Abstract
RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models. Bell (2004) discusses these models and gives some underlying theoretical and computational results. The <b>REGCMPNT</b> program is a Fortran program for performing Gaussian maximum likelihood estimation, signal extraction, and forecasting with RegComponent models. In this paper we briefly examine the nature of RegComponent models, provide an overview of the <b>REGCMPNT</b> program, and then use three examples to show some important features of the program and to illustrate its application to various different RegComponent models.
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