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R, Aditya Setyawan, Mustika Hadijati, and Ni Wayan Switrayni. "Analisis Masalah Heteroskedastisitas Menggunakan Generalized Least Square dalam Analisis Regresi." EIGEN MATHEMATICS JOURNAL 1, no. 2 (December 31, 2019): 61. http://dx.doi.org/10.29303/emj.v1i2.43.
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Regression analysis is one statistical method that allows users to analyze the influence of one or more independent variables (X) on a dependent variable (Y).The most commonly used method for estimating linear regression parameters is Ordinary Least Square (OLS). But in reality, there is often a problem with heteroscedasticity, namely the variance of the error is not constant or variable for all values of the independent variable X. This results in the OLS method being less effective. To overcome this, a parameter estimation method can be used by adding weight to each parameter, namely the Generalized Least Square (GLS) method. This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis and examine the comparison of estimation results using the OLS method with the GLS method in the case of heteroscedasticity.The results show that the GLS method was able to maintain the nature of the estimator that is not biased and consistent and able to overcome the problem of heteroscedasticity, so that the GLS method is more effective than the OLS method.
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Rashid, Intan Maizura Abd. "Determinants of FDI Inflows in Agriculture Sector Using Pooled Ordinary Least Square (OLS), Pooled Generalized Least Square (GLS), Augmented Dickey-Fuller (ADF) and Philips-perron Unit Root Test." International Journal of Psychosocial Rehabilitation 24, no. 5 (April 20, 2020): 2560–67. http://dx.doi.org/10.37200/ijpr/v24i5/pr201955.
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Angriany, A. Muthiah Nur, Georgina Maria Tinungki, and Raupong Raupong. "Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 20, 2018): 54. http://dx.doi.org/10.20956/jmsk.v15i2.5714.
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AbstractsExperiment design is a test or a row of test by using both statistical description and inference statistical. The aim of this test is to change an input to become an output as a respond of the experiment. In the experiment design, variance of factor A, B , AB error of variance are called as variant component. The aim of this study is to estimate variance component on complete random factorial design for fixed model and mixed model by using Generalized Least Squares (GLS)method, where GLS method as a development of Ordinary Least Square method. It used to be applied on data of complete random factorial design, namely like the influence to density pelleting food which is caused by increasing adhesive material and longtime in storage. The results show that there is no influence of increasing adhesive material to the density of pelleting food. In addition, there exist of diversity of longtime of storage and there exists a diversity interaction between adding adhesive material and long of time of storage to the density of pelleting food Keywords: Generalized Least Squares, variance component, complete random factorial design AbstrakPerancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet. Kata kunci: Generalized Least Squares, komponen variansi, rancangan faktorial acak lengkap
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Yuan, Ke-Hai, and Yutaka Kano. "Meta-Analytical SEM: Equivalence Between Maximum Likelihood and Generalized Least Squares." Journal of Educational and Behavioral Statistics 43, no. 6 (August 13, 2018): 693–720. http://dx.doi.org/10.3102/1076998618787799.
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Meta-analysis plays a key role in combining studies to obtain more reliable results. In social, behavioral, and health sciences, measurement units are typically not well defined. More meaningful results can be obtained by standardizing the variables and via the analysis of the correlation matrix. Structural equation modeling (SEM) with the combined correlations, called meta-analytical SEM (MASEM), is a powerful tool for examining the relationship among latent constructs as well as those between the latent constructs and the manifest variables. Three classes of methods have been proposed for MASEM: (1) generalized least squares (GLS) in combining correlations and in estimating the structural model, (2) normal-distribution-based maximum likelihood (ML) in combining the correlations and then GLS in estimating the structural model (ML-GLS), and (3) ML in combining correlations and in estimating the structural model (ML). The current article shows that these three methods are equivalent. In particular, (a) the GLS method for combining correlation matrices in meta-analysis is asymptotically equivalent to ML, (b) the three methods (GLS, ML-GLS, ML) for MASEM with correlation matrices are asymptotically equivalent, (c) they also perform equally well empirically, and (d) the GLS method for SEM with the sample correlation matrix in a single study is asymptotically equivalent to ML, which has being discussed extensively in the SEM literature regarding whether the analysis of a correlation matrix yields consistent standard errors and asymptotically valid test statistics. The results and analysis suggest that a sample-size weighted GLS method is preferred for combining correlations and for MASEM.
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Angriany, A. Muthiah Nur, Georgina Maria Tinungki, and Raupong Raupong. "Estimasi Komponen Variansi pada Rancangan Faktorial Acak Lengkap Menggunakan Metode Generalized Least Squares." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (December 6, 2018): 52. http://dx.doi.org/10.20956/jmsk.v15i2.5569.
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Perancangan percobaan adalah suatu uji atau sederet uji baik itu menggunakan statistika deskripsi maupun statistika inferensi, yang bertujuan untuk mengubah peubah input menjadi suatu output yang merupakan respon dari percobaan tersebut. Dalam perancangan percobaan, variansi dari faktor A, variansi dari faktor B, variansi interaksi faktor AB, dan variansi galat disebut dengan komponen varian. Penelitian ini bertujuan untuk mengestimasi komponen variansi pada rancangan faktorial acak lengkap model tetap dan model campuran menggunakan metode Generalized Least Squares (GLS), dimana metode GLS adalah pengembangan dari metode Ordinary Least Square yang biasa digunakan untuk mengatasi asumsi homogenitas yang biasa dilanggar dalam perancangan percobaan. Metode tersebut diterapkan pada data rancangan faktorial acak lengkap yaitu pengaruh berat jenis pakan pellet dengan kombinasi perlakuan penambahan bahan perekat dan lama penyimpanan. Hasil menunjukkan bahwa tidak terdapat pengaruh penambahan bahan perekat terhadap berat jenis pakan pellet. Selain itu, terdapat keragaman faktor lama penyimpan dan terdapat keragaman interaksi antara faktor penambahan perekat dan lama penyimpanan terhadap berat jenis pakan pellet.
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Ammari, Mohamed Lassaad, and Paul Fortier. "Analysis of MIMO Receiver Using Generalized Least Squares Method in Colored Environments." Journal of Computer Networks and Communications 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/720546.
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The classical detection techniques for multiple-input multiple-output (MIMO) systems are usually designed with the assumption that the additive complex Gaussian noise is uncorrelated. However, for closely spaced antennas, the additive noise is correlated due to the mutual antenna coupling. This letter analyzes an improved zero-forcing (ZF) technique for MIMO channels in colored environments. The additive noise is assumed to be correlated and the Rayleigh MIMO channel is considered doubly correlated. The improved ZF detector, based on the generalized least squares estimator (GLS), takes into account the noise covariance matrix and provides an unbiased estimator of the transmitted symbol vectors. We introduce some novel bounds on the achievable sum rate, on the normalized mean square error at the receiver output, and on the outage probability. The derived expressions are compared to Monte Carlo simulations.
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Prastuti, Mike, and Iis Dewi Ratih. "KAJIAN SIMULASI ESTIMASI PARAMETER MODEL GSTAR-GLS UNTUK DATA BERPOLA MUSIMAN." MEDIA BINA ILMIAH 13, no. 12 (July 23, 2019): 1769. http://dx.doi.org/10.33758/mbi.v13i12.261.
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Secara umum, metode untuk mengestimasi parameter dalam model GSTAR adalah Ordinary Least Squares (OLS). Estimasi parameter dengan menggunakan OLS untuk model GSTAR dengan residual yang berkorelasi akan menghasilkan estimator yang tidak efisien. Metode yang sesuai untuk mengestimasi parameter dengan residual yang berkorelasi adalah Generalized Least Square (GLS). Tujuan dari makalah ini adalah untuk mengusulkan metode GLS untuk mengestimasi parameter dalam model GSTAR Musiman, yang dikenal sebagai GSTAR-GLS, dan membandingkan hasilnya dengan metode OLS atau GSTAR-OLS. Selain itu, tujuan dari makalah ini adalah untuk mengekaji lebih lanjut dalam penentuan bobot spasial yang sesuai pada model GSTAR. Hasil studi simulasi menunjukkan bahwa penentuan bobot spasial pada model GSTAR dapat dilakukan secara optimal dengan menggunakan normalisasi hasil inferensi statistik terhadap parsial korelasi silang antar lokasi pada lag waktu yang bersesuaian. Selain itu, GSTAR-GLS menghasilkan estimator yang lebih efisien daripada GSTAR-OLS, dimana standard error yang dihasilkan lebih kecil
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Roch, Sebastien, and Karl Rohe. "Generalized least squares can overcome the critical threshold in respondent-driven sampling." Proceedings of the National Academy of Sciences 115, no. 41 (September 25, 2018): 10299–304. http://dx.doi.org/10.1073/pnas.1706699115.
