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 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 Seemingly Unrelated Regression Equations for Developing a Pavement Performance Model

2015 ◽  
Vol 9 (13) ◽  
pp. 199 ◽  
Author(s):  
Ciro Caliendo ◽  
Maurizio Guida ◽  
Emiliana Pepe
Keyword(s):  
Statistical Significance ◽  
Performance Model ◽  
Seemingly Unrelated Regression ◽  
Likelihood Method ◽  
Pavement Performance ◽  
Joint Analysis ◽  
Ratio Test ◽  
Regression Equations ◽  
Unrelated Regression ◽  
Error Terms

<p>The paper presents a joint analysis of some pavement performance indicators based on a system of seemingly unrelated regression equations (SURE) which allows to handle correlated error terms. In particular, three major indicators such as the side friction coefficient (SFC20°C), mean-profile depth (MPD), and international roughness index (IRI), were measured in a case study and subsequently used in analysis. Regression parameters were estimated by the Maximum Likelihood Method and the t-statistic was considered to show the statistical significance of regression coefficients. The results show that estimation points have the signs expected: the SFC<sub>20°C</sub> decreases as the number of accumulated trucks (<em>N</em><sub>t</sub>) increases; whereas the MPD and IRI increase as the number of trucks increases. A likelihood ratio test was also carried out to determine whether the system model, which assumes correlation among error terms, was more appropriate than separate models. In this particular case, with three degrees of freedom, was found that the result corresponds to a p-value 0.150 and the null hypothesis cannot be rejected at any significance level less than this value.</p>

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.

A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections

2002 ◽  
Vol 10 (1) ◽  
pp. 49-65 ◽  
Author(s):  
John E. Jackson
Keyword(s):  
Regression Model ◽  
Small Sample ◽  
Seemingly Unrelated Regression ◽  
Monte Carlo Experiments ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Small Sample Properties ◽  
Distributional Assumptions ◽  
Number Of Parties ◽  
Stochastic Element

This paper develops an estimator for models of election returns in multiparty elections. It shares the same functional formas the Katz—King estimator but is computationally simpler, can be used with any number of parties, and is based on more conventional distributional assumptions. Small sample properties of the estimator are derived, which makes it particularly useful in many of the applications where there are a relatively small number of voting districts. The distributional assumptions are contained in two elements. The first treats the observed votes as the outcomes resulting from sampling the voters in each district. The second stochastic element arises from the usual treatment of the stochastic term in a regression model, namely, the inability of the included variables and the linear form to match the underlying process perfectly. The model is then used to analyze the 1993 Polish parliamentary elections. The results from this analysis are used to develop Monte Carlo experiments comparing several different yet feasible estimators. The conclusion is that a number of accessible estimators, including the standard seemingly unrelated regression model and the Beck-Katz model with panel-corrected standard errors, are all good choices.

scholarly journals The Magnitudes of Economic and Non-Economic Factors on the Demand for U.S. Domestic Air Travel

2014 ◽  
Author(s):  
Ju Dong Park ◽  
Won W. Koo
Keyword(s):  
Air Travel ◽  
Seemingly Unrelated Regression ◽  
Economic Factors ◽  
Factors Affecting ◽  
Correlated Error ◽  
Unrelated Regression ◽  
Demand Models ◽  
Error Terms ◽  
Passenger Behavior ◽  
Relationship Of

The primary purpose of this study is to analyze air carriers’ behavior in capturing market share by examining the economic factors affecting passenger behavior toward air travel. This study also examines non-economic factors such as seasonality, unexpected events (9/11 attack), mergers, and trends. Because the airlines included in this study compete with each other, seemingly unrelated regression estimation (SURE) is used to estimate the parameters of the demand models which have correlated error terms. The economic and statistical relationship of the factors with air passenger miles provides valuable information to understand the nature of the demand for the U.S. air passenger industry. In examining demand determinants, this study concludes that air fare, income, seasonality, and mergers play significant roles in determining the demand for air passengers.

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 Effects of Correlation between the Error Term and Autocorrelation on Some Estimators in a System of Regression Equations

2020 ◽  
pp. 57-75
Author(s):  
Olanrewaju, Samuel Olayemi
Keyword(s):  
Regression Model ◽  
Time Series Data ◽  
Equation Model ◽  
Seemingly Unrelated Regression ◽  
Series Data ◽  
Regression Equations ◽  
Joint Influence ◽  
Unrelated Regression ◽  
Correlation Problem ◽  
Error Terms

Seemingly unrelated regression model developed to handle the problem of correlation among the error terms of a system of the regression equations is still not without a challenge, where each regression equation must satisfy the assumptions of the standard regression model. When dealing with time-series data, some of these assumptions, especially that of independence of the regressors and error terms leading to multicollinearity and autocorrelation respectively, are often violated. This study examined the effects of correlation between the error terms and autocorrelation on the performance of seven estimators and identify the estimator that yields the most preferred estimates under the separate or joint influence of the two correlation effects considered by the researcher. A two-equation model was considered, in which the first equation had multicollinearity and autocorrelation problems while the second one had no correlation problem. The error terms of the two equations were also correlated. The levels of correlation between the error terms and autocorrelation were specified between -1 and +1 at interval of 0.2 except when it approached unity.

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 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.

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