ESTIMATION AND INFERENCE ON LONG-RUN EQUILIBRIA: A SIMULATION STUDY

In this paper we study the finite sample properties of some asymptotically equivalent estimators of cointegrating relationships and related test statistics: the Fully Modified Least Squares estimator proposed by Phillips and Hansen (1990), the Dynamic OLS estimator of Saikkonen (1991) and Stock and Watson (1993), the maximum likelihood estimator (reduced rank regression estimator) of Johansen (1988). On the basis of previous Monte Carlo results on this topic, the main objective of our simulation experiments is to study the sensitivity of the finite sample distribution of estimators and test statistics to three features of the DGP of the observable variables, namely, the degree of serial correlation of the cointegrating relationship, the condition of weak exogeneity and the signal-to-noise ratio. To this end, we consider 100 different DGPs and four increasing sample sizes. Besides the usual descriptive statistics, further information about the empirical distributions of interest by means of graphical and statistical methods are provided. In particular, we study size distortion of test statistics using P-value discrepancy plots and estimate the maximal moment exponent of the empirical distribution of estimators.