Some Exact Distribution Theory for Maximum Likelihood Estimators of Cointegrating Coefficients in Error Correction Models
This paper derives some exact ﬁnite sample distributions and characterizes the tail behavior of maximum likelihood estimators of the cointegrating coeﬀicients in error correction models. It is shown that the reduced rank regression estimator has a distribution with Cauchy-like tails and no ﬁnite moments of integer order. The maximum likelihood estimator of the coeﬀicients in a particular triangular system representation is studied and shown to have matrix t -distribution tails with ﬁnite integer moments to order T - n + r where T is the sample size, n is the total number of variables in the system and r is the dimension of the cointegration space. These results help to explain some recent simulation studies where extreme outliers are found to occur more frequently for the reduced rank regression estimator than for alternative asymptotically eﬀicient procedures that are based on the triangular representation. In a simple triangular system, the Wald statistic for testing linear hypotheses about the columns of the cointegrating matrix is shown to have an F distribution, analogous to Hotelling’s T 2 distribution in multivariate linear regression.
Phillips, Peter C.B., "Some Exact Distribution Theory for Maximum Likelihood Estimators of Cointegrating Coefficients in Error Correction Models" (1992). Cowles Foundation Discussion Papers. 1282.