On the prediction bias and variance in long-term growth projections

Forest growth is often projected for several subsequent periods. This being the case, the independent variables of a growth model will contain error due to prediction errors in the preceding periods, which cumulate through the simulation process. Thus, the accuracy of growth predictions depends on the number of growth periods. In this paper, the effect of cumulating errors is considered by conducting a simulation study using models estimated from thinning experiments carried out in Finland. The aim of the study is to estimate the prediction bias and the precision of long-term growth projections due to sequential use of growth models. The study demonstrates that it is important to take the residual variation of the models into account in long-term projections in order to reduce the prediction bias. (C) 1997 Elsevier Science B.V.