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Inference for non‐stationary time series regression with or without inequality constraints
Author(s) -
Zhou Zhou
Publication year - 2015
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12077
Subject(s) - inference , mathematics , series (stratigraphy) , statistical inference , regression , property (philosophy) , linear regression , regression analysis , boundary (topology) , parameter space , invariant (physics) , econometrics , statistics , computer science , mathematical analysis , artificial intelligence , paleontology , philosophy , epistemology , mathematical physics , biology
Summary We consider statistical inference for time series linear regression where the response and predictor processes may experience general forms of abrupt and smooth non‐stationary behaviours over time. Meanwhile, the regression parameters may be subject to linear inequality constraints. A simple and unified procedure for structural stability checks and parameter inference is proposed. In the case where the regression parameters are constrained, the methodology proposed is shown to be consistent whether or not the true regression parameters are on the boundary of the restricted parameter space via utilizing an asymptotically invariant geometric property of polyhedral cones.