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Full-Text Articles in Social and Behavioral Sciences
Nonparametric Predictive Regression, Ioannis Kasparis, Elena Andreou, Peter C.B. Phillips
Nonparametric Predictive Regression, Ioannis Kasparis, Elena Andreou, Peter C.B. Phillips
Cowles Foundation Discussion Papers
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 …
On Confidence Intervals For Autoregressive Roots And Predictive Regression, Peter C.B. Phillips
On Confidence Intervals For Autoregressive Roots And Predictive Regression, Peter C.B. Phillips
Cowles Foundation Discussion Papers
A prominent use of local to unity limit theory in applied work is the construction of confidence intervals for autogressive roots through inversion of the ADF t statistic associated with a unit root test, as suggested in Stock (1991). Such confidence intervals are valid when the true model has an autoregressive root that is local to unity (τ = 1 + ( c/n )) but are invalid at the limits of the domain of definition of the localizing coefficient c because of a failure in tightness and the escape of probability mass. Consideration of the boundary case shows that these …