NONPARAMETRIC BOOTSTRAP PROCEDURES FOR PREDICTIVE INFERENCE BASED ON RECURSIVE ESTIMATION SCHEMES*
NONPARAMETRIC BOOTSTRAP PROCEDURES FOR PREDICTIVE INFERENCE BASED ON RECURSIVE ESTIMATION SCHEMES*
复制标题
基于递归估计方案的预测推理非参数引导程序*
DOI：
10.1111/j.1468-2354.2007.00418.x
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发表时间：
2007
影响因子：
1.5
通讯作者：
Corradi V
中科院分区：
文献类型：
--
作者：
Corradi V
我们引入块引导技术，是（一阶）有效的递归估计框架。此后，我们提出了两个例子，预测精度测试的操作使用我们的新的引导程序。在一个应用中，我们概述了样本外非线性格兰杰因果关系的一致性检验，在另一个应用中，我们概述了在多个替代预测模型中进行选择的检验，所有这些模型都可能被错误指定。在蒙特卡罗调查中，我们比较了有限样本性质的块引导程序的参数引导由于基利安（应用计量经济学杂志14（1999），491-510），在包含和预测精度测试的背景下。在实证分析中，我们发现失业对通货膨胀具有非线性的边际预测内容。
We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out‐of‐sample nonlinear Granger causality, and in the other we outline a test for selecting among multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (Journal of Applied Econometrics14 (1999), 491–510), within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.