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Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives
Author(s) -
BLAKE ANDREW P.,
KAPETANIOS GEORGE
Publication year - 2003
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/1467-9892.00306
Subject(s) - mathematics , unit root , monte carlo method , nonlinear system , statistical hypothesis testing , econometrics , artificial neural network , statistics , statistical physics , machine learning , computer science , physics , quantum mechanics
. This paper describes artificial neural network based pure significance tests for the unit root hypothesis against nonlinear alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.