The computing resources required for the SSM procedure depend on several factors. The memory requirement for the procedure is largely dependent on the number of observations to be processed and the size of the state vector underlying the specified model. If n denotes the sample size and m denotes the size of the state vector, the memory requirement for the smoothing phase of the Kalman filter is of the order of bytes, ignoring the lower-order terms. If the smoothed component estimates are not needed, then the memory requirement is of the order of bytes. Besides m and n, the computing time for the parameter estimation depends on the size of the parameter vector and how many likelihood evaluations are needed to reach the optimum.