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To sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov model for RDS, previous research has shown that if the typical participant refers too many contacts, then the variance of common estimators does not decay like O(n−1), where n is the sample size. This implies that confidence intervals will be far wider than under a typical sampling design. Here we show that generalized least squares (GLS) can effectively reduce the variance of RDS estimates. In particular, a theoretical analysis indicates that the variance of the GLS estimator is O(n−1). We then derive two classes of feasible GLS estimators. The first class is based upon a Degree Corrected Stochastic Blockmodel for the underlying social network. The second class is based upon a rank-two model. It might be of independent interest that in both model classes, the theoretical results show that it is possible to estimate the spectral properties of the population network from a random walk sample of the nodes. These theoretical results point the way to entirely different classes of estimators that account for the network structure beyond node degree. Diagnostic plots help to identify situations where feasible GLS estimators are more appropriate. The computational experiments show the potential benefits and also indicate that there is room to further develop these estimators in practical settings.
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Affandi, Affandi, and Eddy Gunawan. "PENGARUH EKSPOR, IMPOR DAN JUMLAH PENDUDUK TERHADAP PDB INDONESIA TAHUN 1969 -2016." JURNAL PERSPEKTIF EKONOMI DARUSSALAM 4, no. 2 (July 1, 2019): 249–64. http://dx.doi.org/10.24815/jped.v4i2.13021.
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This research aims to know the influence of export, import and population against Indonesia gross domestic product. The data used in this research is secondary data from the years 1969-2016 were sourced from a variety of reports and the compilation of the particular publication of the World Bank. The model used was multiple linear regression analysis method using the approach of Generalized Least Square parameter estimation (GLS). The results of calculations indicate that the variable is positive and significant effect of exports to GDP, population of Indonesia a negative and significant effect against Indonesia'S GDP, while imports of influential positive and insignificant to GDP Indonesian. The value of the coefficient of determination (R2 = 0.9464 adj.)show that Indonesia'S GDP amounted to 94.64 percent affected by the Export, import and Population, while the remaining 5.36 percent affected by factors other than this research.Keywords : Import, Export, Population, Gross Domestic Product, Generalized Least SquareAbstrakPenelitian ini bertujuan untuk mengetahui pengaruh ekspor, impor dan jumlah penduduk terhadap produk domestik bruto Indonesia. Data yang digunakan dalam penelitian ini adalah data sekunder dari tahun 1969-2016 yang bersumber dari berbagai laporan dan kompilasi khususnya publikasidari World Bank. Model yang digunakan adalah regresi linear berganda dengan metode analisis menggunakan pendekatan estimasi parameter Generalized Least Square (GLS). Hasil perhitungan menunjukkan bahwa variabel ekspor berpengaruh positif dan signifikan terhadap PDB Indonesia, jumlah penduduk berpengaruh negatif dan signifikan terhadap PDB Indonesia, sedangkan imporberpengaruh positif dan tidak signifikan terhadap PDB Indonesia. Nilai koefisien determinasi (Adj.R2= 0.9464) Menunjukkan bahwa PDB Indonesia sebesar 94,64 persen dipengaruhi oleh Ekspor, Impor dan Jumlah Penduduk, sedangkan sisanya 5,36 persen dipengaruhi oleh faktor-faktor lain diluar penelitian ini.Kata Kunci : Impor, Ekspor, Jumlah Penduduk, Produk Domestik Bruto, Generalized Least Square
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Nisak, Siti Choirun. "Seemingly Unrelated Regression Approach for GSTARIMA Model to Forecast Rain Fall Data in Malang Southern Region Districts." CAUCHY 4, no. 2 (June 3, 2016): 57. http://dx.doi.org/10.18860/ca.v4i2.3488.
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Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR) is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS) is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS) assumes the variance-covariance matrix has a constant error 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝝈𝟐) but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR) approach is used to accommodate the weakness of the OLS. SUR assumption is 𝜀𝑖𝑗~𝑁𝐼𝐷(𝟎,𝚺) for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS). Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1)(1,12,36),(0),(1))-SUR with determination coefficient generated with the average of 57.726%.
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Marzjarani, Morteza. "A Comparison of a General Linear Model and the Ratio Estimator." International Journal of Statistics and Probability 9, no. 3 (April 15, 2020): 54. http://dx.doi.org/10.5539/ijsp.v9n3p54.
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In data analysis, selecting a proper statistical model is a challenging issue. Upon the selection, there are other important factors impacting the results. In this article, two statistical models, a General Linear Model (GLM) and the Ratio Estimator will be compared. Where applicable, some issues such as heteroscedasticity, outliers, etc. and the role they play in data analysis will be studied. For reducing the severity of heteroscedasticity, Weighted Least Square (WLS), Generalized Least Square (GLS), and Feasible Generalized Least Square (FGLS) will be deployed. Also, a revised version of FGLS is introduced. Since these issues are data dependent, shrimp effort data collected in the Gulf of Mexico for the years 2005 through 2018 will be used and it is shown that the revised FGLS reduces the impact of heteroscedasticity significantly compared to that of FGLS. The data sets will also be checked for the outliers and corrections are made (where applicable). It is concluded that these issues play a significant role in data analysis and must be taken seriously. Further, the two statistical models, that is, the GLM and the Ratio Estimator are compared.
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Selvaratnam, Selvakkadunko, and Alwell Julius Oyet. "Minimax robust designs for wavelet estimation of nonparametric regression models with autocorrelated errors." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 03 (March 9, 2017): 1750025. http://dx.doi.org/10.1142/s0219691317500254.
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We discuss the construction of designs for estimation of nonparametric regression models with autocorrelated errors when the mean response is to be approximated by a finite order linear combination of dilated and translated versions of the Daubechies wavelet bases with four vanishing moments. We assume that the parameters of the resulting model will be estimated by weighted least squares (WLS) or by generalized least squares (GLS). The bias induced by the unused components of the wavelet bases, in the linear approximation, then inflates the natural variation of the WLS and GLS estimates. We therefore construct our designs by minimizing the maximum value of the average mean squared error (AMSE). Such designs are said to be robust in the minimax sense. Our illustrative examples are constructed by using the simulated annealing algorithm to select an optimal [Formula: see text]-point design, which are integers, from a grid of possible values of the explanatory or design variable [Formula: see text]. We found that the integer-valued designs we constructed based on GLS estimation, have smaller minimum loss when compared to the designs for WLS or ordinary least squares (OLS) estimation, except when the correlation parameter [Formula: see text] approaches 1.
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Mulyasari, Andini. "Pengaruh Indeks Pembangunan Manusia dan Angkatan Kerja terhadap Produk Domestik Regional Bruto." Economics Development Analysis Journal 5, no. 4 (March 14, 2018): 368–76. http://dx.doi.org/10.15294/edaj.v5i4.22174.
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Tujuan penelitian ini adalah untuk menganalisis besaran pengaruh indeks pembangunan manusia dan angkatan kerja yang bekerja terhadap PDRB Kabupaten/Kota di Jawa Tengah Tahun 2010-2014. Penelitian ini menggunakan analisis regresi data panel melalui pendekatan Fixed Effect Model (FEM) dengan metode Generalized Least Square (GLS). Hasil penelitian menunjukan bahwa secara bersama-sama variabel indeks pembangunan manusia dan angkatan kerja yang bekerja berpengaruh positif dan signifikan terhadap PDRB Kabupaten/Kota di Jawa Tengah. Sedangkan hasil secara parsial menunjukan bahwa indeks pembangunan manusia berpengaruh positif dan signifikan terhadap PDRB Kabupaten/Kota di Jawa Tengah dan angkatan kerja yang bekerja juga berpengaruh positif dan signifikan terhadap PDRB Kabupaten/Kota di Jawa Tengah.
