunakan model Almost Ideal Demand System (AIDS).Objek penelitian adalah mahasiswa matematika Pasca Sarjana Universitas Andalas Padang yangmengkonsumsi komoditi sumber protein hewani meliputi : daging, ayam dan telur. Pendugaan parameter menggunakan metode Generalized Least Square (GLS) melalui persamaan Seemingly Unrelated Regression (SUR). Hasil penelitian menunjukkan proporsikonsumsi pangan yang dominan adalah komoditas ayam sebesar 0.409. Nilai elastisitas harga permintaan untuk ketiga komoditi memiliki tanda negatif, ini berarti bahwaketiga komoditi merupakan kebutuhan pokok. Elastisitas pendapatan bertanda positif,mengindikasikan bahwa ketiga komoditi adalah barang normal. Pada umumnya elastistasharga silang bertanda positif, mengindikasikan bahwa antar komoditi pangan memilikihubungan saling menggantikan.

Spending Behavior of Thai Tourists in Dan Sai District, Loei province, Thailand: Seemingly Unrelated Regression Estimation Analysis

2019 ◽  
Vol 9 (5) ◽  
pp. 1062
Author(s):  
Sakkarin NONTHAPOT ◽  
Thanet WATTANAKUL ◽  
Kitiya WANGKEEREE
Keyword(s):  
Marketing Strategies ◽  
Seemingly Unrelated Regression ◽  
Demand System ◽  
Price Strategy ◽  
Regression Estimation ◽  
Travel Costs ◽  
Unrelated Regression ◽  
Food And Beverage ◽  
Spending Behavior ◽  
The Cost

The purpose of this study is to investigate the spending behavior of Thai tourists in Dan Sai District, Loei province, Thailand by applying the Almost Ideal Demand System (AIDS) model. The analysis was conducted by employing Seemingly Unrelated Regression Estimation (SURE). A questionnaire was used to collect data and 400 samples were collected. The results revealed that the price factor influences the cost of accommodation on other expenses, food and beverage expenses on other expenses and travel expenses on other expenses while the revenue factor influences travel costs on other expenses and the cost of souvenirs on other expenses. The results of the study show that there should be measures to stimulate tourist spending using price strategy to encourage tourists to spend more, and marketing strategies should be used to encourage high-income tourists to spend in Dan Sai District.

scholarly journals Kesenjangan Upah Antargender di Indonesia: Bukti Empiris di Sektor Manufaktur

2018 ◽  
pp. 1-21
Author(s):  
Martesa Husna Laili ◽  
Arie Damayanti
Keyword(s):  
Manufacturing Sector ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Wage Discrimination ◽  
Firm Level ◽  
Least Square Estimator ◽  
Exporting Firms ◽  
Unrelated Regression ◽  
Level Data ◽  
And Gender

Theoretically, in the labor market without discrimination, wages should be paid according to productivity. Unlike other studies that use worker level data, this study will identify gender wage discrimination using firm-level data. Using Industrial Survey Data in 1996 and 2006, the gender wage ratio and gender productivity ratio were estimated simultaneously using the nonlinear seemingly unrelated regression (NLSUR) with least square estimator. We find that there is wage discrimination against women in the manufacturing sector. After disaggregating the firms by trade orientation, we show that wage discrimination against women occurs in non-exporting firms. While in exporting firms there is no wage discrimination. ========================= Secara teori, di pasar kerja yang tidak ada diskriminasi, seharusnya upah dibayar sesuai dengan produktivitas. Berbeda dengan penelitian lain yang menggunakan data level pekerja, penelitian ini akan mengidentifikasi diskriminasi upah antargender dengan menggunakan data di level perusahaan. Dengan menggunakan data Industri Besar dan Sedang tahun 1996 dan 2006, rasio upah gender dan rasio produktivitas gender diestimasi secara simultan menggunakan metode non-linear seemingly unrelated regression (NLSUR) dengan estimator least square. Penelitian ini menemukan bukti ada diskriminasi upah terhadap perempuan di sektor manufaktur. Setelah mendisagregasi perusahaan berdasarkan status ekspor, diskriminasi upah terhadap perempuan ditemukan di perusahaan non-eksportir, sedangkan di perusahaan eksportir tidak ditemukan diskriminasi upah.

scholarly journals Pola Permintaan Daging Tingkat Rumah Tangga di Indonesia: Analisa Data Mikro 2013

