Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form
未知形式条件异方差下时间序列条件均值模型的广义谱检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是时间序列条件均值检验，并明确应用于股票日收益的可预测非线性。
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未知形式条件异方差下时间序列条件均值模型的广义谱检验
- 研究问题
- 在条件异方差及更高阶时变矩形式未知时，如何检验线性或非线性时间序列条件均值模型，并覆盖大量滞后信息？
- 方法与思路
- 作者提出广义谱设定检验，对大量滞后进行检验并降低远期滞后的权重，允许条件信息集具有无限维度及不同参数估计方法。
- 材料陈述的贡献
- 摘要称，检验对未知异方差稳健，参数估计不影响极限分布；股票日收益应用在处理非同步交易后仍发现可预测的均值非线性。
- 阅读关注点
- 应核对无干扰参数性质的成立条件、滞后权重与调节参数的选择，以及模拟中的功效比较和非同步交易调整方法。
- 阅读边界
- 摘要未列明正则条件、具体统计量与功效数值；样本内发现均值非线性，不足以说明样本外预测表现或可获得交易收益。
原文摘要
Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to conditional heteroscedasticity and higher order time-varying moments of unknown form. They check a large number of lags, but naturally discount higher order lags, which is consistent with the stylized fact that economic behaviours are more affected by the recent past events than by the remote past events. No specific estimation method is required, and the tests have the appealing "nuisance parameter free" property that parameter estimation uncertainty has no impact on the limit distribution of the tests. A simulation study shows that it is important to take into account the impact of conditional heteroscedasticity; failure to do so will cause overrejection of a correct conditional mean model. In a horse race competition on testing linearity in mean, our tests have omnibus and robust power against a variety of alternatives relative to some existing tests. In an application, we find that after removing significant but possibly spurious autocorrelations due to nonsynchronous trading, there still exists significant predictable nonlinearity in mean for S&P 500 and NASDAQ daily returns. Copyright 2005, Wiley-Blackwell.