The purpose of this study was to analyze the influence of human development index and labor toward GRDP regency/city in Central Java 2010-2014. This study used panel data regression analysis through Fixed Effect Model (FEM) approach with the Generalized Least Square (GLS) method. The results of this study showed that the variable of human development index and labor has positive and significant influence to the GRDP regency/city in Central Java. While the partial results showed that the index of human development have positive and significant influence to the GRDP regency/city in Central Java and the labor have positive and significant influence to the GRDP regency/city in Central Java.
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Liu, Qingfeng, and Andrey L. Vasnev. "A Combination Method for Averaging OLS and GLS Estimators." Econometrics 7, no. 3 (September 9, 2019): 38. http://dx.doi.org/10.3390/econometrics7030038.
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To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information criteria and propose feasible versions that work even when the variance-covariance matrix is unknown. The optimality of the method is proven under some regularity conditions. The results of a Monte Carlo simulation demonstrate that the method is adaptive in the sense that it achieves almost the same estimation accuracy as if the homoscedasticity or heteroscedasticity of the error term were known.
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Khan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad, and Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study." Journal of Mathematical Analysis and Modeling 2, no. 1 (March 29, 2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.
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In regression modeling, first-order auto correlated errors are often a problem, when the data also suffers from independent variables. Generalized Least Squares (GLS) estimation is no longer the best alternative to Ordinary Least Squares (OLS). The Monte Carlo simulation illustrates that regression estimation using data transformed according to the GLS method provides estimates of the regression coefficients which are superior to OLS estimates. In GLS, we observe that in sample size $200$ and $\sigma$=3 with correlation level $0.90$ the bias of GLS $\beta_0$ is $-0.1737$, which is less than all bias estimates, and in sample size $200$ and $\sigma=1$ with correlation level $0.90$ the bias of GLS $\beta_0$ is $8.6802$, which is maximum in all levels. Similarly minimum and maximum bias values of OLS and GLS of $\beta_1$ are $-0.0816$, $-7.6101$ and $0.1371$, $0.1383$ respectively. The average values of parameters of the OLS and GLS estimation with different size of sample and correlation levels are estimated. It is found that for large samples both methods give similar results but for small sample size GLS is best fitted as compared to OLS.
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Iswati, Helmi, Rahmat Syahni, and Maiyastri . "PERBANDINGAN PENDUGA ORDINARY LEAST SQUARES (OLS) DAN GENERALIZED LEAST SQUARES (GLS) PADA MODEL REGRESI LINIER DENGAN REGRESOR BERSIFAT STOKASTIK DAN GALAT MODEL BERAUTOKORELASI." Jurnal Matematika UNAND 3, no. 4 (December 1, 2014): 168. http://dx.doi.org/10.25077/jmu.3.4.168-176.2014.
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Pendugaan parameter model regresi linier pada analisis regresi linier, biasanyadilakukan dengan metode penduga OLS. Penduga OLS harus memenuhi asumsi-asumsistatistik yang disebut dengan asumsi klasik. Jika asumsi tidak dipenuhi, maka akanmenghasilkan kesimpulan yang tidak valid sehingga penduga OLS tidak bisa digunakanlagi dalam melakukan pendugaan parameter. Oleh karena itu diperlukan metode pendugaan lain untuk memperoleh hasil yang valid yaitu penduga GLS. Pelanggaran asumsidiantaranya terdapat autokorelasi pada galat model dan regresor bersifat stokastik.Adanya autokorelasi dengan regresor bersifat stokastik dilihat melalui simulasi MonteCarlo dengan ukuran sampel, koefisien autokorelasi dan ulangan yang bervariasi. Selainitu, pendugaan parameter juga dievaluasi melalui beberapa kriteria yaitu nilai AbsolutBias, Varian dan MSE. Hasil simulasi menunjukkan bahwa semakin bertambahnya ukuran sampel mengakibatkan kriteria penduga parameter semakin kecil. Sementara itu,ulangan yang tinggi yang dilakukan pada simulasi ini tidak mempengaruhi kriteria penduga parameter. Pada pendugaan parameter model untuk semua penduga, penduga GLSlebih efisien dan stabil dibanding dengan penduga OLS kecuali untuk koefisen autokorelasi −0.5 ≤ ρ ≤ −0.25 dan ρ = 0.5 pada βb1 , dan ρ = −0.25 pada βb2.
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Hayati, Erna. "Modeling Financial Performance District/City in East Java." Jurnal Ekonomi Pembangunan 18, no. 2 (January 1, 2021): 185. http://dx.doi.org/10.22219/jep.v18i2.14730.
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This study aims to determine the relationship between Local Own-Source Revenue, Capital Expenditure, and Leverage on District/City Government's Financial Performance in East Java Province. The data used is the District/City Government's financial report data in East Java Province 2014-2018. The method used is panel data regression with the parameter estimation method, namely Generalized Least Square (GLS). The results obtained are the appropriate estimation model is the REM model. The variables that significantly affect districts/cities' financial performance in East Java are Capital Expenditure and Leverage, while Local Own-Source Revenue does not have a significant effect
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Wardhana, Adhitya. "ANALISIS PERAN PENGELUARAN PEMERINTAH TERHADAP PENINGKATAN PEMBANGUNAN MANUSIA DI WILAYAH METROPOLITAN INDONESIA." Creative Research Journal 7, no. 01 (June 26, 2021): 1. http://dx.doi.org/10.34147/crj.v7i01.307.
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Penelitian ini bertujuan untuk menganalisis peran pemerintah dalam meningkatkan pembangunan manusia melalui pengeluaran infrastruktur, ekonomi, kesehatan dan pendidikan. Kemudian penelitian ini menganalisis pengaruh pengeluaran pemerintah (infrastruktur, ekonomi, kesehatan dan pendidikan) terhadap indikator pendidikan, kesehatan dan pendapatan perkapita. Ruang lingkup penelitian yaitu 54 Kabupaten/Kota di wilayah Metropolitan Indonesia. Model penelitian menggunakan model Generalized Least Square (GLS) panel data. Hasil penelitian menunjukkan pengaruh positif seluruh pengeluaran pemerintah terhadap pembangunan manusia yang diproksikan dengan Indeks Pembangunan Manusia (IPM). Kemudian hasil penelitian lainnya menunjukkan bahwa total pengeluaran pemerintah mempengaruhi positif terhadap rata lama sekolah (indikator pendidikan), angka harapan hidup (indikator kesehatan) dan pendapatan perkapitan.
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SUHARJO, B., LA MBAU, and N. K. K. ARDANA. "PEMANTAUAN PERSAMAAN MODEL STRUKTURAL DALAM DATA ORDINAL." Journal of Mathematics and Its Applications 8, no. 1 (July 1, 2009): 21. http://dx.doi.org/10.29244/jmap.8.1.21-36.
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Structural equation modeling (SEM) is one of multivariate techniques that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. To estimates their parameters, SEM based on structure covariance matrix, there are severals methods can be used as estimation methods, namely maximum likelihood (ML), weighted least squares (WLS), generalized least squares (GLS) and unweighted least squares (ULS). The purpose of this paper are to learn these methods in estimating SEM parameters and to compare their consistency, accuracy and sensitivity based on sample size and multinormality assumption of observed variables. Using a fully crossed design, data were generated for 2 conditions of normality and 5 different sample sizes. The result showed that when data are normally distributed, ML and GLS more consistent and accurate then the other methods
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Marzjarani, Morteza. "Heteroscedasticity and Model Selection via Partitioning in Fisheries Data." International Journal of Statistics and Probability 7, no. 6 (September 12, 2018): 33. http://dx.doi.org/10.5539/ijsp.v7n6p33.
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Selecting a proper model for a data set is a challenging task. In this article, an attempt was made to answer and to find a suitable model for a given data set. A general linear model (GLM) was introduced along with three different methods for estimating the parameters of the model. The three estimation methods considered in this paper were ordinary least squares (OLS), generalized least squares (GLS), and feasible generalized least squares (FGLS). In the case of GLS, two different weights were selected for improving the severity of heteroscedasticity and the proper weight (s) was deployed. The third weight was selected through the application of FGLS. Analyses showed that only two of the three weights including the FGLS were effective in improving or reducing the severity of heteroscedasticity. In addition, each data set was divided into Training, Validation, and Testing producing a more reliable set of estimates for the parameters in the model. Partitioning data is a relatively new approach is statistics borrowed from the field of machine learning. Stepwise and forward selection methods along with a number of statistics including the average square error testing (ASE), Adj. R-Sq, AIC, AICC, and ASE validate along with proper hierarchies were deployed to select a more appropriate model(s) for a given data set. Furthermore, the response variable in both data files was transformed using the Box-Cox method to meet the assumption of normality. Analysis showed that the logarithmic transformation solved this issue in a satisfactory manner. Since the issues of heteroscedasticity, model selection, and partitioning of data have not been addressed in fisheries, for introduction and demonstration purposes only, the 2015 and 2016 shrimp data in the Gulf of Mexico (GOM) were selected and the above methods were applied to these data sets. At the conclusion, some variations of the GLM were identified as possible leading candidates for the above data sets.