2015 ◽  
Vol 16 (1) ◽  
pp. 47-58 ◽  
Author(s):  
Sigit Nugroho ◽  
Sita Wardhani Suparyono
Keyword(s):  
Urban Education ◽  
Least Square ◽  
Demand Elasticity ◽  
Demand System ◽  
Almost Ideal Demand System ◽  
Household Demand ◽  
Meat Demand ◽  
Demand Pattern ◽  
Economic Survey ◽  
Linear Least Square

AbstractThe objective of this study is to understand the meat demand pattern of household in Indonesia. This study used the 2013 National Socio-Economic Survey (SUSENAS) and Agriculrural Census (SP). The method used to understand the meat demand pattern is Quadratic Almost Ideal Demand System (QUAIDS) approach with Iterated Linear Least Square (ILLS) estimator. The result shows that urban, education, household size, and domestic meat supply have an influence on meat demand pattern and its elasticity. Keywords: Quadratic Almost Ideal Demand System (QUAIDS); Household Demand; Meat; Demand Elasticity AbstrakTujuan penelitian ini adalah untuk mempelajari pola permintaan daging di Indonesia. Penelitian ini menggunakan data survei rumah tangga SUSENAS 2013 dan Sensus Pertanian 2013 yang dikeluarkan oleh Badan Pusat Statistik. Penelitian ini menggunakan pendekatan Quadratic Almost Ideal Demand System (QUAIDS) dengan estimator Iterated Linear Least Square (ILLS). Hasil analisis menunjukkan bahwa terdapat pengaruh kota, pendidikan, ukuran rumah tangga, kelas pendapatan, dan suplai daging domestik terhadap pola permintaan dan elastisitas daging. 

PENERAPAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (SGSTAR SUR) PADA PERAMALAN HASIL PRODUKSI PADI

2021 ◽  
Vol 10 (2) ◽  
pp. 241-249
Author(s):  
Leni Pamularsih ◽  
Mustafid Mustafid ◽  
Abdul Hoyyi
Keyword(s):  
Rice Yield ◽  
Space Time ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Training Data ◽  
Seasonal Effect ◽  
Validation Data ◽  
Central Java ◽  
Unrelated Regression ◽  
Inverse Distance

Ordinary Least Square (OLS) is general method to estimate Generalized Space Time Autoregressive (GSTAR) parameters. Parameter estimation by using OLS for GSTAR model with correlated residuals between equations will produce inefficient estimators. The method that appropriate to estimate the parameter model with correlated residuals between equations is Generalized Least Square (GLS), which is usually used in Seemingly Unrelated Regression (SUR). This research aims to build the seasonal GSTAR SUR model as model of rice yield forecasting in three locations by using the best weighting. Weights used are binary weights, inverse distance and normalization of cross correlation. Data which used in this research are the data of rice yield per quarter in three districts in Central Java, namely Banyumas, Cilacap and Kebumen. The data from the period of January 1981 to December 2014 as training data and the period of January 2015 to December 2018 as validation data. The resulting is a model that has a seasonal effect with the autoregressive order and the spasial order limited to 1 so the model formed is SGSTAR (41)-I(1)(1)3. The best model produced is the SGSTAR SUR (41)-I(1)(1)3 model with inverse distance weighting because it fulfills both assumptions, residuals white noise and residuals normally multivariate distribution. Additionally, it has the smallest MAPE value when compared the other weighting, that is 20%. This MAPE value indicates  that the accuracy rate of forecast is accurate.Keywords: Rice yield, Seasonal, GSTAR, SUR.

scholarly journals PERMINTAAN BUAH-BUAHAN RUMAHTANGGA DI PROPINSI LAMPUNG

2016 ◽  
Vol 4 (2) ◽  
pp. 137
Author(s):  
Rini Desfaryani ◽  
Sri Hartoyo ◽  
Lukytawati Anggraeni
Keyword(s):  
Income Elasticity ◽  
Regression Method ◽  
Seemingly Unrelated Regression ◽  
System Model ◽  
Demand System ◽  
Positive Sign ◽  
Unrelated Regression ◽  
Cross Price Elasticity ◽  
Household Members ◽  
Demand Changes

