scholarly journals Spatial Autoregressive Model untuk Pemodelan Angka Harapan Hidup (AHH) di Provinsi Jawa Timur

2018 ◽  
Vol 11 (2) ◽  
pp. 37-42
Author(s):  
Nova Ratih Intan ◽  
Edy Sulistiyawan
Keyword(s):  
Data Analysis ◽  
Life Expectancy ◽  
Autoregressive Model ◽  
Information Criterion ◽  
Least Square ◽  
P Value ◽  
Akaike's Information Criterion ◽  
Spatial Autoregressive Model ◽  
Ordinary Least Square ◽  
Spatial Autoregressive

Life expectancy is an estimation of life spans that can be attained in a region. Life expectancy is an indicator of the amount of a country’s public health. Life expectancy also can be a benchmark for evaluating the government’s performance in health, social and economic fields. So, we need a statistic model to analyze the factors that affect life expectancy in East Java. The data analysis using multiple linear regression method with Ordinary Least Square (OLS) approach is not enough if some of OLS assumption is not fulfilled. That is why to overcome that problem we use Spatial Autoregressive Model (SAR) method which is used to know the spatial lag on variable response and parameter estimate. According to the data analysis, on the spatial aspect the data has fulfilled the assumption of spatial dependency using Moran’s I with p-value of  0,004315. The spatial weighted matrices that is used is weighted matrices Queen Contiguity. There is the coefficient of determination value (R2) and Akaike’s Information Criterion (AIC) from Spatial Autoregressive Model that is better than OLS consecutively that is 72,459% and 137,36. The significant factor that affect life expectancy on every region/city in East Java is the percentage of households that live clean and health (X7) and the percentage of poor people (X8).   Angka Harapan Hidup adalah perkiraan usia hidup yang dapat dicapai oleh penduduk pada suatu wilayah. Angka harapan hidup digunakan sebagai salah satu indikator derajat kesehatan masyarakat suatu negara. Angka harapan hidup juga dapat menjadi tolak ukur untuk mengevaluasi kinerja pemerintah dalam bidang kesehatan, sosial dan ekonomi. Oleh karena itu diperlukan sebuah pemodelan statistika untuk menganalisis faktor-faktor yang mempengaruhi angka harapan hidup di Jawa Timur. Analisis data menggunakan metode regresi linear berganda dengan pendekatan Ordinary Least Square (OLS) tidak cukup jika beberapa asumsi OLS tidak terpenuhi. Maka untuk mengatasi hal tersebut digunakan metode Spatial Autoregressive Model (SAR) yang digunakan untuk mengetahui lag spasial pada variabel respon dan menaksir parameter. Berdasarkan hasil analisis, pada aspek spasial data telah memenuhi asumsi dependensi spasial menggunakan uji Moran’s I dengan p-value sebesar 0,004315. Matriks pembobot yang digunakan adalah matriks pembobot Queen Contiguity. Diperoleh nilai koefisien determinasi (R2) dan Akaike’s Information Criterion (AIC) dari Spatial Autoregressive Model yang lebih baik dari OLS berturut-turut yaitu 72,459% dan 137,36. Faktor signifikan yang mempengaruhi AHH di setiap kabupaten/kota di Jawa Timur adalah persentase rumah tangga berperilaku hidup bersih dan sehat (X7) dan persentase penduduk miskin (X8).

scholarly journals A Spatial Econometric Analysis of Health in Romania Augmented with Computer Vision

2019 ◽  
Vol 1 (1) ◽  
pp. 288-303
Author(s):  
Florin-Ionuț Jurchiş
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Health Status ◽  
Life Expectancy ◽  
Autoregressive Model ◽  
Urban Areas ◽  
Specific Area ◽  
Spatial Autoregressive Model ◽  
Urban Green Spaces ◽  
Spatial Autoregressive

Abstract The purpose of this study is to analyze the health status in Romania at regional NUTS3 level together with its influential socio-economic factors. Apart from statistical and classical econometrics which are being used in most studies, a spatial analysis has been conducted in order to determine possible similarities and dissimilarities among regions, accounting for the fact that events taking place in a specific area are interrelated with the events in the neighboring regions. The negative distribution of the dependent variable, life expectancy, involves the use of Quantile Spatial Autoregressive Model which also allows to observe the socio-economic and environmental factor influences in different parts of health status proxy distribution. The analysis has led to the conclusion that greater the gaps between rich and poor, or greater the difference between less versus better educated, the greater the differences in health status and life expectancy are. Hence a need for policies designed to reduce territorial health disparities has been identified across Romania’s counties. Moreover, Computer Vision and Deep Learning techniques have been used in order to showcase data collection for urban green spaces variables given that more than half of the globe population is living in urban areas and urban greenery has a high positive influence on health. Using Deep Learning on this particular matter together with the Quantile Spatial Autoregressive Model is an innovative approach that has the main aim of improving the classical econometric modelling.