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Hossain, Md Shakib. "Foreign Direct Investment, Economic Freedom and Economic Growth: Evidence from Developing Countries." International Journal of Economics and Finance 8, no. 11 (October 26, 2016): 200. http://dx.doi.org/10.5539/ijef.v8n11p200.
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<p class="Default">This paper has explores the interplay between economic freedom, foreign direct investment and economic growth using panel data analysis for a sample of 79 developing countries from 1998 to 2014 by considering the level of economic freedom, as provided by the “Heritage Foundation”. Panel unit root, pedroni residual co-integration test, generalized least square (GLS), feasible GLS (FGLS), pooled OLS, random effect, fixed effect, poisson regression, prais-winsten, generalized method of movement (GMM) and generalized estimating equation (GEE) methods have used to estimates the relationship. According to the OLS and generalized method of movement the coefficient implies that a one standard deviation improvement in business freedom, trade freedom, size, investment freedom, property rights, freedom from corruption, labor freedom, financial freedom, fiscal freedom, monetary freedom increases FDI by 21.4%, 15.6%, 21.6%, 17.5%, 11.55, 9.1%, 6.9%, 8.5%, 7.4%, 10.3% and 56.1%, 45.3%, 58.3%, 51.6%, 33.7%, 39.2%, 47.4%, 41.6%, 32.5%, 38.5% points respectively and for the economic variable ,the coefficient implies that a one standard deviation improvement in GDPG and GDPPC increases FDI by 24.1%, 17.4% and 30.2%, 33.4% points respectively. By using the other method like random effect, fixed effect, poisson regression, prais-winsten and generalized estimating equation (GEE) method explores that economic freedom in the host country is a positive determinants of FDI inflows in developing countries and also the result suggests that foreign direct investment is positively correlated with the economic growth in the host countries.</p>
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Ika Wahyuntari, Linda, and Amin Pujiati. "Disparitas Pembangunan Wilayah Kabupaten/ Kota di Provinsi Jawa Tengah." Economics Development Analysis Journal 5, no. 3 (March 14, 2018): 296–305. http://dx.doi.org/10.15294/edaj.v5i3.22153.
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Penelitian ini bertujuan untuk mengidentifikasi klasifikasi daerah cepat maju dan cepat tumbuh, menganalisis pengaruh aglomerasi industri, dana perimbangan, IPM, dan klasifikasi daerah cepat maju dan cepat tumbuh terhadap disparitas pembangunan wilayah kabupaten/ kota di Provinsi Jawa Tengah. Penelitian ini menggunakan metode analisis deskriptif Tipologi Klassen dan analisis regresi data panel dengan metode Generalized Least Square (GLS). Hasil identifikasi kabupaten/ kota yang konsisten berada di klasifikasi daerah cepat maju dan cepat tumbuh dalam kurun waktu tahun 2009-2013, yaitu Kabupaten Cilacap, Kota Magelang, Kota Surakarta, dan Kota Semarang. Hasil dari penelitian ini menunjukkan bahwa aglomerasi industri berpengaruh positif dan signifikan, sedangkan dana perimbangan, IPM, dan klasifikasi daerah cepat dan cepat tumbuh berpengaruh negatif dan signifikan terhadap disparitas pembangunan wilayah kabupaten/ kota di Provinsi Jawa Tengah.
This study aims to identify the classification of the area fast forward and fast-growing, analyze the effect of industrial agglomeration, the balance funds, HDI, and area classification fast forward and fast-growing against the disparity of development districts/ cities in Central Java province. This research using descriptive analysis Typology Klassen and panel data regression analysis with the method of Generalized Least Square (GLS). The results of the identification of districts/ cities that are in the area classification consistently fast forward and fast-growing in the period 2009-2013, namely Kabupaten Cilacap, Kota Magelang, Kota Surakarta and Kota Semarang. The results of this study indicate that the industrial agglomeration effect on positive and significant, while the balance funds, HDI, and the classification of fast and fast-growing regions a significant negative effect on the development disparity districts/ cities in Central Java province.
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Rakotoasimbola, Eric, and Sam Blili. "Measures of fit impacts: Application to the causal model of consumer involvement." International Journal of Market Research 61, no. 1 (August 30, 2018): 77–92. http://dx.doi.org/10.1177/1470785318796950.
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Using the Monte Carlo simulation method, this study analyzes the impacts on fit indices by the degree of nonnormality of variables, the sample size, and the choice of estimation method. To address these issues, we use the causal model of consumer involvement as elaborated by Mittal and Lee. Results of this study show that adjusted goodness of fit index (AGFI) and goodness of fit index (GFI) are subject to variation in sample size, and their use requires a sample size of at least 300 observations to be reliable. Comparative fit index (CFI) and root mean square error of approximation (RSMEA) are more reliable with the generalized least squares (GLS) compared with maximum likelihood estimation (MLE) method under different settings of sample size and degree of nonnormality. Finally, for the standardized root mean square residual (SRMR), it is recommended that it is used with the MLE method. This study provides prescriptions for the choice of fit indices and the requirements of sample size and estimation method to test the causal model of consumer involvement. The method used here can be extended to any model before fitting it to real data. It helps researchers to prevent conflictual results regarding the choice of fit indices.
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Mehrabani, Ali, and Aman Ullah. "Improved Average Estimation in Seemingly Unrelated Regressions." Econometrics 8, no. 2 (April 27, 2020): 15. http://dx.doi.org/10.3390/econometrics8020015.
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In this paper, we propose an efficient weighted average estimator in Seemingly Unrelated Regressions. This average estimator shrinks a generalized least squares (GLS) estimator towards a restricted GLS estimator, where the restrictions represent possible parameter homogeneity specifications. The shrinkage weight is inversely proportional to a weighted quadratic loss function. The approximate bias and second moment matrix of the average estimator using the large-sample approximations are provided. We give the conditions under which the average estimator dominates the GLS estimator on the basis of their mean squared errors. We illustrate our estimator by applying it to a cost system for United States (U.S.) Commercial banks, over the period from 2000 to 2018. Our results indicate that on average most of the banks have been operating under increasing returns to scale. We find that over the recent years, scale economies are a plausible reason for the growth in average size of banks and the tendency toward increasing scale is likely to continue
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He, Ni (Phil), Jihong (Solomon) Zhao, and Nicholas P. Lovrich. "Community Policing: A Preliminary Assessment of Environmental Impact With Panel Data on Program Implementation in U.S. Cities." Crime & Delinquency 51, no. 3 (July 2005): 295–317. http://dx.doi.org/10.1177/0011128704266756.
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This article examines the environmental impact on the programmatic implementation of community-oriented policing (COP) in large municipal police agencies during the 1990s. Three waves of nationwide surveys (1993, 1996, and 2000) based on a random sample of 281 municipalities and the corresponding police agencies were used for our analysis. Based on one-way generalized least square (GLS) panel data analysis, we found that post-Crime Control Act of 1994federal funding and council-manager forms of government are significant predictors of COP implementation. To the contrary, other environmental factors such as personnel resources, city socioeconomic status, and mechanisms for citizen participation did not yield any statistically significant effects.
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Desti, Cesa Febri, Dodi Devianto, and Izzati Rahmi HG. "ANALISIS KETERKAITAN A NTAR KOMODITAS PROTEIN DENGAN MENGGUNAKAN MODEL ALMOST IDEAL DEMAND SYSTEM (AIDS)." Jurnal Matematika UNAND 2, no. 3 (September 10, 2013): 162. http://dx.doi.org/10.25077/jmu.2.3.162-166.2013.
