Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Hendry, David & Hubrich, Kirstin, 2006.
"Forecasting Economic Aggregates by Disaggregates,"
CEPR Discussion Papers
5485, C.E.P.R. Discussion Papers.
- Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- John F. Henry & L. Randall Wray, 1998.
"Economic Time,"
Macroeconomics
9811004, University Library of Munich, Germany.
- John F. Henry & L. Randall Wray, 1998. "Economic Time," Economics Working Paper Archive wp_255, Levy Economics Institute.
- Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
- Hendry, David F. & Hubrich, Kirstin, 2011.
"Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
- David F. Hendry & Kirstin Hubrich, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 216-227, April.
- Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003.
"Macroeconomic forecasting in the Euro area: Country specific versus area-wide information,"
European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
- Massimiliano Marcellino & James H. Stock & Mark W. Watson, "undated". "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Hubrich, Kirstin, 2005.
"Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?,"
International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
- Hubrich, Kirstin, 2003. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Working Paper Series 247, European Central Bank.
- Kirstin Hubrich, 2004. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," Computing in Economics and Finance 2004 230, Society for Computational Economics.
- Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011.
"Forecasting large datasets with Bayesian reduced rank multivariate models,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute.
- Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
- Buchen, Teresa & Wohlrabe, Klaus, 2011.
"Forecasting with many predictors: Is boosting a viable alternative?,"
Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
- Buchen, Teresa & Wohlrabe, Klaus, 2010. "Forecasting with many predictors - Is boosting a viable alternative?," Discussion Papers in Economics 11788, University of Munich, Department of Economics.
- Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
- Fagan, Gabriel & Henry, Jerome & Mestre, Ricardo, 2005. "An area-wide model for the euro area," Economic Modelling, Elsevier, vol. 22(1), pages 39-59, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Guilherme Lindenmeyer & Pedro Pablo Skorin & Hudson da Silva Torrent, 2021. "Using boosting for forecasting electric energy consumption during a recession: a case study for the Brazilian State Rio Grande do Sul," Letters in Spatial and Resource Sciences, Springer, vol. 14(2), pages 111-128, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
- Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
- Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021.
"Forecasting the production side of GDP,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
- Dr. Gregor Bäurle & Elizabeth Steiner & Dr. Gabriel Züllig, 2018. "Forecasting the production side of GDP," Working Papers 2018-16, Swiss National Bank.
- Luciani, Matteo, 2014.
"Forecasting with approximate dynamic factor models: The role of non-pervasive shocks,"
International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
- Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
- Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
- Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
- Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
- Ivan Kitov & Oleg Kitov, 2013.
"Does Banque de France control inflation and unemployment?,"
Papers
1311.1097, arXiv.org.
- Kitov, Ivan & KItov, Oleg, 2013. "Does Banque de France control inflation and unemployment?," MPRA Paper 50239, University Library of Munich, Germany.
- Kirstin Hubrich & Guenter Beck & Massimiliano Marcellino, 2000.
"Regional Inflation Dynamics within and across Euro Area Countries and a Comparison with the US,"
Regional and Urban Modeling
283600037, EcoMod.
- Beck, Günter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," CFS Working Paper Series 2007/01, Center for Financial Studies (CFS).
- Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
- Kim, Hyun Hak & Swanson, Norman R., 2014.
"Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence,"
Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
- Huyn Hak Kim & Norman R. Swanson, 2011. "Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence," Departmental Working Papers 201119, Rutgers University, Department of Economics.
- Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, October.
- Esteves, Paulo Soares, 2013.
"Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice,"
Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
- Paulo Esteves, 2011. "Direct vs bottom-up approach when forecasting GDP: reconciling literature results with institutional practice," Working Papers w201129, Banco de Portugal, Economics and Research Department.
- Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
- Klaus Wohlrabe & Teresa Buchen, 2014.
"Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
- Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
- Teresa, Buchen & Wohlrabe, Klaus, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100626, Verein für Socialpolitik / German Economic Association.
- Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
- Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017.
"The role of indicator selection in nowcasting euro-area GDP in pseudo-real time,"
Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
- A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
- Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
- Schumacher, Christian, 2010.
"Factor forecasting using international targeted predictors: The case of German GDP,"
Economics Letters, Elsevier, vol. 107(2), pages 95-98, May.
- Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank.
- Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
- Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
More about this item
JEL classification:
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
NEP fields
This paper has been announced in the following NEP Reports:- NEP-FOR-2015-02-16 (Forecasting)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:vfsc14:100310. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vfsocea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.