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Generalized autoregressive (GAR) model: A comparison of maximum likelihood and Whittle estimation procedures using a simulation study. (English) Zbl 1159.62325

Summary: This article evaluates the performance of two estimators namely, the maximum likelihood estimator (MLE) and Whittle’s estimator (WE) through a simulation study for the generalized autoregressive (GAR) model.
As expected, it is found that for the parameters \(\alpha \) and \(\sigma ^{2}\), the MLE and WE have better performance than the method of moments (MOM) estimator. For the parameter \(\delta \), MOM sometimes appears to have a slightly better performance than MLE and WE, possibly due to truncation approximations associated with the hypergeometric functions for calculating the autocorrelation function. However, the MLE and WE can be used in practice without loss of efficiency.

MSC:

62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
65C60 Computational problems in statistics (MSC2010)
33C90 Applications of hypergeometric functions
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