Is aggregation necessarily bad

By Y. Grunfeld, Z. Griliches. 1960

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Citations in the corpus

Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, unknown): “…Senra & Albacete (2002) compare the forecasting accuracy of aggregate and disaggregate approaches of forecasting euro area inflation, disaggregating either by coun- The debate about aggregation versus disaggregation of economic variables goes back to Theil (1954) and Grunfeld & Griliches (1960).”
Aggregate Versus Subaggregate Models in Local Area Forecasting (1976, background): “In 1960 Grunfeld and Griliches [11] published an important article in”
Cross-sectional aggregation of non-linear models (2000, background): “Under the in#uence of the seminal contributions of Gorman (1953), Klein (1953, Chapter 8), Theil (1954), Grunfeld and Griliches (1960), and Ando (1971), the literature on aggregation has focussed on two separate but closely related issues; namely the &aggregation problem', and the &model selection…”
Factors Influencing Illinois Farmland Values (2006, background): “This treatment also helps to smooth the impact of random shocks and irregular practices occurring in transactions on land prices and may mitigate problems arising from measurement errors in disaggregate data (Grunfeld and Griliches, 1960; Richter and Brorsen, forthcoming).”
Hierarchical forecasts for Australian domestic tourism (2009, methodology): “Finally, we consider the optimal combination approach recently introduced by Hyndman et al. (2007). Our evaluation of the forecast performance of all five approaches shows that the best performing method for the two tourism time series hierarchies we consider are the top-down method based on forecas”
The Impact of Training on Productivity and Wages: Evidence from British Panel Data (2005, background): “We follow Grunfeld and Griliches (1960) in arguing that the pros of aggregation probably outweigh the cons.”
The economic consequences of euro-area macro-modelling shortcuts (2010, methodology): “The most natural criterion to assess the relevance of the aggregation bias consists of comparing the sum of squared residuals associated with the disaggregate equations with that of the aggregate ones, as first proposed by Grunfeld and Griliches (1960).”
Policy-induced internal migration: An empirical investigation of the Canadian case (2006, unknown): “Grunfeld and Griliches (1960) point out that aggregation may result in gains if specification error is a problem in micro models.”