Estimation of Vector Error Correction Models
In this chapter, estimation of VECMs is discussed. The asymptotic properties of estimators for nonstationary models differ in important ways from those of stationary processes. Therefore, in the first section, a simple special case model with no lagged differences and no deterministic terms is considered and different estimation methods for the parameters of the error correction term are treated. For this simple case, the asymptotic properties can be derived with a reasonable amount of effort and the difference to estimation in stationary models can be seen fairly easily. Therefore it is useful to treat this case in some detail. The results can then be extended to more general VECMs which are considered in Section 7.2. In Section 7.3, Bayesian estimation including the Minnesota or Litterman prior for integrated processes is discussed and forecasting and structural analysis based on estimated processes are considered in Sections 7.4–7.6.
KeywordsAsymptotic Property Asymptotic Distribution Consistent Estimator Data Generation Process Vector Error Correction Model
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