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Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses
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George Kapetanios
Published/Copyright:
July 1, 2003
This paper introduces bootstrap neural network pure significance tests for the no cointegration hypothesis against nonlinear cointegration alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.
Published Online: 2003-7-1
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Stochastic Growth with Increasing Returns: Stability and Path Dependence
- Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses
- Globally-Stabilizing Fiscal Policy Rules
- A Nonlinear Approach to Forecasting with Leading Economic Indicators
- Erratum
- Algorithm
- Reconstructing the Kalman Filter for Stationary and Non Stationary Time Series
