Similarity Issues in Cointegration Analysis

Abstract

Usually cointegration models involve a dynamic, stochastic component\nas well as deterministic components. This paper identifies relevant\ncointegration models in terms of interpretability and similarity\nwith respect to parameters of deterministic components. Similarity\nimplies that inference on cointegration rank or common trends can be\nseparated from inference on parameters of deterministic components.\nThe idea is that the functional form and thereby the interpretation\nof deterministic components is not questioned in connection with the\nrank test, but it can be tested subsequently. The paper focuses on\nlikelihood based inference in vector autoregressive models.

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