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.

This paper corrects some points in the appendix of the paper: George Kapetanios (2003) "Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses", Studies in Nonlinear Dynamics & Econometrics: Vol. 7: No. 2, Article 2.

Using a new methodology that allows nonlinearities, we find frequent support for external debt sustainability in a number of Latin American countries. Our findings reverse the results for several countries, obtained with traditional unit-root tests and present a richer framework for evaluating...

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares...

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares...

Increasing longevity causes an upward trend in the dependency ratio in many countries. This raises concerns about the financial sustainability of social security schemes, and reform initiatives and proposals abound. It is shown that a fundamental policy choice inevitably arises since a given...

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error...

The rank of the spectral density matrix conveys relevant information in a variety of statistical modelling scenarios. This note shows how to estimate the rank of the spectral density matrix at any given frequency. The method presented is valid for any hermitian positive definite matrix estimate...