Identification through heteroskedasticity : a likelihood-based approach

In this paper we show how the analysis of identification of simultaneous systems of equations with different volatility regimes can be addressed in a conventional likelihood-based setup, generalizing previous works in different directions. We discuss general conditions for identification and one of the results shows that an adequate number of different levels of heteroskedasticity is sufficient to identify the parameters of the structural form without the inclusion of any kind of restriction. A Full Information Maximum Likelihood (FIML) algorithm is discussed