An Evaluation Framework for Targeted Indicators Aggregates vs. Disaggregates
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- George Kapetanios & Fotis Papailias, 2021. "UK Economic Conditions during the Pandemic: Assessing the Economy using ONS Faster Indicators," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers (2017-2023 papers hosted by The National Institute of Economic and Social Research) ESCoE DP-2021-10, Economic Statistics Centre of Excellence (ESCoE).
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Estrella, Arturo & Hardouvelis, Gikas A, 1991.
"The Term Structure as a Predictor of Real Economic Activity,"
Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
- Arturo Estrella & Gikas A. Hardouvelis, 1989. "The term structure as a predictor of real economic activity," Research Paper 8907, Federal Reserve Bank of New York.
- Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
- Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006.
"A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series,"
Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
- Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Stock, James & Watson, Mark & Marcellino, Massimiliano, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, Centre for Economic Policy Research.
- J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Kapetanios, George & Papailias, Fotis, 2023. "Nowcasting with Backcalculated Short Time Series Simulations & Empirical Evidence," Discussion Papers escoe-dp-2023-08, Economic Statistics Centre of Excellence.
- repec:nsr:escoet:escoe-tr-18 is not listed on IDEAS
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.- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021.
"Machine learning and oil price point and density forecasting,"
Energy Economics, Elsevier, vol. 102(C).
- Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner P. Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Yihao Lin, 2021. "Machine Learning and Oil Price Point and Density Forecasting," Working Papers Series 544, Central Bank of Brazil, Research Department.
- Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023.
"Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models,"
Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
- Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
- Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
- Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009.
"Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets,"
Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
- Vladimir Yankov & Egon Zakrajsek & Simon Gilchrist, 2009. "Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets," 2009 Meeting Papers 514, Society for Economic Dynamics.
- Simon Gilchrist & Vladimir Yankov & Egon Zakrajsek, 2009. "Credit Market Shocks and Economic Fluctuations: Evidence from Corporate Bond and Stock Markets," NBER Working Papers 14863, National Bureau of Economic Research, Inc.
- Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
- Hendry, David F. & Hubrich, Kirstin, 2006.
"Forecasting economic aggregates by disaggregates,"
Working Paper Series
589, European Central Bank.
- Hendry, David & Hubrich, Kirstin, 2006. "Forecasting Economic Aggregates by Disaggregates," CEPR Discussion Papers 5485, Centre for Economic Policy Research.
- 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.
- Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
- Wang, Zhenzhong & Zhu, Zhengyuan & Yu, Cindy, 2025. "Variable Selection in Macroeconomic Forecasting with Many Predictors," Econometrics and Statistics, Elsevier, vol. 36(C), pages 19-36.
- Donato Ceci & Andrea Silvestrini, 2023.
"Nowcasting the state of the Italian economy: The role of financial markets,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
- Donato Ceci & Andrea Silvestrini, 2022. "Nowcasting the state of the Italian economy: the role of financial markets," Temi di discussione (Economic working papers) 1362, Bank of Italy, Economic Research and International Relations Area.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," ERSA Working Paper Series 41, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Pang, Iris Ai Jao, 2010. "Were Fed’s active monetary policy actions necessary?," MPRA Paper 32496, University Library of Munich, Germany.
- Eickmeier, Sandra & Ng, Tim, 2011.
"Forecasting national activity using lots of international predictors: An application to New Zealand,"
International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
- Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511.
- Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
- Eickmeier, Sandra & Ng, Tim, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Discussion Paper Series 1: Economic Studies 2009,11, Deutsche Bundesbank.
- Bae, Juhee, 2024. "Factor-augmented forecasting in big data," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1660-1688.
- Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
- Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006.
"Interpolation and backdating with a large information set,"
Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
- Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2003. "Interpolation and backdating with a large information set," Working Paper Series 252, European Central Bank.
- Henry, Jérôme & Marcellino, Massimiliano & Angelini, Elena, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, Centre for Economic Policy Research.
- Buncic, Daniel & Tischhauser, Martin, 2017.
"Macroeconomic factors and equity premium predictability,"
International Review of Economics & Finance, Elsevier, vol. 51(C), pages 621-644.
- Buncic, Daniel & Tischhauser, Martin, 2015. "Macroeconomic Factors and Equity Premium Predictability," Economics Working Paper Series 1522, University of St. Gallen, School of Economics and Political Science.
- Massimiliano Marcellino, 2007.
"Pooling‐Based Data Interpolation and Backdating,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
- Massimiliano Marcellino, 2005. "Pooling-based Data Interpolation and Backdating," Working Papers 299, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, Centre for Economic Policy Research.
- Pijush Kanti Das & Prabir Kumar Das, 2025. "Artificial Intelligence and Inflation Forecasting: A Contemporary Perspective," South Asian Journal of Macroeconomics and Public Finance, , vol. 14(1), pages 133-164, June.
More about this item
Keywords
; ; ; ; ;JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CMP-2022-07-11 (Computational Economics)
- NEP-FOR-2022-07-11 (Forecasting)
- NEP-MAC-2022-07-11 (Macroeconomics)
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:nsr:escoet:escoe-tr-17. 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: ESCoE Centre Manager (email available below). General contact details of provider: https://edirc.repec.org/data/escoeuk.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/p/nsr/escoet/escoe-tr-17.html