<em>One of commodity which is important to be fulfilled is fruit. From producer side, it has been available much in Lampung Province but has not been utilized optimally by consumer. This study purposed to analyze the factors influencing fruit demand and the response of demand changes due to shifting of price and income. The analysis was conducted by using AIDS (Almost Ideal Demand System) model and the parameter was estimated by using SUR (Seemingly Unrelated Regression) method. The results stated that the fruit demand was influenced by the fruit price (either own-price or cross-price), expenditure, and the number of household members. The elasticity shows that the own-price for all kinds of fruit is inelastic. The cross-price elasticity indicates that there are some fruits that have substitution or complementary relationship among them and in all types of fruits, income elasticity has positive sign.</em>

Pengaruh PDRB, Tingkat Pengangguran Terbuka dan UMR Terhadap Indeks Pembangunan Manusia di Kabupaten dan Kota Provinsi Jawa Timur

Bharanomics ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 33-46
Author(s):  
Lintang Sania ◽  
Mohammad Balafif ◽  
Nurul Imamah
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Fixed Effect ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression

Penelitian ini bertujuan untuk membuktikan apakah Pengaruh Produk Domestik Regional Bruto, Tingkat Pengangguran Terbuka Dan Upah Minimum Regional Terhadap Indeks Pembangunan Manusia Di Kabupaten Dan Kota Provinsi Jawa Timur Tahun 2014-2019. Pengambilan data menggunakan data sekunder diambil melalui website BPS Jawa Timur, yaitu data PDRB, Tingkat Pengangguran Terbuka dan Indeks Pembangunan Manusia Tahun 2014-2019, sedangkan data UMR diambil melalui Surat Keputusan Gubernur Jawa Timur Tahun 2014-2019. Metode analisis dalam penelitian ini menggunakan analisis regresi data panel yang merupakan gabungan antara data time series dan cross section yang dianalisis dengan Model Fixed Effect (FEM) dengan penimbang Feasible Generalized Least Square-Seemingly Unrelated Regression (FGLS-SUR) yang diolah dengan aplikasi EViews 9.0 diperoleh persamaan regresi IPM = -27.22579 + 3.380970*LNPDRBit + (-0.035903)*TPTit + 4.433382*LNUMRit . Hasil penelitian ini menunjukkan bahwa variabel independen produk domestik regional bruto dan upah minimum regional berpengaruh positif signifikan terhadap Indeks Pembangunan Manusia di Provinsi Jawa Timur.Sedangkan untuk variabel tingkat pengangguran terbuka berpengaruh negatif signifikan terhadap Indeks Pembangunan Manusia.

scholarly journals PENERAPAN GSTAR-SUR PADA JUMLAH PENUMPANG PESAWAT DOMESTIK DI BANDARA INDONESIA

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Gilang Habibie, Yundari, Hendra Perdana
Keyword(s):  
Space Time ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Ordinary Least Square ◽  
Unrelated Regression

Generalized space time autoregressive (GSTAR) adalah model ruang waktu yang banyak digunakan di Indonesia. Sebagian besar penelitian model GSTAR menggunakan ordinary least square (OLS) untuk mengestimasi parameter. Namun, estimasi dengan metode OLS pada model GSTAR dengan residual saling berkorelasi akan menghasilkan estimator yang tidak efisien terutama pada data musiman. Metode estimasi yang sesuai untuk residual yang saling berkorelasi adalah generalized least square (GLS), yang biasa digunakan dalam model seemingly unrelated regression (SUR). Penelitian ini bertujuan untuk menganalisis model GSTAR-SUR dan membandingkannya dengan GSTAR-OLS dengan bobot seragam dan jarak. Data yang digunakan adalah data jumlah penumpang pesawat domestik setiap bulan di Bandara Polonia/Kualanamu, Soekarno-Hatta, Juanda dan Ngurah Rai dari Januari 2006 hingga September 2019. Hasil estimasi parameter GSTAR-SUR dengan bobot seragam adalah Polonia/Kualanamu (∅10=-0,494; ∅11=0,046), Soekarno-Hatta (∅10=-0,300; ∅11=-0,828), Juanda (∅10=-0,451; ∅11=0,033) dan Ngurah Rai (∅10=-0,198; ∅11=-0,019). Sedangkan GSTAR-SUR dengan bobot jarak menghasilkan estimasi Polonia/Kualanamu (∅10=-0,492; ∅11=0,026), Soekarno-Hatta (∅10=-0,292; ∅11=-1,186), Juanda (∅10=-0,455; ∅11=0,058) dan Ngurah Rai (∅10=-0,211; ∅11=0,017). Berdasarkan nilai MAPE GSTAR-SUR lebih baik dari GSTAR-OLS dengan nilai MAPE untuk model GSTAR-OLS adalah 12,90% pada bobot seragam dan 13,43% pada bobot jarak. Model GSTAR-SUR menghasilkan nilai MAPE 6,65% untuk bobot seragam dan 7,06% untuk bobot jarak. Model terbaik adalah GSTAR-SUR bobot seragam dengan nilai MAPE 6,65%. Kata Kunci : OLS, GLS, spacetime, korelasi eror

