G2TT
来源类型Discussion paper
规范类型论文
来源IDDP9570
DP9570 Nonparametric Predictive Regression
Elena Andreou
发表日期2013-07-21
出版年2013
语种英语
摘要A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit root processes. In this sense the proposed tests provide a unifying framework for predictive inference, allowing for possibly nonlinear relationships of unknown form, and offering robustness to integration order and functional form. Under the null of no predictability the limit distributions of the tests involve functionals of independent 2 variates. The tests are consistent and divergence rates are faster when the predictor is stationary. Asymptotic theory and simulations show that the proposed tests are more powerful than existing parametric predictability tests when deviations from unity are large or the predictive regression is nonlinear. Some empirical illustrations to monthly SP500 stock returns data are provided.
主题Financial Economics
关键词Functional regression Nonparametric predictability test Nonparametric regression Stock returns Predictive regression
URLhttps://cepr.org/publications/dp9570
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/538405
推荐引用方式
GB/T 7714
Elena Andreou. DP9570 Nonparametric Predictive Regression. 2013.
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