{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T11:17:23Z","timestamp":1789816643234,"version":"4.0.1"},"reference-count":44,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2012,5,14]],"date-time":"2012-05-14T00:00:00Z","timestamp":1336953600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2013,4]]},"abstract":"<jats:title>SUMMARY<\/jats:title><jats:p>This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low\u2010frequency variable with a large number of high\u2010frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed\u2010data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright \u00a9 2012 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.2279","type":"journal-article","created":{"date-parts":[[2012,5,15]],"date-time":"2012-05-15T03:23:37Z","timestamp":1337052217000},"page":"392-411","source":"Crossref","is-referenced-by-count":80,"title":["POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES"],"prefix":"10.1002","volume":"28","author":[{"given":"Vladimir","family":"Kuzin","sequence":"first","affiliation":[{"name":"Deutsche Bundesbank Frankfurt am Main Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"Marcellino","sequence":"additional","affiliation":[{"name":"EUI Florence Universit\u00e0 Bocconi Milan Italy"},{"name":"CEPR London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Schumacher","sequence":"additional","affiliation":[{"name":"Deutsche Bundesbank Frankfurt am Main Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2012,5,14]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1162\/REST_a_00045"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2010.01.004"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"AndreouE GhyselsE KourtellosA.2010b.Should macroeconomic forecasters use daily financial data and how?Mimeo University of North Carolina Chapel Hill NC.","DOI":"10.2139\/ssrn.1711899"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/00279501082030011101"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00273"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/073500106000000413"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","unstructured":"BanburaM ModugnoM.2010.Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data. 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