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dc.contributor.authorQin, Duoen_US
dc.contributor.authorCagas, Marie Anneen_US
dc.contributor.authorDucanes, Geoffreyen_US
dc.contributor.authorMagtibay-Ramos, Nedelynen_US
dc.description.abstractThis paper compares forecast performance of the ALI method and the MESMs and seeks ways of improving the ALI method. Inflation and GDP growth form the forecast objects for comparison, using data from China, Indonesia and the Philippines. The ALI method is found to produce better forecasts than those by MESMs in general, but the method is found to involve greater uncertainty in choosing indicators, mixing data frequencies and utilizing unrestricted VARs. Two possible improvements are found helpful to reduce the uncertainty: (i) give theory priority in choosing indicators and include theory-based disequilibrium shocks in the indicator sets; and (ii) reduce the VARs by means of the general→specific model reduction procedure.en_US
dc.publisher|aQueen Mary, Univ. of London, Dep. of Economics |cLondonen_US
dc.relation.ispartofseries|aWorking Paper, Department of Economics, Queen Mary, University of London |x554en_US
dc.subject.keywordDynamic factor models, Model reduction, VARen_US
dc.titleForecasting inflation and GDP growth: Comparison of automatic leading indicator (ALI) method with macro econometric structural models (MESMs)en_US
dc.typeWorking Paperen_US

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