出版社：Fundação Getulio Vargas, Escola de Pós-Graduação em Economia
摘要：We study the joint determination of the lag length, the dimension of the
cointegrating space and the rank of the matrix of short-run parameters of a
vector autoregressive (VAR) model using model selection criteria. We consider
model selection criteria which have data-dependent penalties for a lack of
parsimony, as well as the traditional ones. We suggest a new procedure which is
a hybrid of traditional criteria and criteria with data-dependant penalties. In
order to compute the fit of each model, we propose an iterative procedure to
compute the maximum likelihood estimates of parameters of a VAR model with
short-run and long-run restrictions. Our Monte Carlo simulations measure the
improvements in forecasting accuracy that can arise from the joint determination
of lag-length and rank, relative to the commonly used procedure of selecting the
lag-length only and then testing for cointegration.