scholarly journals On Seemingly Unrelated Regression and Single Equation Estimators Under Heteroscedastic Error and Non-Gaussian Responses

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Rasaq B Afolayan ◽  
Alabi W Banjoko ◽  
Mohammad K Garba ◽  
Waheed B Yahya
Keyword(s):  
Small Sample ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Response Model ◽  
Sample Sizes ◽  
Contemporaneous Correlation ◽  
Heteroscedastic Error ◽  
Unrelated Regression ◽  
Error Terms ◽  
Non Gaussian

This study investigated the efficiency of Seemingly Unrelated Regression (SUR) estimator of Feasible Generalized Least Square (FGLS) compared to robust MM-BISQ, M-Huber, and Ordinary Least Squares (OLS) estimators when the variances of the error terms are non-constant and the distribution of the response variables is not Gaussian. The finite properties and relative performance of these other estimators to OLS were examined under four forms of heteroscedasticity of the error terms, levels of Contemporaneous Correlation (Cc) with gamma responses. The efficiency of four estimation techniques for the SUR model was examined using the Root Mean Square Error (RMSE) criterion to determine the best estimator(s) under different conditions at various sample sizes. The simulation results revealed that the SUR estimator (FGLS) showed superior performance in the small sample situations when the contemporaneous correlation ( ) is almost perfect ( =0.95) with the gamma response model while MM-BISQ was the best under low contemporaneous correlation. The relative efficiencies of MM-BISQ, M-Huber and FGLS estimators over the OLS are respectively 89%, 71%, and 14% in a small sample 30) and 49%, 32% and 1% in large sample sizes  under gamma response model. The study concluded that MM-BISQ and M-Huber estimators are the most efficient estimators for modeling systems of simultaneous equations with non-Gaussian responses under either homoscedastic or multiplicative heteroscedastic error terms irrespective of the sample size.Keywords—, Contemporaneous correlation, Feasible Generalized Least Square, Heteroscedasticity, Homoscedasticity, Seemingly unrelated Regression. 

scholarly journals PEMODELAN INDEKS HARGA KONSUMEN DI JAWA TENGAH DENGAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR)

2018 ◽  
Vol 7 (4) ◽  
pp. 337-347
Author(s):  
Mega Fitria Andriyani ◽  
Abdul Hoyyi ◽  
Hasbi Yasin
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Estimation Method ◽  
Space Time ◽  
Least Square ◽  
Seemingly Unrelated Regression ◽  
Residual Correlation ◽  
Central Java ◽  
Previous Time ◽  
Unrelated Regression

The Generalized Space Time Autoregressive (GSTAR) model with Seemingly Unrelated Regression (SUR) estimation method or often called GSTAR-SUR is more efficient to be used for residual correlation than Ordinary Least Square (OLS) estimation method. The SUR estimation method utilizes residual correlation information to improve the estimated efficiency resulting in a smaller standard error. The purpose of this research is to get the GSTAR-SUR model according to Consumer Price Index (CPI) data in four regencies or cities in Central Java namely Purwokerto, Surakarta, Semarang, and Tegal. Based on the assumed white noise assumption, the smallest MAPE and RMSE averages, the best model chosen in this research is the GSTAR-SUR(11)I(1) model with the heavy of normalized cross-correlation with the average MAPE value of 0.4455% and RMSE value of 0.80582. The best model obtained explains that the CPI data in Purwokerto, Semarang, and Tegal not only influenced by the previous time but also influenced by the locations. Meanwhile, the CPI data in Surakarta is only influenced by the previous time, but it is not affected by other locations. Keywords: SUR, OLS, Consumer Price Index

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