scholarly journals PEMODELAN DATA RUNTUN WAKTU DENGAN ARIMAX

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nurfitri, Yundari, Shantika Martha
Keyword(s):  
Moving Average ◽  
Information Criterion ◽  
Least Square ◽  
P Value ◽  
Akaike's Information Criterion ◽  
Autoregressive Integrated Moving Average ◽  
Akaike’S Information Criterion ◽  
Schwarz Criterion

Model Autoregressive Integrated Moving Average Exogenous (ARIMAX) merupakan salah satu perluasan dari model ARIMA dengan penambahan variabel exogenous. Dalam penelitian ini, model ARIMAX digunakan untuk pemodelan data harga saham PT. Astra Agro Lestari Tbk (AALI) terhadap data kurs USD bulanan dari tahun 2010 sampai dengan 2018 dengan kurs USD sebagai variabel exogenous dengan tujuan untuk mengestimasi parameter model ARIMAX . Langkah awal dilakukan  uji kestasioneran data. Dari hasil uji kestasioneran data diperoleh hasil bahwa data tidak stasioner sehingga dilakukan proses diferensiasi satu kali pada masing-masing data. Selanjutnya  identifikasi model ARIMA, estimasi parameter model ARIMA menggunakan metode kuadrat terkecil, uji diagnostik pada model ARIMA dan pemilihan model ARIMA terbaik berdasarkan pada nilai probabilitas atau p-value yang signifikan, adjusted r-squared yang lebih besar serta nilai akaike’s information criterion (AIC) dan schwarz criterion (SC) yang terkecil.  Tahapan selanjutnya ialah penambahan variabel exogenous ke dalam model ARIMA sehingga diperoleh model ARIMAX. Estimasi parameter  model ARIMAX dengan menggunakan metode kuadrat terkecil (least square). Setelah mengestimasi parameter maka dilakukan uji diagnostik pada model ARIMAX dengan menggunakan uji Q-Ljung-Box sehingga diperoleh  bahwa harga saham AALI dipengaruhi oleh harga saham itu sendiri sedangkan kurs USD tidak signifikan pada model ARIMAX sehingga menunjukkan bahwa kurs USD tidak berpengaruh signifikan terhadap harga saham AALI. Kata Kunci : Saham, Kurs USD, Akaikes’s Information Criterion (AIC).

scholarly journals Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1448
Author(s):  
Xuan Liu ◽  
Jianbao Chen
Keyword(s):  
Variable Selection ◽  
Autoregressive Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Spatial Effect ◽  
Spatial Effects ◽  
Spatial Autoregressive Model ◽  
Monte Carlo Studies ◽  
Spatial Autoregressive ◽  
Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPLEMENTASI ANGGARAN BERBASIS KINERJA

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Kaillin Lalli Randa ◽  
Ida Ayu Purba Riani ◽  
Balthazar Kreuta
Keyword(s):  
Data Analysis ◽  
Organizational Commitment ◽  
Positive Influence ◽  
Least Square ◽  
Ordinary Least Square ◽  
Average Value ◽  
Data Analysis Technique ◽  
Analysis Technique ◽  
Partial Analysis ◽  
The Mean

The purpose of the study was to analyze what factors influence the Performance Based Budget by using a sample of 87 respondents working at the Secretariat of the Papuan People's Representative Council. While the data analysis technique used is the Ordinary least square (OLS) technique. The results of the study are indicated by the calculation of the mean (mean) of 32 item questions and 87 respondents and the result is 137.31. If the value is compared to the criteria that the author has set, then the average value is included in the "Very Good" category. While the results of the partial analysis of organizational commitment (X1) have a significant and positive influence on the performance-based budget of 1,261. Keywords: Performance Based Budget

scholarly journals Estimation of Partially Specified Spatial Autoregressive Model

2014 ◽  
Vol 2 (3) ◽  
pp. 226-235
Author(s):  
Yuanqing Zhang
Keyword(s):  
Autoregressive Model ◽  
Instrumental Variable ◽  
Error Term ◽  
Sufficient Conditions ◽  
Spatial Autoregressive Model ◽  
Dimensional Parameter ◽  
Instrumental Variable Estimation ◽  
Weighting Matrix ◽  
Finite Dimensional ◽  
Spatial Autoregressive

Abstract In this paper, we study estimation of a partially specified spatial autoregressive model with heteroskedasticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, we propose an instrumental variable estimation. Under some sufficient conditions, we show that the proposed estimator for the finite dimensional parameter is root-n consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and also asymptotically distributed though at a rate slower than root-n. Monte Carlo simulations verify our theory and the results suggest that the proposed method has some practical value.

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