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首页> 外文期刊>Journal of Time Series Analysis >LIMITING DISTRIBUTIONS OF UNCONDITIONAL MAXIMUM LIKELIHOOD UNIT ROOT TEST STATISTICS IN SEASONAL TIME-SERIES MODELS
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LIMITING DISTRIBUTIONS OF UNCONDITIONAL MAXIMUM LIKELIHOOD UNIT ROOT TEST STATISTICS IN SEASONAL TIME-SERIES MODELS

机译:季节性时序模型中无条件最大似然单元根检验统计量的极限分布

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The likelihood function of a seasonal model, Y_t = ρY_(t-d) + e_t as implemented in computer algorithms under the assumption of stationary initial conditions is a function of ρ which is zero at the point ρ = 1. It is a smooth function for ρ in the above seasonal model with a well-defined maximum regardless of the data-generating mechanism. Gonzalez-Farias (PhD Thesis, North Carolina State University, 1992) proposed tests for unit roots based on maximizing the stationary likelihood function in nonseasonal time series. We extend it to seasonal time series. The limiting distribution of seasonal unit root test statistics based on the unconditional maximum likelihood estimators are shown. Models having a single mean, seasonal means, and a single-trend variable across the seasons are considered.
机译:在固定初始条件下在计算机算法中实现的季节性模型的似然函数Y_t =ρY_(td)+ e_t是ρ的函数,在ρ= 1时为零。对于ρ而言,这是一个平滑函数在上述季节性模型中,无论数据生成机制如何,都具有明确定义的最大值。 Gonzalez-Farias(北卡罗来纳州立大学博士学位论文,1992年)提出了基于最大化非季节时间序列中的平稳似然函数的单位根检验。我们将其扩展到季节性时间序列。显示了基于无条件最大似然估计量的季节性单位根检验统计量的极限分布。考虑具有单一均值,季节性均值和整个季节的单一趋势变量的模型。

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