when simply re-estimating an augmented observation set. The Kalman
filter updating technique is developed for both the structural econometric
model and its restricted reduced form. To assess the relative performance
of this approach, a comparison of the forecasting accuracy of the two
varying parameter models and their constant parameter counterpart is
presented.
The following section outlines the varying parameter model, its
implications and correspondence to a particular type of Kalman filter
model and extends the varying parameter structure to it. Then, the
subsequent sections present the specification and estimated parameters
of the recursive model both under constant parameter and varying parameter
regimes. Finally, the forecasting accuracy of the different models
will be presented and discussed.
The Varying Parameter Model
The rationale for incorporating parameter variation stems from
the lack of controlled effects and numerous unobservable forces inherent
to modeling economic systems. Economists are typically constrained to
analyzing secondary data with its attendant errors of reporting and
collection with no assurance that the assumption of constant parameters
holds unambigously. The reasons for this uncertainty are twofold. First,
the actual coefficients may be generated by an underlying non-stationary
process. Or secondly, the true parameters may be stable within the
appropriate or ideal model context but factors such as omitted variables,
errors in variables, aggregation bias and improper functional form may
preclude the formulation of the appropriate model. A varying parameter