dc.contributor.author | MARCELLINO, Massimiliano | |
dc.date.accessioned | 2016-07-26T15:10:50Z | |
dc.date.available | 2016-07-26T15:10:50Z | |
dc.date.issued | 2007 | |
dc.identifier.citation | Journal of time series analysis, 2007, Vol. 28, No. 1, pp. 53-71 | |
dc.identifier.issn | 1467-9892 | |
dc.identifier.uri | https://hdl.handle.net/1814/42718 | |
dc.description.abstract | Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improve 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 in this context also. | |
dc.language.iso | en | |
dc.relation.ispartof | Journal of time series analysis | |
dc.subject | Pooling | |
dc.subject | Interpolation | |
dc.subject | Factor model | |
dc.subject | Kalman filter | |
dc.subject | spline | |
dc.subject | C32 | |
dc.subject | C43 | |
dc.subject | C82 | |
dc.title | Pooling-based data interpolation and backdating | |
dc.type | Article | |
dc.identifier.doi | 10.1111/j.1467-9892.2006.00498.x | |
dc.identifier.volume | 28 | |
dc.identifier.startpage | 53 | |
dc.identifier.endpage | 71 | |
eui.subscribe.skip | true | |
dc.identifier.issue | 1 | |