Pooling-based Data Interpolation and Backdating
Abstract
Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the quality of the generated data. Both simulation results and empirical analyses with macroeconomic time series indicate that pooling plays a positive and important role also in this context.Download Info
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Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 299.Length:
Date of creation: 2005
Date of revision:
Handle: RePEc:igi:igierp:299
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Related research
Keywords:Other versions of this item:
- Massimiliano Marcellino, 2007. "Pooling-Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, 01.
- Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-15 (All new papers)
- NEP-ECM-2005-10-15 (Econometrics)
- NEP-ETS-2005-10-15 (Econometric Time Series)
References
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- Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001.
"Diffusion index-based inflation forecasts for the euro area,"
BIS Papers chapters,
in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 109-138
Bank for International Settlements.
- Elena Angelini & Jerome Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series 061, European Central Bank.
- Nijman, T E & Palm, F C, 1986.
"The Construction and Use of Approximations for Missing Quarterly Observations: A Model-based Approach,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 4(1), pages 47-58, January.
- Nijman, T.E. & Palm, F.C., 1986. "The construction and use of approximations for missing quarterly observations: a model-based approach," Open Access publications from Maastricht University urn:nbn:nl:ui:27-6000, Maastricht University.
- Nijman, T.E. & Palm, F.C., 1985. "The construction and use of approximations for missing quarterly observations: A model-based approach," Open Access publications from Tilburg University urn:nbn:nl:ui:12-153292, Tilburg University.
- 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.
- Angelini, Henry, Marcellino, 2002. "interpolation with a large information set," Computing in Economics and Finance 2002 72, Society for Computational Economics.
- Tommaso Proietti, 2004.
"Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited,"
Econometrics
0411011, EconWPA.
- Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
- Graham Elliott & Allan Timmermann, 2008.
"Economic Forecasting,"
Journal of Economic Literature,
American Economic Association, vol. 46(1), pages 3-56, March.
- Elliott, Graham & Timmermann, Allan G, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
- David Hendry & Michael P. Clements, 2001.
"Pooling of Forecasts,"
Economics Papers
2002-W9, Economics Group, Nuffield College, University of Oxford.
- David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
- David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
- Chow, Gregory C & Lin, An-loh, 1971.
"Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series,"
The Review of Economics and Statistics,
MIT Press, vol. 53(4), pages 372-75, November.
- Tom Doan, . "DISAGGREGATE: RATS procedure to implement general disaggregation (interpolation/distribution) procedure," Statistical Software Components RTS00050, Boston College Department of Economics.
- Tom Doan, . "CHOWLIN: RATS procedure to distribute a series to a higher frequency using related series," Statistical Software Components RTS00036, Boston College Department of Economics.
- Timmermann, Allan G, 2005.
"Forecast Combinations,"
CEPR Discussion Papers
5361, C.E.P.R. Discussion Papers.
- Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, Elsevier.
- Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
- Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, School of Economics and Management, University of Aarhus.
- Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
- 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, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Jushan Bai & Serena Ng, 2004. "Confidence Intervals for Diffusion Index Forecasts with a Large Number of Predictor," Econometrics 0408006, EconWPA.
- 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.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Gabriel Fagan & Jérôme Henry & Ricardo Mestre, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 42, European Central Bank.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
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