Show simple item record

dc.contributor.authorFORONI, Claudia
dc.contributor.authorMARCELLINO, Massimiliano
dc.contributor.authorSCHUMACHER, Christian
dc.date.accessioned2016-03-09T10:07:21Z
dc.date.available2016-03-09T10:07:21Z
dc.date.issued2015
dc.identifier.citationJournal of the Royal Statistical Society : series A, statistics in society, 2015, Vol. 178, No. 1, pp. 57-82
dc.identifier.issn1467-985X
dc.identifier.urihttps://hdl.handle.net/1814/39301
dc.descriptionFirst published online: 9 December 2013
dc.description.abstractMixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low frequency variable by high frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary to employ distributed lag functions. We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted-MIDAS (U-MIDAS) regressions from linear high frequency models, discuss identification issues and show that their parameters can be estimated by ordinary least squares. In Monte Carlo experiments, we compare U-MIDAS with MIDAS with functional distributed lags estimated by non-linear least squares. We show that U-MIDAS performs better than MIDAS for small differences in sampling frequencies. However, with large differing sampling frequencies, distributed lag functions outperform unrestricted polynomials. The good performance of U-MIDAS for small differences in frequency is confirmed in empirical applications on nowcasting and short-term forecasting euro area and US gross domestic product growth by using monthly indicators.
dc.language.isoen
dc.relation.ispartofJournal of the Royal Statistical Society : series A, statistics in society
dc.titleUnrestricted mixed data sampling (MIDAS) : MIDAS regressions with unrestricted lag polynomials
dc.typeArticle
dc.identifier.doi10.1111/rssa.12043
dc.identifier.volume178
dc.identifier.startpage57
dc.identifier.endpage82
eui.subscribe.skiptrue
dc.identifier.issue1


Files associated with this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record