Cross-sectional aggregation of non-linear models

This paper considers the problem of cross-sectional aggregation when the un derlying micro behavioural relations are characterized by general non-linea r specifications. It focuses on forecasting the aggregates, and shows how a n optimal aggregate model can be derived by minimizing the mean squared pre diction errors conditional on the aggregate information. The paper also der ives model selection criteria for distinguishing between aggregate and disa ggregate models when the primary object of the analysis is forecasting the aggregates, and establishes the consistency of the model selection criteria in large samples. In the case of standard non-linear micro relations with additive errors it also provides suitable small sample corrections. For mor e general non-linear specifications we consider bootstrap techniques to cor rect for small sample bias of the proposed model selection criteria. Some o f the ideas in the paper are illustrated using log-linear micro relations, often employed in applied research. The paper also contains an empirical ap plication where log-linear production functions are estimated for the UK ec onomy disaggregated by eight industrial sectors and at the aggregate level over the period 1954-1995, (C) 2000 Elsevier Science S.A. All rights reserv ed.