p-Value Adjustments for Multiple Tests for Nonlinearity

Zacharias Psaradakis A

15 citations0/0 files storedStudies in Nonlinear Dynamics and Econometrics

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Monetary Policy and Economic Impact Statistical Methods and Applications Diverse Scientific and Economic Studies

Abstract

When the hypothesis of linearity of a univariate time series model is tested using a battery of tests for neglected nonlinearity, the probability of one or more tests' leading to a false rejection increases with the number of tests being performed. This paper discusses how this undesirable effect of multiple testing may be controlled by means of some simple and easily implemented procedures. Monte Carlo experiments are used to demonstrate the finite-sample effectiveness of the various methods, and an analysis of the nonlinearity properties of GDP data from five OECD countries is presented as an illustration.

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1996A test for independence based on the correlation dimension 1997A single-blind controlled competition among tests for nonlinearity and chaos 2001Testing for Neglected Nonlinearity in Time Series Models: A Comparison of Neural Network Methods and Alternative Tests 1999The effect of aggregation on nonlinearity

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