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Summary
Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a framework for nonlinear contemporaneous aggregation with possibly stochastic or time-varying weights is developed and different predictors for an aggregate are compared theoretically as well as with simulations. Two examples based on European unemployment and inflation series are used to illustrate the virtue of the theoretical setup and the forecasting results.
Keywords: Forecasting; stochastic aggregation; autoregression; moving average; vector autoregressive process
Online erschienen: 2016-3-16
Erschienen im Druck: 2011-2-1
© 2011 by Lucius & Lucius, Stuttgart
