Guesstimation

摘要

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 minimization of squares of ex-post prediction errors, weighted by penalty weights and subject to a learning process. The examples are those of a Monte Carlo analysis of a regression problem and of a dynamic disequilibrium model. It is also an example of an empirical econometric model of the Polish economy.