Macroeconomic model builders attempting to construct forecasting models frequently face constraints of data scarcity in terms of short time series of data, and also of parameter non-constancy and underspecification. Hence, a realistic alternative is often to guess rather than to estimate parameters of such models. This paper concentrates on repetitive guessing (drawing) parameters from iteratively changing distributions, with the straightforward objective function being that of minimisation of squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process. The numerical Monte Carlo examples are those of a regression problem and a dynamic disequilibrium model
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