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State Space Models and the Kalman Filter in EViews

The State Space model is a time-series econometric framework for analyzing dynamic systems, integrating observable data with unobserved components like hidden trends or expectations. The structure comprises signal equations and state equations. The Kalman filter provides an optimal recursive algorithm for computing forecasts, updating, and smoothing states In EViews, you implement this using the Sspace object. Declare observation equations using the @SIGNAL keyword and transition equations using @STATE. Variances are defined directly via [var=...] or parameterized using @ename and @evar. The Proc - Define State Space... tool can automate this specification. Click Estimate to evaluate unknown parameters via maximum likelihood (MLE), and use Forecast for dynamic, smoothed, or n-step ahead predictions.