• Cofractional processes;
  • cointegration rank;
  • fractional cointegration;
  • likelihood inference;
  • vector autoregressive model

We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model, based on the Gaussian likelihood conditional on initial values. We give conditions on the parameters such that the process Xt is fractional of order d and cofractional of order db; that is, there exist vectors β for which βXt is fractional of order db and no other fractionality order is possible. For b=1, the model nests the I(d−1) vector autoregressive model. We define the statistical model by 0 < bleqslant R: less-than-or-eq, slantd, but conduct inference when the true values satisfy 0leqslant R: less-than-or-eq, slantd0b0<1/2 and b0≠1/2, for which β0Xt is (asymptotically) a stationary process. Our main technical contribution is the proof of consistency of the maximum likelihood estimators. To this end, we prove weak convergence of the conditional likelihood as a continuous stochastic process in the parameters when errors are independent and identically distributed with suitable moment conditions and initial values are bounded. Because the limit is deterministic, this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of inline image is mixed Gaussian, while for the remaining parameters it is Gaussian. The limit distribution of the likelihood ratio test for cointegration rank is a functional of fractional Brownian motion of type II. If b0<1/2, all limit distributions are Gaussian or chi-squared. We derive similar results for the model with d = b, allowing for a constant term.