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