The paper provides general matrix formulas for minimum mean squared error signal extraction, for a finitely sampled time series whose signal and noise components are nonstationary ARIMA processes. These formulas are quite practical; as well as being simple to implement on a computer, they make it possible to easily derive important general properties of the signal extraction filters. We also extend these formulas to estimates of future values of the unobserved signal, and show how this result combines signal extraction and forecasting.

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The effectiveness of alternate models for estimating trading day and moving holiday effects in economic time series are examined.

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Update on the Development of X-13ARIMA-SEATS

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This paper provides an update to Monsell (2007) to give details of new features found in the current prototype of X-13ARIMA-SEATS.

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Time Series and Seasonal Adjustment Working Papers

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