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LONG MEMORY IN PRODUCER PRICES OF SOUTH AFRICA'S TRADING PARTNERS: ARFIMA MODEL

机译:南非贸易伙伴生产商价格中的长期记忆:ARFIMA模型

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The fractionally integrated autoregressive moving average model, denoted by ARFIMA can be used for the analysis of a univariate time series y_t with long memory. The long run behavior of a time series can be modelled in a flexible way with the ARFIMA model. Models of long-memory processes include fractional Brownian noise[1] and the ARFIMA process introduced by [2] and [3]. Long memory entails that shocks or innovations to a time series do not have a persistent or a short-run transitory effect, but that they last for a long time. The producer prices of 10 trading partner countries of South Africa are modelled as fractionally integrated processes. The countries included are USA, UK, Japan, Korea, Canada, Singapore, Sweden, Israel, South Africa, Switzerland and Euro. Three parametric estimation procedures are used in the present study; one, due to [4] is the Exact Maximum Likelihood (EML) estimator, and the others are the nonlinear least squares (NLS) estimator by [5] and the Modified Profile Likelihood (MPL). These procedures are implemented using the ARFIMA package for the Ox program [6].
机译:由ARFIMA表示的分数积分自回归移动平均模型可用于分析具有长记忆的单变量时间序列y_t。可以使用ARFIMA模型以灵活的方式对时间序列的长期行为进行建模。长内存过程的模型包括分数布朗噪声[1]和由[2]和[3]引入的ARFIMA过程。长时间记忆会导致对时间序列的冲击或创新不会产生持久或短期的短暂影响,而是会持续很长时间。南非的10个贸易伙伴国家的生产者价格被建模为部分集成过程。包括的国家是美国,英国,日本,韩国,加拿大,新加坡,瑞典,以色列,南非,瑞士和欧元。本研究中使用了三种参数估计程序。一个是由于[4],是精确最大似然(EML)估计量,另一个是[5]和修正轮廓似然(MPL)得出的非线性最小二乘(NLS)估计量。这些程序是使用针对Ox程序的ARFIMA软件包实现的[6]。

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