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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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Salatin, Parvaneh, and Naahid Noorpoor. "Governance quality impact on health economics in selected countries: The panel data approach." Journal of Governance and Regulation 4, no. 2 (2015): 148–54. http://dx.doi.org/10.22495/jgr_v4_i2_c1_p8.
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The purpose of this paper is investigating the theoretical relationship between the effectiveness of governance quality on health economics in selected middle-income countries, using panel data. The Results of the estimation by using the Method of Generalized Least Squares (GLS) & Generalized Method of Moments (GMM) in selected countries for the period 2002-2011 show that governance quality has positive & significant effect on the life expectancy as an index showing the health economics in the group of the selected countries.
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Yudhi Pramono, Agung, and Etty Soesilowati. "Determinan Kualitas Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Tengah." Economics Development Analysis Journal 5, no. 3 (March 14, 2018): 269–77. http://dx.doi.org/10.15294/edaj.v5i3.22149.
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Penelitian ini bertujuan untuk menganalisis seberapa besar pengaruh pengeluaran pemerintah daerah sektor pendidikan, pengeluaran pemerintah daerah sektor kesehatan, rasio ketergantungan penduduk dan pendapatan perkapita terhadap pembangunan manusia yang diukur dengan IPM. Populasi penelitian terdiri dari 35 Kabupaten/Kota di Provinsi Jawa Tengah, menggunakan data sekunder dari Badan Pusat Statistik Provinsi Jawa Tengah dan Biro Keuangan Sekretaris Daerah Provinsi Jawa Tengah dalam periode 2009 sampai 2013. Variabel penelitian ini indeks pembangunan manusia, pengeluran pemerintah daerah sektor pendidikan, pengeluaran pemerintah daerah sektor kesehatan, rasio ketergantungan penduduk, dan pendapatan perkapita. Dalam penelitian ini, digunakan metode penelitian kuantitatif dengan menggunakan analisis regresi data panel model efek tetap (FEM) dengan metode Generalized Least Square (GLS). Hasil penelitian ini dapat diketahui bahwa pengeluaran pemerintah daerah sektor pendidikan berpengaruh positif dan signifikan terhadap IPM, pengeluaran pemerintah daerah sektor kesehatan berpengaruh positif dan signifikan terhadap IPM, rasio ketergantungan penduduk berpengaruh negatif dan signifikan terhadap IPM, sementara pendapatan perkapita tidak berpengaruh secara signifikan terhadap IPM.
This research has purpose to analyze how much influence of the local government expenditure in educational sector, local government expenditure in health sector, dependency ratio, and per capita income of a human development measured by HDI. the population of this research consists of 35 regionals in Central Java and region bureau money secretary of Central Java province among 2009 and 2013 period. the variables used in this research are HDI, local government expenditure in educational sector, local government expenditure in health sector, dependency ratio, and per capita income. in this research, quantitative and regression analysis of Fixed Effect Model is used as well as Generalized Least Square method (GLS). The results of this research are the outcome of regional government in educational and health sector influence significance and positively to the HDI, dependency ratio significance and negatively influence to the HDI, while per capita income does not influence significance to the HDI.
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Paris, Muhammad Amin. "PERBANDINGAN ANTARA UNWEIGHTED LEAST SQUARES (ULS) DAN PARTIAL LEAST SQUARES (PLS) DALAM PEMODELAN PERSAMAAN STRUKTURAL (STUDI KASUS MODEL ANALISIS PRESTASI BELAJAR MAHASISWA TAHUN PERTAMA PROGRAM STUDI S1 MATEMATIKA FMIPA-INSTUTUT PERTANIAN BOGOR)." Jurnal Pendidikan Matematika 2, no. 1 (April 19, 2017): 21. http://dx.doi.org/10.18592/jpm.v2i1.1165.
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Structural Equation Modeling (SEM) is one of multivariate techniques that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. Estimation of Parameter methods that is often applied in SEM are Maximum Likelihood (ML), Weighted Least Squares (WLS), Unweighted Least Squares (ULS), Generalized Least Squares (GLS) and Partial Least Squares (PLS). This research aims to compare ULS method and PLS method in estimating parameter model of achievement of student learning in first year undergraduate Mathematics students, FMIPA, Bogor Agricultural University ( IPB). This research use secondary and primary data which amounts to 112. The result of this research indicates that ULS method is more accurate than PLS methods. The analysis done with ULS method shows that motivation, capability and environmental had an effect to achievement of student learning.
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Baltagi, Badi H., and Ping X. Wu. "UNEQUALLY SPACED PANEL DATA REGRESSIONS WITH AR(1) DISTURBANCES." Econometric Theory 15, no. 6 (December 1999): 814–23. http://dx.doi.org/10.1017/s0266466699156020.
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This paper deals with the estimation of unequally spaced panel data regression models with AR(1) remainder disturbances. A feasible generalized least squares (GLS) procedure is proposed as a weighted least squares that can handle a wide range of unequally spaced panel data patterns. This procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters. The paper also provides a locally best invariant test for zero first-order serial correlation against positive or negative serial correlation in case of unequally spaced panel data.
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Rombebunga, Meyjerd. "Tata Kelola Perusahaan dan Penghindaran Pajak." Perspektif Akuntansi 2, no. 3 (December 23, 2019): 237–55. http://dx.doi.org/10.24246/persi.v2i3.p237-255.
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Penelitian ini bertujuan untuk menguji pengaruh tata kelola perusahaan, antara lain; proporsi komisaris independen, kualitas audit, kepemilikan institusional, dan komite audit terhadap penghindaran pajak perusahaan. Jenis data yang digunakan dalam penelitian ini adalah data panel dari tahun 2012-2016 pada perusahaan tambang mineral dan batubara yang diperoleh dari Bursa Efek Indonesia (BEI). Dengan menggunakan metode kuantitatif berupa uji stasioner dan uji regresi data panel dengan model Generalized Least Square (GLS), penelitian ini menunjukkan bahwa proporsi komisaris independen dan kualitas audit berpengaruh positif dan signifikan terhadap penghindaran pajak. Sedangkan, kualitas audit dan kepemilikan institusional tidak berpengaruh terhadap penghindaran pajak.
Kata Kunci: proporsi komisaris independen, kualitas audit, kepemilikan institusional, komite audit, penghindaran pajak
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Sensuse, Dana Indra, and I. Made Agus Ana Widiatmika. "PENGEMBANGAN MODEL PENERIMAAN TEKNOLOGI INTERNET OLEH PELAJAR DENGAN MENGGUNAKAN KONSEP TECHNOLOGY ACCEPTANCE MODEL (TAM)." Jurnal Sistem Informasi 4, no. 2 (July 12, 2012): 81. http://dx.doi.org/10.21609/jsi.v4i2.249.
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Teknologi internet mulai diterima dan digemari dikalangan pelajar. Pelajar menggunakan fasilitas internet untuk membantu mereka dalam penyelesaian tugas sekolah. Tulisan ini menggunakan Technology Acceptance Model (TAM) untuk membangun model penerimaan teknologi internet di kalangan pelajar. Penelitian ini melibatkan 5 variabel endogen dan mengikutsertakan 4 variabel eksogen. Metode analisis yang digunakan adalah generalized least square (GLS) dan menggunakan polychoric correlation matrix dan asymptotic covariance matrix sebagai data tambahan. Tesis ini melibatkan 15 hipotesis yang mewakili hubungan antara variable-variabel yang ada. Pengujian hipotesis dilakukan untuk mendapatkan dan membuktikan model penerimaan teknologi internet yang komprehensif. Model penerimaan teknologi internet yang diperoleh bisa memberikan gambaran indikator-indikator yang harus diperhatikan dan menjadi hal yang bisa dikembangkan untuk penelitian lebih lanjut.
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Rombebunga, Meyjerd. "Tata Kelola Perusahaan dan Penghindaran Pajak." Perspektif Akuntansi 2, no. 3 (December 23, 2019): 249–67. http://dx.doi.org/10.24246/persi.v2i3.p249-267.
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Penelitian ini bertujuan untuk menguji pengaruh tata kelola perusahaan, antara lain; proporsi komisaris independen, kualitas audit, kepemilikan institusional, dan komite audit terhadap penghindaran pajak perusahaan. Jenis data yang digunakan dalam penelitian ini adalah data panel dari tahun 2012-2016 pada perusahaan tambang mineral dan batubara yang diperoleh dari Bursa Efek Indonesia (BEI). Dengan menggunakan metode kuantitatif berupa uji stasioner dan uji regresi data panel dengan model Generalized Least Square (GLS), penelitian ini menunjukkan bahwa proporsi komisaris independen dan kualitas audit berpengaruh positif dan signifikan terhadap penghindaran pajak. Sedangkan, kualitas audit dan kepemilikan institusional tidak berpengaruh terhadap penghindaran pajak.
