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Aplikasi Model GARCH pada Data Inflasi Bahan Makanan Indonesia

Teguh Santoso

Open publisher page 12 citations

Abstract

In the econometric analysis of time series, data with high volatility will be very risky to be used as a basis for doing forecasting. Included in this analysis is the volatility of food inflation in Indonesia. Time series data have a tendency to bully the error variance (error term) are constant over time. Appropriate econometric model to estimate such behavior is called the Autoregressive Conditional Heteroscedasticity (ARCH)model (Engle, 1982) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model developed by Borreslev 1986. This paper attempts to use models of ARCH / GARCH to explain the behavior of food inflation in Indonesia period 2005.1- 2010.6, explained by incorporating elements of ARCH / GARCH this will produce a better estimation.

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What this paper is about

In the econometric analysis of time series, data with high volatility will be very risky to be used as a basis for doing forecasting. Included in this analysis is the volatility of food inflation in Indonesia. Time series data have a tendency to bully the error variance (error term) are constant over time. Appropriate econometric model to estimate such behavior is called the Autoregressive Conditional Heteroscedasticity (ARCH)model (Engle, 1982) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model developed by Borreslev 1986. This paper attempts to use models of ARCH / GARCH to explain the behavior of food inflation in Indonesia period 2005.1- 2010.6, explained by incorporating elements of ARCH / GARCH this will produce a better estimation.

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Available abstract

In the econometric analysis of time series, data with high volatility will be very risky to be used as a basis for doing forecasting. Included in this analysis is the volatility of food inflation in Indonesia. Time series data have a tendency to bully the error variance (error term) are constant over time. Appropriate econometric model to estimate such behavior is called the Autoregressive Conditional Heteroscedasticity (ARCH)model (Engle, 1982) and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model developed by Borreslev 1986. This paper attempts to use models of ARCH / GARCH to explain the behavior of food inflation in Indonesia period 2005.1- 2010.6, explained by incorporating elements of ARCH / GARCH this will produce a better estimation.

Key concepts: Autoregressive conditional heteroskedasticity, Heteroscedasticity, Econometrics, Volatility (finance), Autoregressive model, Conditional variance, Economics, Time series

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