On Confidence Intervals for Autoregressive Roots and Predictive Regression

• Main Author: Peter C. B. PHILLIPS
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Autoregressive root; Confidence belt; Confidence interval; Coverage probability; Local to unity; Localizing coefficient; Predictive regression; Tightness; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/1830; https://ink.library.smu.edu.sg/context/soe_research/article/2829/viewcontent/ConfidenceIntervalsAutoregressiveRootsPredictiveRegression_2012_pp.pdf
• Institution: Singapore Management University

Local to unity limit theory is used in applications to construct confidence intervals (CIs) for autoregressive roots through inversion of a unit root test (Stock (1991)). Such CIs are asymptotically valid when the true model has an autoregressive root that is local to unity (rho = 1 + c/n), but are shown here to be 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. Failure at the boundary implies that these CIs have zero asymptotic coverage probability in the stationary case and vicinities of unity that are wider than O(n(-1/3)). The inversion methods of Hansen (1999) and Mikusheva (2007) are asymptotically valid in such cases. Implications of these results for predictive regression tests are explored. When the predictive regressor is stationary, the popular Campbell and Yogo (2006) CIs for the regression coefficient have zero coverage probability asymptotically, and their predictive test statistic Q erroneously indicates predictability with probability approaching unity when the null of no predictability holds. These results have obvious cautionary implications for the use of the procedures in empirical practice.