Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries

Nurhayati, Nunung; Pasaribu, Udjianna S.; Neswan, Oki

doi:10.1155/2012/867056

Cited by 23 publications

(13 citation statements)

References 2 publications

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“…This research made that weight matrix construction was less subjective. In application, the GSTAR is rapidly used to forecast Gross Domestic Product (GDP) West European ( Nurhayati et al, 2012 ), chili price in Bandung's market Fadlilah (2015) , and criminality ( Masteriana and Mukhaiyar, 2019 ). The combination of GSTAR modeling and variogram of spatial analysis was conducted by Mukhaiyar (2015) .…”

Section: Gstar With Modified Inverse Distance – Spatial Weight Matrixmentioning

confidence: 99%

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Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Pasaribu¹,

Mukhaiyar²,

Huda³

et al. 2021

Heliyon

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s The movement of positive people Coronavirus Disease that was discovered in 2019 (Covid-19), written 2019-nCoV, from one location to another has a great opportunity to transmit the virus to more people. High-risk locations for transmission of the virus are public transportations, one of which is the train, because many people take turns in or together inside. One of the policies of the government is physical distancing, then followed by large-scale social restrictions. The keys to the policy are distance and movement. The most famous transportation used for the movement of people among provinces on Java is train. Here a Generalized Space Time Autoregressive (GSTAR) model is applied to forecast infected case of 2019-nCoV for 6 provinces in Java. The specialty of this model is the weight matrix as a tool to see spatial dependence. Here, the modified Inverse Distance Weight matrix is proposed as a combination of the population ratio factor with the average distance of an inter-provincial train on the island of Java. The GSTAR model (1; 1) can capture the pattern of daily cases increase in 2019-nCoV, evidenced by representative results, especially in East Java, where the increase in cases is strongly influenced by other provinces on the island of Java. Based on the Mean Squares of Residuals, it is obtained that the modified matrix gives better result in both estimating (in-sample) and forecasting (out-sample) compare with the ordinary matrix.

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Section: Gstar With Modified Inverse Distance – Spatial Weight Matrixmentioning

confidence: 99%

“…The focus in this paper is the weight matrix. Generally, researchers use uniform weights ( Nurhayati et al, 2012 ), binary ( Mukhaiyar and Pasaribu, 2012 ), or non-uniform weights based on distance. This weight selection process is still subjective.…”

Section: Gstar With Modified Inverse Distance – Spatial Weight Matrixmentioning

confidence: 99%

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Pasaribu¹,

Mukhaiyar²,

Huda³

et al. 2021

Heliyon

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show abstract

“…(1) , ∅ 11 (1) , … … . , ∅ 10 ( ) , ∅ 11 ( ) ) ′ then by using least square estimation ̂= (( ′ ) −1 ′ ) [14][15][16][17][18].…”

Section: Parameter Estimationmentioning

confidence: 99%

G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia

Zewdie

Wubit²,

Ayele³

2018

Turkish Journal of Forecasting

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Among many indicators of climate change, the temperature is a key indicator to take remedial action for world global warming. This finding provides application of space-time models for temperature data, which is selected in three meteorology stations (Mekelle, Adigrat and Adwa) of Northern Ethiopia. The objectives of this research are to see the space-time variations of temperature and to find better forecasting model. The steps for building this model starting from order selection of space and autoregressive order, parameters estimation, a diagnostic check of errors and finally forecasting for the long term. The preliminary model is identified by VAR (vector autoregressive) model and tentatively selects the order by using MIC (minimum information criteria) and uses the autoregressive order for the model and fixes the spatial effect, model parameters are estimated using the least square method. Weighted matrix computed by using queen contiguity criteria. It is found that the model STAR(1,1) and GSTAR(1,1) are two options, finally the best-fitted model is GSTAR(1,1) which has high forecasting performance and smallest RMSEF. The outcome of the forecast indicated that in northern Ethiopia, the weather conditions especially temperature of future is increasing trend in dry seasons in all 3 stations in similar fashion but more consistent and has less variation across the region, and less consistent and high variation within the region and the researcher found that spatial effect has high impact on prediction of models.

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“…Several researchers have developed the spatial weight matrix determination, such as [11] using a uniform spatial weight matrix namely the closest neighbors are given the same weight. [5] uses a binary weight matrix considering the uniform weight as a comparison.…”

Section: Introductionmentioning

confidence: 99%

Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

Yundari

Rizki

2021

CAUCHY

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The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.

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Application of Generalized Space-Time Autoregressive Model on GDP Data in West European Countries

Cited by 23 publications

References 2 publications

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

Modelling COVID-19 growth cases of provinces in java Island by modified spatial weight matrix GSTAR through railroad passenger's mobility

G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia

Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

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