Kata Kunci: proporsi komisaris independen, kualitas audit, kepemilikan institusional, komite audit, penghindaran pajak
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Dao, Le Kieu Oanh, Thuy Tu Pham, and Van Chien Nguyen. "Factors Affecting the Competitive Capacity of Commercial Banks: A Critical Analysis in an Emerging Economy." International Journal of Financial Research 11, no. 4 (June 28, 2020): 241. http://dx.doi.org/10.5430/ijfr.v11n4p241.
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This research was conducted to investigate the factors influencing the commercial bank’s competitive capacity in an emerging country. Data were collected from the domestic-owned commercial banks and foreign-owned commercial banks listed on Vietnam’s Stock Exchange over the period of nine years from 2010 to 2018. Three statistic approaches were employed to address econometrics issues and to improve the accuracy of the regression coefficients: Pooled Ordinary Least Square (Pooled OLS), Random Effects Model (REM), and Fixed Effects Model (FEM). To correct the diagnostics and endogeneity in the model, the study uses Generalized Least Square (GLS) and Generalized Method of Moments (GMM). In order to account for the degree of competitive capacity we use Lerner index. Results demonstrate that the impact of bank-specific characteristics on market power in banks is statistically significant, and there are substantial distinguishments of economic consideration among these factors. In addition, a bank with a higher level of competitive capacity in the previous year will outstandingly generate competitive capacity in the current year. Another possibility, a greater level foreign investment into the banks in the host country could further encourage competitive capacity in the banking system. Finally, economic growth rate has no impact on competitive capacity at a significant level of 5% while a positive effect from inflation on bank’s market power could be found.
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Jefferson, Philip N. "Deriving the GLS Transformation Parameter in Elementary Panel Data Models." American Economist 49, no. 1 (March 2005): 45–48. http://dx.doi.org/10.1177/056943450504900103.
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The Generalized Least Squares (GLS) transformation that eliminates serial correlation in the error terms is central to a complete understanding of the relationship between the pooled OLS, random effects, and fixed effects estimators. A significant hurdle to attainment of that understanding is the calculation of the parameter that delivers the desired transformation. This paper derives this critical parameter in the benchmark case typically used to introduce these estimators using nothing more than elementary statistics (mean, variance, and covariance) and the quadratic formula.
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Alhajeri, Bader H., Lucas M. V. Porto, and Renan Maestri. "Habitat productivity is a poor predictor of body size in rodents." Current Zoology 66, no. 2 (July 26, 2019): 135–43. http://dx.doi.org/10.1093/cz/zoz037.
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Abstract The “resource availability hypothesis” predicts occurrence of larger rodents in more productive habitats. This prediction was tested in a dataset of 1,301 rodent species. We used adult body mass as a measure of body size and normalized difference vegetation index (NDVI) as a measure of habitat productivity. We utilized a cross-species approach to investigate the association between these variables. This was done at both the order level (Rodentia) and at narrower taxonomic scales. We applied phylogenetic generalized least squares (PGLS) to correct for phylogenetic relationships. The relationship between body mas and NDVI was also investigated across rodent assemblages. We controlled for spatial autocorrelation using generalized least squares (GLS) analysis. The cross-species approach found extremely low support for the resource availability hypothesis. This was reflected by a weak positive association between body mass and NDVI at the order level. We find a positive association in only a minority of rodent subtaxa. The best fit GLS model detected no significant association between body mass and NDVI across assemblages. Thus, our results do not support the view that resource availability plays a major role in explaining geographic variation in rodent body size.
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Hossain, Shakib, and Abu Zafar Ahmed Mukul. "Does Institutional Quality and Economic Freedom Impact on Foreign Direct Investment? Evidence From Developing Countries." International Journal of Accounting and Financial Reporting 8, no. 4 (October 11, 2018): 324. http://dx.doi.org/10.5296/ijafr.v8i4.13973.
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Using panel data analysis, it is an attempt to estimates the significance of institutional quality and economic freedom on foreign direct investment for a sample of 79 developing countries from 1998 to 2014. Panel unit root, pedroni residual cointegration test, vector error correction model, generalized least square (GLS), feasible GLS (FGLS), pooled OLS, random effect, fixed effect, poisson regression, prais-winsten, generalized method of movement (GMM) and generalized estimating equation (GEE) method are utilizing for estimates the importance of institutional qualities and economic freedom for facilitating foreign direct investment. VECM confirms that there is a long run relationship among the tested variables means that commensurate institutional quality and substantive economic freedom stimulates foreign direct investment. According to the OLS method ,for the institutional quality the coefficient implies that a one standard deviation improvement in political stability and absence of violence, government effectiveness, regulatory qualities, rules of law and control of corruption increases FDI by 24.6%, 31.6%, 12.8%, 23.9% and 37.7% and on the other hand for the economic freedom , the coefficient implies that a one standard deviation improvement in business freedom, trade freedom, government size, investment freedom, property rights, freedom from corruption, labor freedom, financial freedom, fiscal freedom, monetary freedom increases FDI by 28.4%, 32.7%, 29.5%,22.8%, 29.0%, 36.4%,29.3%, 37.5%, 46.1% and 38.2% respectively. By using the other methods like random effect, fixed effect, poisson regression, prais-winsten and generalized estimating equation (GEE) method explores that both the institutional quality and economic freedom are influencing on FDI in the developing countries.
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Pamularsih, Leni, Mustafid Mustafid, and Abdul Hoyyi. "PENERAPAN SEASONAL GENERALIZED SPACE TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (SGSTAR SUR) PADA PERAMALAN HASIL PRODUKSI PADI." Jurnal Gaussian 10, no. 2 (May 31, 2021): 241–49. http://dx.doi.org/10.14710/j.gauss.v10i2.29435.
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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.
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ذاكر, سلمى ثابت, and ديان حميد مجيد. "دراسة مقارنة لبعض طرائق تقدير مصفوفة التباين والتباين المشترك الحصينة للمعلمات المقدرة بطريقة (OLS) في البيانات المقطعية." Journal of Economics and Administrative Sciences 23, no. 98 (August 1, 2018): 384. http://dx.doi.org/10.33095/jeas.v23i98.288.
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المستخلص
أن أنموذج الأنحدار الخطي الطبيعي الكلاسيكي(Classical Normal Linear Regression Model) قائم على أساس العديد من الفرضيات من بينها فرضية تجانس التباين، كما هو معروف فأن استخدام طريقة المربعات الصغرى الأعتيادية (OLS)، تحت ظل وجود هذه المشكلة يجعل مقدراتها تفقد بعضاً من خصائصها المرغوب فيها، كما أن الأستدلال الأحصائي غير مقبول، وعليه فقد تم وضع بديلين مهمين الأول طريقة المربعات الصغرى العمومية (Generalized Least Square) والتي يرمز له (GLS)، أما البديل الثاني فهو تقدير مصفوفة التباين والتباين المشترك الحصينة (Robust covariance matrix estimation) للمعلمات المقدرة بطريقة (OLS). تكون حسب نوع البيانات التي يتم التعامل معها، ولقد تناولنا في هذه الدراسة البيانات المقطعية (Cross-Section)، حيث أن مشكلة عدم تجانس في التباين تكون واردة فيها وان مصفوفة التباين والتباين المشترك الحصينة المقدرة لهذا النوع من البيانات هي (HCCME) وتتضمن العديد من الطرائق، ولقد تناولنا في دراستنا بعضاً منها والمتمثلة ﺑ .
ولقد تمت في هذه الدراسة مقارنة هذه الطرائق وتحديد أولوية أدائها بالنسبة لأداء طريقة (GLS) وذلك في حالة البيانات المقطعية وبأستخدام أسلوب المحاكاة في توليد عينات بأحجام مختلفة.
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Yani, Rina Novi, Muhammad Arfan, and M. Shabri Abd Majid. "WHAT DETERMINES ISLAMIC STOCK RETURNS IN INDONESIA?" Share: Jurnal Ekonomi dan Keuangan Islam 9, no. 1 (June 25, 2020): 1. http://dx.doi.org/10.22373/share.v9i1.6259.
