Bridge models to forecast the euro area GDP

Baffigi, Alberto; Golinelli, Roberto; Parigi, Giuseppe

doi:10.1016/s0169-2070(03)00067-0

Cited by 308 publications

(156 citation statements)

References 21 publications

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“…(1995) for Italy, Baffigi et al (2004) for the euro area and Golinelli and Parigi (2007) for the G7 countries. The main drawback of these studies is that they do not give an overall comparison between direct, demand-side and production-side forecast results.…”

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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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confidence: 99%

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

Drechsel

Scheufele

2017

Empir Econ

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“…As far as euro area GDP is concerned, Angelini et al (2011), Banbura and Rünstler (2011), Marcellino et al (2003), and Rünstler et al (2009) assess the predictive performance of alternative FM nowcasts, while Baffigi et al (2004), Diron (2008) Grassmann and Keereman (2001), and Rünstler and Sédillot (2003) follow the BM approach. In all these papers, the ability of both BM and FM approaches in nowcasting euro area GDP is assessed against simple benchmark time series models.…”

Section: -The State Of the Art In Short Run Modelling For Gdp Forecasmentioning

confidence: 99%

“…Disaggregation can be either about functional GDP components, as in BM by sector (Hahn andSkudelny, 2008, andBurriel, 2012) and BM by expenditure (Baffigi et al 2004), or about modelling single countries belonging to the euro area in both BM and FM (see Marcellino et al, 2003). When the aggregate variable is the target of the forecast, one way of increasing the information set is to disaggregate the target in its components.…”

Section: -The State Of the Art In Short Run Modelling For Gdp Forecasmentioning

confidence: 99%

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The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time

2014

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“…The 22 See for example Lütkepohl (2005). 23 See Baffigi et al (2004) and Angelini et al (2011) for a more detailed discussion of alternative bridge equations.…”

Section: Factor Models (Fm)mentioning

confidence: 99%

Nowcasting GDP in Real-Time: A Density Combination Approach

Aastveit

Gerdrup²,

Jore³

et al. 2011

SSRN Journal

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In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined separately. Then, the nowcasts from the three model classes are combined into a single predictive density. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for the evaluation period 1990Q2-2010Q3. Our results show that the logarithmic score of the predictive densities for U.S. GDP increase almost monotonically as new information arrives during the quarter. While the best performing model class is changing during the quarter, the density nowcasts from our combination framework is always performing well both in terms of logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.JEL-codes: C32, C52, C53, E37, E52.

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Bridge models to forecast the euro area GDP

Cited by 308 publications

References 21 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

The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time

Nowcasting GDP in Real-Time: A Density Combination Approach

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