Three linear methods for estimating parameter values of autoregressive moving average models are presented in this article. Simulation results based on different model structures with varying number of observations suggest that the accuracy of some of these procedures is comparable to maximum likelihood estimation. Versions of these approaches can be implemented on any computer system, micro or mainframe, without any programming effort provided that a linear regression package is available. They can also be used to alleviate the problems of autocorrelation in regression, and to generate estimates for multiple times series models. Examples from economic data are used to illustrate the procedures’ capabilities.