{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T01:00:08Z","timestamp":1787965208833,"version":"build-2784847793"},"reference-count":28,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T00:00:00Z","timestamp":1554336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Temporal aggregation in general introduces a moving\u2010average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed\u2010frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo simulations and in a forecasting application on US macroeconomic variables, the relevance of considering the MA component in MF mixed\u2010data sampling (MIDAS) and unrestricted MIDAS models (MIDAS\u2013autoregressive moving average (ARMA) and UMIDAS\u2010ARMA). Specifically, the simulation results indicate that the short\u2010term forecasting performance of MIDAS\u2010ARMA and UMIDAS\u2010ARMA are better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting US gross domestic product (GDP) growth, investment growth, and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS\u2010ARMA is better than UMIDAS\u2010ARMA.<\/jats:p>","DOI":"10.1002\/jae.2701","type":"journal-article","created":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T19:05:31Z","timestamp":1550775931000},"page":"688-706","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Mixed\u2010frequency models with moving\u2010average components"],"prefix":"10.1002","volume":"34","author":[{"given":"Claudia","family":"Foroni","sequence":"first","affiliation":[{"name":"European Central Bank  Frankfurt Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"Marcellino","sequence":"additional","affiliation":[{"name":"Bocconi University  Milan Italy"},{"name":"IGIER  Milan Italy"},{"name":"Baffi\u2010Carefin  Milan Italy"},{"name":"CEPR  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalibor","family":"Stevanovic","sequence":"additional","affiliation":[{"name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al  Montr\u00e9al, Qu\u00e9bec Canada"},{"name":"CIRANO  Montr\u00e9al, Qu\u00e9bec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,4,4]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2016.02.004"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Andreou E. 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