¹Departamento de Estadística, Universidad Carlos III de Madrid, España. Profesor ayudante doctor. E-mail: aghierna@est-econ.uc3m.es
²Departamento de Economía Cuantitativa, Universidad Complutense de Madrid, España. Profesor asociado. E-mail: jcasalsc@cajamadrid.es
³Departamento de Economía Cuantitativa, Universidad Complutense de Madrid, España. Profesor titular. E-mail: mjerez@ccee.ucm.es

Proponemos un nuevo procedimiento para detectar raíces unitarias basado en métodos de subespacios. Su planteamiento comporta tres aspectos fundamentales. Primero, la misma metodología se puede aplicar a series individuales o a vectores de series temporales. Segundo, utiliza una familia flexible de criterios de información, cuyas funciones de pérdida se pueden adaptar a las propiedades estadísticas de los datos. Finalmente, no requiere especificar un proceso estocástico para las series analizadas. Se demuestra la consistencia del método y los ejercicios de simulación revelan buenas propiedades en muestras finitas. Además, su aplicación práctica se ilustra mediante el análisis de varias series reales.

Palabras clave: modelos estado espacio, análisis de series temporales, criterios de información, función de pérdida.

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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