This paper looks at issues surrounding the testing of fractional integration and nonlinearity in relation to the forward exchange rate anomaly of Fama (1984). Recent tests for fractional integration and nonlinearity are discussed and used to investigate the behaviour of three exchangerates and...

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following...

Random field regression models provide an extremely flexible way to investigate nonlinearity in economic data. This article introduces a new approach to interpreting such models, which may allow for improved inference about the possible parametric specification of nonlinearity.

In this paper we give an account of the approach to nonlinear econometric modelling proposed by Hamilton (2001) and briefly describe some of the methods of nonlinear optimization that may be used in the Gauss computer program provided by Hamilton for the implementation of his methodology. The...

In this paper we give an account of the approach to nonlinear econometric modelling proposed by Hamilton (2001) and briefly describe some of the methods of nonlinear optimization that may be used in the Gauss computer program provided by Hamilton for the implementation of his methodology. The...

Using nominal and real exchange rates for Ireland relative to Germany and the UK from 1975 to 2003, this article explores likely sources of nonlinearity in Purchasing Power Parity (PPP) relationships and difficulties in employing an I(1)/I(0) econometric framework. Tests for fractional...

This article shows that nonlinearity can provide an explanation for the forward exchange rate anomaly (Fama, 1984). Using sterling-Canadian dollar data and modelling nonlinearity of unspecified form by means of a random field, we find strong evidence of time-wise nonlinearity and, significantly,...