Short-Term Forecasting of GDP Using Large Monthly Datasets – A Pseudo Real-Time Forecast Evaluation Exercise

Barhoumi, Karim; Benk, Szilárd; Cristadoro, Riccardo; Reijer, Ard den; Jakaitienė, Audronė; Jelonek, P.; Rua, António; Rünstler, Gerhard; Ruth, Karsten; Nieuwenhuyze, Christophe Van

doi:10.2139/ssrn.1685265

Cited by 50 publications

(45 citation statements)

References 92 publications

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“…This confirms findings of earlier studies, as summarized by Barhoumi et al (2008). Table 11: Relative RMSEs of the forecasting models over simple country-AR benchmark models (estimated over the entire sample period) Note: Month 1, Month 2, Month 3 refers to the month in which the forecast is made; F, R refers to full and restricted data set, respectively (for the PMI model, we report results using individual countries' PMI in the F column, while the R column reports results in which we use the euro area PMI to construct forecasts for individual euro area countries).…”

Section: Forecasting Performance Compared To Simple Ar Benchmarkssupporting

confidence: 93%

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Forecasting Economic Growth in the Euro Area During the Great Moderation and the Great Recession

Lombardi

Maier

2011

SSRN Journal

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Section: Forecasting Performance Compared To Simple Ar Benchmarkssupporting

confidence: 93%

“…The usefulness of factor models for forecasting has been documented extensively (for the euro area, Barhoumi et al, 2008, provide a recent overview). However, two issues remain still unresolved.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Economic Growth in the Euro Area During the Great Moderation and the Great Recession

Lombardi

Maier

2011

SSRN Journal

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“…The paper is closely related to Banbura and Runstler (2007), Angelini et al (2008) and Barhoumi et al (2008), who use the approximate dynamic factor model proposed by Giannone et al (2008) to compute euro area GDP forecasts which are continuously updated as well. As in these proposals, we diverge from the euro area univariate bridge equations employed by Runstler and Sedillot (2003) and Diron (2006) and from those which try to measure high-frequency objects (as real-time activity) on a daily or hourly basis, such as Aruoba, Diebold and Scotti (2009).…”

Section: Introductionmentioning

confidence: 99%

Introducing the Euro-Sting: Short Term Indicator of Euro Area Growth

2008

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We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data set, we show that our simple factor model algorithm, which uses a clear, easy-to-replicate methodology, is able to forecast the euro area GDP growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement. In this context, we provide examples showing how data revisions and data availability a¤ect point forecasts and forecast uncertainty.Keywords: Business Cycles, Output Growth, Time Series. JEL Classi…cation: E32, C22, E27We th a n k th e e d ito r a n d th e tw o re fe re e s fo r e x tre m e ly va lu a b le c o m m e nts w h ich h ave g re a tly im p rove d th e c o nte nts o f o u r p a p e r. We a lso th a n k Fra n c is D ie b o ld , J a n J a c o b s, S im o n va n N o rd e n , K e n Wa llis a n d th e p a rtic ip a nts a t th e 5 th Wo rk sh o p o n Fo re c a stin g Te ch n iq u e s, a t th e 5 th C o llo q u iu m o n M o d e rn To o ls fo r B u sin e ss C y c le A n a ly sis, a n d a t th e inte rn a l se m in a r se rie s o f B a n c o d e E sp a ñ a , th e E u ro p e a n C e ntra l B a n k , C E M F I, th e U n ive rsity C a rlo s I I I, th e U n ive rsity o f A lic a nte a n d th e U n ive rsity o f N ava rra fo r h e lp fu l c o m m e nts a n d su g g e stio n s. We re c e ive d e x tre m e ly va lu a b le re se a rch a ssista n c e fro m C a m ilo U llo a . We a re a lso g ra te fu l to th e E u ro A re a U n it o f th e B a n k o f S p a in fo r a ll th e p re lim in a ry m e e tin g s th a t le a d to th is p a p e r. F in a lly, w e a re th a n k fu l to J u a n A y u so fo r h is c le ve r id e a c o n c e rn in g th e n a m e o f th e in d ic a to r. A ny re m a in in g e rro rs a re o u r ow n re sp o n sib ility. P a rt o f th e p a p e r w a s w ritte n w h e n th e …rst a u th o r w a s v isitin g th e B a n k o f S p a in . M a x im o C a m a ch o th a n k s th e …n a n c ia l su p p o rt o f th e p ro je c t S E J 2 0 0 6 -1 5 1 7 2 . T h e v ie w s in th is p a p e r a re th o se o f th e a u th o rs a n d d o n o t re p re se nt th e v ie w s o f th e B a n k o f S p a in o r th e E u ro S y ste m .

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“…The technique can be found in Krolzig and Hendry (2001). Marcellino and Schumacher (2008) and Barhoumi, Benk, Cristadoro, Reijer, Jakaitiene, Jelonek, Rua, Ruestler, Ruth, and Nieuwenhuyze (2008) are two examples of this approach. The common feature of these approaches is an explicit modeling of the relation between data with different frequencies.…”

Section: Introduction To the Problemsmentioning

confidence: 99%

A Note on Updating Forecasts When New Information Arrives Between Two Periods

Chen

2009

SSRN Journal

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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 EconStor may AbstractIn this note the author discusses the problem of updating forecasts in a time-discrete forecasting model when information arrives between the current period and the next period. To use the information that arrives between two periods, he assumes that the process between two periods can be approximated by a linear interpolation of the timediscrete forecasting model. Based on this assumption the author drives the optimal updating rule for the forecast of the next period when new information arrives between the current period and the next period. He demonstrates by theoretical arguments and empirical examples that this updating rule is simple, intuitively appealing, defendable and useful. JEL: C32

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Short-Term Forecasting of GDP Using Large Monthly Datasets – A Pseudo Real-Time Forecast Evaluation Exercise

Cited by 50 publications

References 92 publications

Forecasting Economic Growth in the Euro Area During the Great Moderation and the Great Recession

Forecasting Economic Growth in the Euro Area During the Great Moderation and the Great Recession

Introducing the Euro-Sting: Short Term Indicator of Euro Area Growth

A Note on Updating Forecasts When New Information Arrives Between Two Periods

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