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ABSTRACT - This study aims to empirically explore and analyze the effects of profitability, liquidity, solvency, and firm size on the rate of returns of Islamic stocks in Indonesia. A total of 30 companies registered in the Jakarta Islamic Index were selected as samples of the study using purposive sampling techniques during the 2013-2017 period and estimated using the panel model of Generalized Least Square (GLS). This study found evidence of a positive and significant effect of profitability on the Islamic stock returns, while liquidity, solvency, and company size were documented to insignificant in affecting the Islamic stock returns. The results of this study imply that to gain a maximum rate of returns, investors should pay attention to the profitability gained by the companies listed on the Islamic stock market.==================================================================================================ABSTRAK – Apa yang Menentukan Tingkat Pengembalian Saham Syariah di Indonesia? Penelitian ini bertujuan untuk menguji dan menganalisis pengaruh profitabilitas, likuiditas, solvabilitas, dan ukuran perusahaan terhadap tingkat pengembalian saham syariah di Indonesia. Sebanyak 30 perusahaan yang terdaftar di Jakarta Islamic Index dipilih sebagai sampel dalam penelitian ini dengan menggunakan teknik purposive sampling selama periode 2013-2017 dan diestimasi dengan model panel Generalized Least Square. Penelitian ini menemukan bukti bahwa profitabilitas berpengaruh positif dan signifikan terhadap tingkat pengembalian saham syariah, sedangkan likuiditas, solvabilitas, dan ukuran perusahaan tidak berpengaruh signifikan terhadap tingkat pengembalian saham syariah. Hasil penelitian ini menunjukkan bahwa untuk memaksimumkan tingkat pengembalian, investor harus memperhatikan keuntungan yang diperolehi perusahaan yang terdaftar di pasar saham syariah.
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Ramnath, Vishal. "Comparison of straight line curve fit approaches for determining parameter variances and covariances." International Journal of Metrology and Quality Engineering 11 (2020): 14. http://dx.doi.org/10.1051/ijmqe/2020011.
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Pressure balances are known to have a linear straight line equation of the form y = ax + b that relates the applied pressure x to the effective area y, and recent work has investigated the use of Ordinary Least Squares (OLS), Weighted Least Squares (WLS), and Generalized Least Squares (GLS) regression schemes in order to quantify the expected values of the zero-pressure area A0 = b and distortion coefficient λ = a/b in pressure balance models of the form y = A0(1 + λx). The limitations with conventional OLS, WLS and GLS approaches is that whilst they may be used to quantify the uncertainties u(a) and u(b) and the covariance cov(a, b), it is technically challenging to analytically quantify the covariance term cov(A0, λ) without additional Monte Carlo simulations. In this paper, we revisit an earlier Weighted Total Least Squares with Correlation (WTLSC) algorithm to determine the variances u2(a) and u2(b) along with the covariance cov(a, b), and develop a simple analytical approach to directly infer the corresponding covariance cov(A0, λ) for pressure metrology uncertainty analysis work. Results are compared to OLS, WLS and GLS approaches and indicate that the WTLSC approach may be preferable as it avoids the need for Monte Carlo simulations and additional numerical post-processing to fit and quantify the covariance term, and is thus simpler and more suitable for industrial metrology pressure calibration laboratories. Novel aspects is that a Gnu Octave/Matlab program for easily implementing the WTLSC algorithm to calculate parameter expected values, variances and covariances is also supplied and reported.
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Damayanti, Sari Minjari. "Analisis Pengaruh Variabel-Variabel Makroekonomi terhadap Tingkat Pengembalian di Pasar Modal Periode 2000 -2011 dengan Membandingkan Hasil Estimasi OLS, GLS dan MLE." Binus Business Review 5, no. 1 (May 30, 2014): 267. http://dx.doi.org/10.21512/bbr.v5i1.1215.
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The factors in macroeconomic gives enormous influence on the fluctuation rate of return on stocks that is reflected in the stock price movement in the stock market. Movements in excess of the normal state, such as those caused by the global economic crisis, from the macro variables will create specific shocks on the capital markets, which will affect the value of the return on stocks in the capital market. To determine the effect of the factors or macroeconomic variables on the return of the index shares on BEI, empirical tests are accurately performed on these variables. This study has two main objectives: first to test how much the influence of macroeconomic variables on the return of the shares in the Indonesian Stock Exchange (BEI). Second, empirical research testing of variables using three different estimation methods, namely, Ordinary Least Squares (OLS), Generalized Least Square (GLS) and Maximum Likelihood Estimation (MLE) to find out how much the estimation accuracy of the three methods. The empirical result shows that there is a significant relationship between composite stock returns BEI and three macroeconomic variables, the consumer price index (inflation rate), exchange rate and interest rate of SBI. These results indicate that the three macro variables that affect the rate of return on the stock market.
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Luskin, Robert C. "Wouldn't It Be Nice …? The Automatic Unbiasedness of OLS (and GLS)." Political Analysis 16, no. 3 (2008): 345–49. http://dx.doi.org/10.1093/pan/mpn003.
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In a recent issue of this journal, Larocca (2005) makes two notable claims about the best linear unbiasedness of ordinary least squares (OLS) estimation of the linear regression model. The first, drawn from McElroy (1967), is that OLS remains best linear unbiased in the face of a particular kind of autocorrelation (constant for all pairs of observations). The second, much larger and more heterodox, is that the disturbance need not be assumed uncorrelated with the regressors for OLS to be best linear unbiased. The assumption is unnecessary, Larocca says, because “orthogonality [of disturbance and regressors] is a property of all OLS estimates” (p. 192). Of course OLS's being best linear unbiased still requires that the disturbance be homoskedastic and (McElroy's loophole aside) nonautocorrelated, but Larocca also adds that the same automatic orthogonality obtains for generalized least squares (GLS), which is also therefore best linear unbiased, when the disturbance is heteroskedastic or autocorrelated.
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Wijayanti, Ratih, Irham Irham, and Suhatmini Hardyastuti. "DAMPAK KEBIJAKAN TARIF DAN NON-TARIF TERBADAP PERMINTAAN DAN DAYA SAING TUNA INDONESIA DI PASAR UNI EROPA, AMERIKA DAN JEPANG." Agro Ekonomi 18, no. 1 (November 28, 2016): 9. http://dx.doi.org/10.22146/agroekonomi.16703.
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The objective of this study is to analyse the impact of implementation tariff and non-tariff policy also the other factors on demand and competitiveness Indonesia's tuna commodity. Panel data was implemented in this research because beside used anually time series data during the period 1983-2008 also used cross section data which describe the demand and competitiveness condition of Indonesia's tuna commodity in three major market. Equation models in this research were estimated with Generalized Least Square (GLS) method withfzxed effect to analyse all of demand and competitiveness of export tuna in three major market and Ordinary Least Square (OLS) method to analyse demand and competitiveness of export tuna in each market. Meanwhile the competitiveness of tuna is measured using Revealed Comparatif Advantage (RCA) index. The results show that export price in European union and shrimp price in Japan are main factors the demand of Indonesia's tuna export in three major market. The change of Gross Domestic Product (GDP) Japan has not been influenced the demand of Indonesia's tuna export to these country because Japan's import of fishery product from Indonesia has been donefrequently and Indonesia's market share is very high. Export tuna from Indonesia is competing with export tuna from Thailand in European and Japan market while with export tuna from Philippines in USA market. Tariff policy more reduce and didn't influenced on demand and competitiveness. Thisfinding were confirmed by significancy which more little than non-tariff policy.Penelitian ini bertujuan untuk mengetahui dampak penerapan kebijakan tarif 'dan non-tarif serta beberapa faktor lainnya terhadap permintaan dan daya saing tuna Indonesia. Data dalam penelitian ini dianalisis menggunakan data panel karena selain menggunakan data runtut waktu (1983-2008) juga menggunakan data silang yang menggambarkan kondisi permintaan dan daya saing tuna di ketiga pasar yaitu Uni Eropa, Amerika dan Jepang. Model persamaan dalam penelitian ini diestimasi dengan metode data panel (Generalized Least Square/ GLS dengan efek tetap) untuk menggambarkan seluruh permintaan dan daya saing ekspor tuna ke tiga pasar dan metode Ordinary Least Square (OLS) untuk menggambarkan kondisi permintaan dan daya saing tuna di masing-masing pasar. Pengukuran daya saing tuna dengan menggunakan indeks Revealed Comparatif Advantage (RCA). Hasil analisis menunjukkan bahwa harga ekspor di Uni .Eropa dan harga udang di Jepang merupakan penentu utama permintaan tuna Indonesia di pasar produktif. Impor perikanan Jepang akan produk tuna dari Indonesia yang sudah rutin dilakukan dan besamya pangsa pasar tuna Indonesia di Jepang menyebabkan perubahan pendapatan nasional (GDP masyarakat Jepang) tidak mempengaruhi permintaan tuna Indonesia ke negara tersebut. Indonesia bersaing dengan Thailand di pasar Uni Eropa dan Jepang serta bersaing dengan Filipina di pasar Amerika. Kebijakan tarif semakin tidak berpengaruh terhadap permintaan dan daya saing tuna Indonesia ke pasar produktif yang dibuktikan dengan nilai signifIkansi yang lebih keeil dibandingkan kebijakan non-tarif yang diberlakukan.
