Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement er...

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Main Authors: Hendry, D, Hubrich, K
Format: Working paper
Language:English
Published: European Central Bank 2010
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author Hendry, D
Hubrich, K
author_facet Hendry, D
Hubrich, K
author_sort Hendry, D
collection OXFORD
description To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data.
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spelling oxford-uuid:d927c4af-ea20-47a9-90cf-38985945368a2022-03-27T08:53:51ZCombining disaggregate forecasts or combining disaggregate information to forecast an aggregate.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:d927c4af-ea20-47a9-90cf-38985945368aEnglishDepartment of Economics - ePrintsEuropean Central Bank2010Hendry, DHubrich, KTo forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data.
spellingShingle Hendry, D
Hubrich, K
Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title_full Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title_fullStr Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title_full_unstemmed Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title_short Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate.
title_sort combining disaggregate forecasts or combining disaggregate information to forecast an aggregate
work_keys_str_mv AT hendryd combiningdisaggregateforecastsorcombiningdisaggregateinformationtoforecastanaggregate
AT hubrichk combiningdisaggregateforecastsorcombiningdisaggregateinformationtoforecastanaggregate