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首页> 外文会议>International Conference on Applied Statistics >Development of Generalized Space Time Autoregressive Integrated with ARCH Error (GSTARI - ARCH) Model based on Consumer Price Index Phenomenon at Several Cities in North Sumatera Province
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Development of Generalized Space Time Autoregressive Integrated with ARCH Error (GSTARI - ARCH) Model based on Consumer Price Index Phenomenon at Several Cities in North Sumatera Province

机译：基于消费者价格指数现象在北苏马特省几个城市的消费者价格指数现象的推广空间时间自回归发展

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摘要

Inflation is defined as a situation where generally the price of goods has increased continuously. In order to measure inflation, Statistics of Indonesia (BPS) use the Consumer Price Index (CPI). Inflation in North Sumatera Province monitored through CPI change in several major cities which are Medan, Pematang Siantar, Sibolga, and Padangsidimpuan. The CPI value in these cities was affected by the previous times value and have correlation between one another. In data modeling, data that have correlation in time and spatial is called space time data. One of data modeling methods that can be used to analyze the space time data is the Generalized Space Time Autoregressive (GSTAR) which was introduced by Ruchjana (2002) with assumed constant variance error. Furthermore, time series data such as inflation often have high volatility which implicates on an inconstant value of variance and error. Nainggolan (2011) was introduced GSTAR model with an Autoregressive Conditional Heteroscedastic (ARCH) error, called GSTAR-ARCH model. In this model, the mean equation was modeled by GSTAR model and the variance equation was modeled by the ARCH model. For non stationarity data, we apply GSTAR-Integrated with ARCH error (GSTARI-ARCH) model, and the estimation parameters are using Generalized Least Square (GLS) method as introduced by Nainggolan (2011).
机译：通货膨胀被定义为货物价格持续增加的情况。为了衡量通货膨胀，印度尼西亚（BPS）的统计数据使用消费者价格指数（CPI）。北苏马特省的通货膨胀通过CPI变化监测了Medan，Pematang Siantar，Sibolga和Padangsidimpuan的几个主要城市。这些城市中的CPI值受前一次值的影响，彼此之间具有相关性。在数据建模中，具有时间和空间相关的数据称为空间时间数据。可用于分析空间时间数据的数据建模方法之一是由Ruchjana（2002）引入的广义空间时间自回归（GSTAR），其具有假定的恒定方差误差。此外，诸如膨胀的时间序列数据通常具有高挥发性，这意味着对差异和误差的不稳定值。 Nainggolan（2011）被引入了GSTAR模型，具有归进口条件异源型（ARCH）误差，称为GSTAR-ARCH模型。在该模型中，平均等式由GSTAR模型建模，方案方程由ARCH模型建模。对于非实用性数据，我们将GSTAR集成与ARCH错误（GSTARI-ARCH）模型应用，并且估计参数使用Nainggolan（2011）引入的广义最小二乘（GLS）方法。

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