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Maidina, Laras Putri, and Lela Nurlaela Wati. "PENGARUH KONEKSI POLITIK, GOOD CORPORATE GOVERNANCE DAN KINERJA KEUANGAN TERHADAP TAX AVOIDANCE." JURNAL AKUNTANSI 9, no. 2 (November 30, 2020): 118–31. http://dx.doi.org/10.37932/ja.v9i2.95.
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The purpose of the study was to test the influence of Political Connections, Good Corporate Governance, and Financial Performance on Tax Avoidance. The research method used is a quantitative method. The study used data of 45 manufacturing companies listed Index Stock Exchange (IDX) during the period 2014 to 2018. Samples are taken by the purposive sampling method and which meets the criteria for sample selection. Data is processed with Version 9 Eviews software using the Generalized Least Square (GLS) method. Results show that Political Connection and Financial Performance have a positive influence on Tax Avoidance, this suggests that there are still companies that practice tax evasion. Corporate Governance has no effect on Tax Avoidance, meaning the existence of Corporate Governance is effective in attempting to prevent tax avoidance practices.
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von Jeinsen, Beatrice, Ramachandran S. Vasan, David D. McManus, Gary F. Mitchell, Susan Cheng, and Vanessa Xanthakis. "Joint influences of obesity, diabetes, and hypertension on indices of ventricular remodeling: Findings from the community-based Framingham Heart Study." PLOS ONE 15, no. 12 (December 10, 2020): e0243199. http://dx.doi.org/10.1371/journal.pone.0243199.
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Introduction Obesity, hypertension, and diabetes are independently associated with cardiac remodeling and frequently co-cluster. The conjoint and separate influences of these conditions on cardiac remodeling have not been investigated. Materials and methods We evaluated 5,741 Framingham Study participants (mean age 50 years, 55% women) who underwent echocardiographic measurements of left ventricular (LV) mass (LVM), LV ejection fraction (LVEF), global longitudinal strain (GLS), mitral E/e’, left atrial end-systolic (peak) dimension (LASD) and emptying fraction (LAEF). We used multivariable generalized linear models to estimate the adjusted-least square means of these measures according to cross-classified categories of body mass index (BMI; normal, overweight and obese), hypertension (yes/no), and diabetes (yes/no). Results We observed statistically significant interactions of BMI category, hypertension, and diabetes with LVM, LVEF, GLS, and LAEF (p for all 3-way interactions <0.01). Overweight and obesity (compared to normal BMI), hypertension, and diabetes status were individually and conjointly associated with higher LVM and worse GLS (p<0.01 for all). We observed an increase of 34% for LVM and of 9% for GLS between individuals with a normal BMI and without hypertension or diabetes compared to obese individuals with hypertension and diabetes. Presence of hypertension was associated with higher LVEF, whereas people with diabetes had lower LVEF. Conclusions Obesity, hypertension, and diabetes interact synergistically to influence cardiac remodeling. These findings may explain the markedly heightened risk of heart failure and cardiovascular disease when these factors co-cluster.
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Abd. Majid, M. Shabri, and Hartomi Maulana. "A Comparative Analysis of the Productivity of Islamic and Conventional Mutual Funds in Indonesia: Data Envelopment Analysis (DEA) and General Least Square (GLS) Approaches." Gadjah Mada International Journal of Business 14, no. 2 (May 1, 2012): 183. http://dx.doi.org/10.22146/gamaijb.5439.
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This paper is an extended version of our earlier study (Abd. Majid and Maulana 2010) to further re-examine the relative efficiencies of selected Islamic and conventional mutual funds companies in Indonesia during the period 2004 to 2007 and their determinants. To measure their efficiencies, the output-input data consisting of a panel of conventional and Islamic mutual funds companies are empirically examined based on the most commonly used non-parametric approach, namely, Data Envelopment Analysis (DEA). It also attempts to investigate the influence of the mutual funds companies’ characteristicson efficiency measures using the Generalized Least Square (GLS) estimation. The study finds that, on average, the Indonesian mutual funds companies experienced a decrease in Total Factor Productivity (TFP) growth. It is mainly caused by a decline in both efficiency and technical efficiencies, where the efficiency change is largely contributed by the changes in pure efficiency rather than scale efficiency. Additionally, the study also documents that the funds size negatively affects efficiency. This indicates that due to its diseconomies of scale, a larger mutual funds company is less efficient than a smaller funds company. Finally, in comparing the efficiency of the mutual funds companies, the study finds that, on average, the Islamic unit trust companies perform more poorly than their conventionalcounterparts.
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Green, Donald P., and Lynn Vavreck. "Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation Approaches." Political Analysis 16, no. 2 (September 14, 2007): 138–52. http://dx.doi.org/10.1093/pan/mpm025.
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Analysts of cluster-randomized field experiments have an array of estimation techniques to choose from. Using Monte Carlo simulation, we evaluate the properties of point estimates and standard errors (SEs) generated by ordinary least squares (OLS) as applied to both individual-level and cluster-level data. We also compare OLS to alternative random effects estimators, such as generalized least squares (GLS). Our simulations assess efficiency across a variety of scenarios involving varying sample sizes and numbers of clusters. Our results confirm that conventional OLS SEs are severely biased downward and that, for all estimators, gains in efficiency come mainly from increasing the number of clusters, not increasing the number of individuals within clusters. We find relatively minor differences across alternative estimation approaches, but GLS seems to enjoy a slight edge in terms of the efficiency of its point estimates and the accuracy of its SEs. We illustrate the application of alternative estimation approaches using a clustered experiment in which Rock the Vote TV advertisements were used to encourage young voters in 85 cable TV markets to vote in the 2004 presidential election.
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Syukriyah, Ana. "The Analysis of Absolute Convergency of Human Development Inter Provinces in Indonesia." Economics Development Analysis Journal 5, no. 4 (March 14, 2018): 362–67. http://dx.doi.org/10.15294/edaj.v5i4.22173.
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The purpose of this study was to identify the sigma and absolute beta convergence of the Human Development Index (HDI) inter provinces in Indonesia, and identify the speed of absolute beta convergence. This study used a quantitative analysis with tool used is regression panel data with fixed effect model Generalize Least Square method (GLS). The results shows that there happen sigma convergence of HDI and absolute beta convergence of HDI inter provinces in Indonesia. The speed of absolute convergence is equal to 0.807 percent annually.
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KABOUDAN, MAK. "GP VERSUS GLS SPATIAL INDEX MODELS TO FORECAST SINGLE-FAMILY HOME PRICES." New Mathematics and Natural Computation 04, no. 02 (July 2008): 143–63. http://dx.doi.org/10.1142/s1793005708001021.
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This paper investigates use of genetic programming regression models to forecast home values. Neighborhood prices in a city are represented by a quarterly index. Index values are ratios of each local neighborhood to the global city average real price of homes sold. Relative average neighborhood home attributes, local socioeconomic characteristics, spatial measures, and real mortgage rates explain spatiotemporal variations in the index. To examine efficacy of model estimation, forecasts obtained using genetic programming are compared with those obtained using generalized least squares. Out-of-sample genetic programming predictions of home prices obtained using spatial index models deliver reasonable forecasts of home prices.