Subspace-Based Methods to Determine Unit Roots and Cointegrating Ranks

Alfredo García-Hiernaux, José Casals & Miguel Jerez

 

Abstract       

We propose a new procedure to detect unit roots based on subspace methods. It has three main original aspects. First, the same method can be applied to single or multiple time series. Second, it uses a flexible family of information criteria, which loss functions can be adapted to the statistical properties of the data. Last, it does not require the specification of a stochastic process for the series analyzed. This procedure is consistent and a simulation exercise shows that it has good finite sample properties. Its application is illustrated with the analysis of several real time series

 

Key words: State space models, Time series analysis, Information criteria, Loss function.

 

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