nach oben

Empirical Economics

Erschienen in:

11.12.2019

Nowcasting East German GDP growth: a MIDAS approach

verfasst von: João C. Claudio, Katja Heinisch, Oliver Holtemöller

Erschienen in: Empirical Economics | Ausgabe 1/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.

Vorheriger Artikel Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts

Nächster Artikel Nowcasting Finnish real economic activity: a machine learning approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

For an general overview on obstacles in regional forecasting, see Lehmann and Wohlrabe (2014b).

Other studies, e.g., Lehmann and Wohlrabe (2015), refer to East Germany excluding Berlin.

Data are available at https://www.iwh-halle.de/en/research/data-and-analysis/macroeconomic-reports/macro-data-download/.

In this paper, we use the terms nowcasting and forecasting similarly, both referring to the current quarter.

See IWH-flash-estimate: http://www.iwh-halle.de/en/research/data-and-analysis/macroeconomic-reports/iwh-flash-indicator/.

A drawback of the ifo survey data for East Germany is that they do not include Berlin.

In contrast to Lehmann and Wohlrabe (2017), we also take the indicators into account that are used for disaggregation of annual data to see whether there are information advantages.

To make the indicators comparable to GDP, we report them in quarterly frequency in the figures.

Table 6 in Appendix B provides an overview of the forecasting properties of our benchmark models.

In forecast round F2 and F3, the same benchmark models are used.

They consider quarterly year-on-year forecasts for the sample 1997–2013.

http://www.iwh-halle.de/en/research/data-and-analysis/iwh-macrometer/iwh-indicators-for-east-germany/.

Data on the expenditure side are even published only with a delay of 2–3 years.

Although quarterly data have been published until 1999, these figures cannot be directly used due to revisions of the regional data up to 5 years.

Andreou E, Ghysels E, Kourtellos A (2013) Should macroeconomic forecasters use daily financial data and how? J Bus Econ Stat 31(2):240–251CrossRef

Armesto MT, Engemann KM, Owyang MT (2010) Forecasting with mixed frequencies. Fed Reserve Bank St Louis Rev 92(6):521–536

Bragoli D, Fosten J (2018) Nowcasting Indian GDP. Oxf Bull Econ Stat 80(2):259–282CrossRef

Chow GC, Al Lin (1971) Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. Rev Econ Stat 53(4):372–375CrossRef

Clements MP, Galvão AB (2008) Macroeconomic forecasting with mixed frequency data: forecasting US output growth. J Bus Econ Stat 26(4):546–554CrossRef

Diebold FX (2015) Comparing predictive accuracy, twenty years later: a personal perspective on the use and abuse of Diebold–Mariano tests. J Bus Econ Stat 33(1):1–1CrossRef

Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3):253–263

Drechsel K, Scheufele R (2012) The performance of short-term forecasts of the German economy before and during the 2008/2009 recession. Int J Forecast 28(2):428–445CrossRef

Foroni C, Marcellino M, Schumacher C (2015) U-MIDAS: MIDAS regressions with unrestricted lag polynomials. J R Stat Soc Ser A(178):57–82CrossRef

Ghysels E, Santa-Clara P, Valkanov R (2004) The MIDAS touch: mixed data sampling regression models. Tech. rep

Ghysels E, Santa-Clara P, Valkanov R (2006) Predicting volatility: getting the most out of return data sampled at different frequencies. J Econom 131:59–95CrossRef

Ghysels E, Santa-Clara P, Valkanov R (2007) MIDAS regressions: further results and new directions. Econ Rev 26(1):53–90CrossRef

Giacomini R, White H (2006) Tests of conditional predictive ability. Econometrica 74(6):1545–1578CrossRef

Gießler S, Heinisch K, Holtemöller O (2019) (Since When) Are East and West German business cycles synchronized? IWH Discussion Paper 7, IWH

Granger CW, Newbold P (1977) Forecasting economic time series. Academic Press, London

Heinisch K, Scheufele R (2018) Bottom-up or direct? Forecasting German GDP in a data-rich environment. Empir Econ 54(2):705–745CrossRef

Henzel SR, Lehmann R, Wohlrabe K (2015) Nowcasting regional GDP: the case of the Free State of Saxony. Rev Econ 66(1):71–98

Kopoin A, Moran K, Paré JP (2013) Forecasting regional GDP with factor models: How useful are national and international data? Econ Lett 121(2):267–270CrossRef

Kuzin V, Marcellino M, Schumacher C (2011) MIDAS vs. mixed-frequency VAR: nowcasting GDP in the euro area. Int J Forecast 27(2):529–542CrossRef

Lehmann R, Wohlrabe K (2014a) Forecasting gross value-added at the regional level: Are sectoral disaggregated predictions superior to direct ones? Rev Reg Res Jahrbuch für Regionalwissenschaft 34(1):61–90CrossRef

Lehmann R, Wohlrabe K (2014b) Regional economic forecasting: state-of-the-art methodology and future challenges. Econ Bus Lett 3(4):218–231CrossRef

Lehmann R, Wohlrabe K (2015) Forecasting GDP at the regional level with many predictors. Ger Econ Rev 16(2):226–254CrossRef

Lehmann R, Wohlrabe K (2017) Boosting and regional economic forecasting: the case of Germany. Lett Spat Resour Sci 10(2):161–175CrossRef

Marcellino M, Schumacher C (2010) Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxf Bull Econ Stat 72(4):518–550CrossRef

Rossi B, Inoue A (2012) Out-of-sample forecast tests robust to the choice of window size. J Bus Econ Stat 30(3):432–453CrossRef

Stock JH, Watson MW (2003) Forecasting output and inflation: the role of asset prices. J Econ Lit 47(3):788–829CrossRef

Stock JH, Watson MW (2004) Combination forecasts of output growth in a seven-country data set. J Forecast 23(6):405–430CrossRef

Timmermann A (2006) Forecast combinations. In: Elliot G, Granger CW, Timmermann A (eds) Handbook of forecasting, vol 1. Elsevier, Amsterdam, pp 135–196 chap 4CrossRef

West KD (1996) Inference about predictive ability. Econometrica 64(5):1067–1084CrossRef

Titel: Nowcasting East German GDP growth: a MIDAS approach
verfasst von: João C. Claudio
Katja Heinisch
Oliver Holtemöller
Publikationsdatum: 11.12.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 1/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01810-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 1/2020

Forecasting with supervised factor models

Evaluation of economic forecasts for Austria

Business cycle dating and forecasting with real-time Swiss GDP data

Forecasting of recessions via dynamic probit for time series: replication and extension of Kauppi and Saikkonen (2008)

Long-term prediction intervals of economic time series

Appropriate monetary policy and forecast disagreement at the FOMC

Premium Partner