A note on aggregation, disaggregation and forecasting performance

Zellner, Arnold; Tobias, Justin L.

doi:10.1002/1099-131x(200009)19:5<457::aid-for761>3.0.co;2-6

Cited by 108 publications

(61 citation statements)

References 8 publications

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“…This study finds that disaggregation does often not result in lower forecast errors compared to directly modelling the aggregate inflation rate. Zellner and Tobias (2000) also investigate forecasts of GDP growth…”

Section: Some Considerations On Contemporaneous Aggregationmentioning

confidence: 99%

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Drechsel

Scheufele

2017

Empir Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: Some Considerations On Contemporaneous Aggregationmentioning

confidence: 99%

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Drechsel

Scheufele

2017

Empir Econ

View full text Add to dashboard Cite

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“…A disaggregated analysis can also be of interest if the impulse response functions of the components of a vector time series differ only in the short term, but then its results will only differ in the short run with respect to the results of an aggregate study. Certainly the practice of disaggregation has limits (Zellner and Tobias, 2000). In particular, if the quality of data deteriorates when disaggregating or the analyst does not succeed in modelling data properly, then the disaggregated models could be wrong and the forecasts derived from them for the aggregate could be much worse than the forecasts from an aggregate model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors

Espasa¹,

Senra²,

Albacete³

2002

The European Journal of Finance

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In ation in the European Monetary Union is measured by the Harmonized Indices of Consumer Prices (HICP) and it can be analysed by breaking down the aggregate index in two different ways. One refers to the breakdown into price indexes corresponding to big groups of markets throughout the European countries and another considers the HICP by countries. Both disaggregations are of interest because in each one, the component prices are not fully cointegrated, having more than one common factor in their trends. The paper shows that the breakdown by group of markets improves the European in ation forecasts and constitutes a framework in which general and speci c indicators can be introduced for further improvements.

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“…Certainly, the practice of disaggregation has limits (see Zellner and Tobias, 2000). In particular, if the quality of data deteriorates when disaggregating, or the analyst does not succeed in modelling the data properly, then the disaggregated models could be faulty, and the forecasts derived from them for the aggregate could be much worse than the forecasts from an aggregate model.…”

mentioning

confidence: 99%

Econometric modelling for short‐term inflation forecasting in the euro area

Espasa

Albacete

2007

Journal of Forecasting

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This paper examines the problem of forecasting macro-variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision-making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long-run restrictions between the different time series and the shortterm correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block-diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro-variables.

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A note on aggregation, disaggregation and forecasting performance

Cited by 108 publications

References 8 publications

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors

Econometric modelling for short‐term inflation forecasting in the euro area

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