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首页> 外文期刊>International Journal of Econometrics and Financial Management >Smooth Bootstrap Methods on External Sector Statistics
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Smooth Bootstrap Methods on External Sector Statistics

机译：外部部门统计数据的平滑自举方法

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The investigation of the possibility of a significant difference existing in the parametric and nonparametric bootstrap methods on external sector statistics, and establishing the sample data distribution using the smooth bootstrap is the focus of this study. The root mean square error (RMSE) and the kernel density will be used on the test statistic θ in the determination of such difference. Establishing this difference will lead to more detailed study to discover reasons for such difference. This will also aid the Nigeria economy to aim at improving the performance of the external sector statistics (ESS). The study used secondary data from Central bank of Nigeria (1983-2012). Analysis was carried out using R-statistical package. In the course of the analysis, 17280 scenarios were replicated 200 times. The result shows a significant difference between the performances of the parametric and nonparametric smooth bootstrap methods, namely; wild and pairwise bootstrap respectively. The significantly better performance of the wild bootstrap indicate the possible use of this technique in assessment of comparative performance of ESS with a view to further understanding the better performers in order to identify factors contributing to such better performance. Also, when the sample size and the bootstrap level are very high, the smooth bootstrap or kernel density estimates outperform the pair wise bootstrap notwithstanding that they are nonparametric methods. The kernel density plots revealed that the sampling distribution of the ESS was found to be a Chi-square distribution and was confirmed by the smooth bootstrap methods.
机译：研究在外部部门统计上的参数和非参数自举方法中存在显着差异的可能性，以及使用平滑自举建立样本数据分布是本研究的重点。在确定这种差异时，将在检验统计量θ上使用均方根误差（RMSE）和内核密度。建立这种差异将导致进行更详细的研究以发现这种差异的原因。这也将帮助尼日利亚经济改善外部部门统计数据（ESS）的绩效。该研究使用了尼日利亚中央银行（1983-2012）的二级数据。使用R统计包进行分析。在分析过程中，复制了17280个场景200次。结果表明，参数化和非参数平滑自举方法的性能之间存在显着差异，即：分别为野生和成对引导。野生引导程序的明显更好的性能表明该技术可能用于评估ESS的比较性能，以进一步了解性能更好的人员，从而确定促成这种性能更好的因素。同样，当样本大小和引导程序级别很高时，尽管平滑引导程序或内核密度估计是非参数方法，但它们仍优于成对引导程序。核密度图显示，ESS的采样分布为卡方分布，并通过平滑自举方法得到了证实。

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