المستودع الأكاديمي جامعة المدينة

Forecasting quarterly German GDP at monthly intervals using monthly IFO business conditions data

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dc.creator Mittnik, Stefan
dc.creator Zadrozny, Peter A.
dc.date 2004
dc.date.accessioned 2013-10-16T07:01:33Z
dc.date.available 2013-10-16T07:01:33Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/18842
dc.identifier ppn:389724351
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/18842
dc.description The paper illustrates and evaluates a Kalman filtering method for forecasting German real GDP at monthly intervals. German real GDP is produced at quarterly intervals but analysts and decision makers often want monthly GDP forecasts. Quarterly GDP could be regressed on monthly indicators, which would pick up monthly feedbacks from the indicators to GDP, but would not pick up implicit monthly feedbacks from GDP onto itself or the indicators. An efficient forecasting model which aims to incorporate all significant correlations in monthlyquarterly data should include all significant monthly feedbacks. We do this with estimated VAR(2) models of quarterly GDP and up to three monthly indicator variables, estimated using a Kalman-filtering-based maximum-likelihood estimation method. Following the method, we estimate monthly and quarterly VAR(2) models of quarterly GDP, monthly industrial production, and monthly, current and expected, business conditions. The business conditions variables are produced by the Ifo Institute from its own surveys. We use early insample data to estimate models and later out-of-sample data to produce and evaluate forecasts. The monthly maximum-likelihood-estimated models produce monthly GDP forecasts. The Kalman filter is used to compute the likelihood in estimation and to produce forecasts. Generally, the monthly German GDP forecasts from 3 to 24 months ahead are competitive with quarterly German GDP forecasts for the same time-span ahead, produced using the same method and the same data in purely quarterly form. However, the present mixed-frequency method produces monthly GDP forecasts for the first two months of a quarter ahead which are more accurate than one-quarter-ahead GDP forecasts based on the purely-quarterly data. Moreover, quarterly models based on purely-quarterly data generally cannot be transformed into monthly models which produce equally accurate intra-quarterly monthly forecasts.
dc.language eng
dc.relation CESifo working papers 1203
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject C32
dc.subject E37
dc.subject ddc:330
dc.subject mixed-frequency data
dc.subject VAR models
dc.subject maximum-likelihood estimation
dc.subject Kalman filter
dc.subject Konjunkturprognose
dc.subject Prognoseverfahren
dc.subject VAR-Modell
dc.subject Maximum-Likelihood-Methode
dc.subject Zustandsraummodell
dc.subject Schätzung
dc.subject Deutschland
dc.title Forecasting quarterly German GDP at monthly intervals using monthly IFO business conditions data
dc.type doc-type:workingPaper


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