Trend Function Hypothesis Testing in the Presence of Serial Correlation
Trend Function Hypothesis Testing in the Presence of Serial Correlation
复制标题
存在序列相关性时的趋势函数假设检验
DOI：
10.2307/2998543
复制
发表时间：
1998
期刊：
影响因子：
6.1
通讯作者：
T. Vogelsang
中科院分区：
文献类型：
--
作者：
T. Vogelsang
提出了一种检验单变量时间序列确定性趋势函数参数假设的检验统计量。检验对于误差中的一般形式的序列相关是有效的，并且不需要序列相关参数的估计(参数或非参数)。这些测试对静态误差和单位根误差有效。允许的趋势函数包括可能具有结构变化的时间的线性多项式。渐近结果被应用于一个具有简单线性趋势的模型，并被用来利用战后数据构建八个工业化国家平均国民生产总值增长率的可信区间。
Test statistics are proposed for testing hypotheses about the parameters of the deterministic trend function of a univariate time series. The tests are valid for general forms of serial correlation in the errors and do not require estimates (parametric or nonparametric) of serial correlation parameters. The tests are valid for stationary and unit root errors. Allowable trend functions include linear polynomials of time that may have structural change. Asymptotic results are applied to a model with a simple linear trend and are used to construct confidence intervals for average GNP growth rates for eight industrialized countries using postwar data.