  This paper considers both the least squares and quasi-maximum likelihood
estimation for the recently proposed scalable ARMA model, a parametric
infinite-order vector AR model, and their asymptotic normality is also
established. It makes feasible the inference on this computationally efficient
model, especially for economic and financial time series. An efficient block
coordinate descent algorithm is further introduced to search for estimates, and
a Bayesian information criterion with selection consistency is suggested for
model selection. Simulation experiments are conducted to illustrate their
finite sample performance, and a real application on six macroeconomic
indicators illustrates the usefulness of the proposed methodology.
