Estimation of vector error correction models with mixed-frequency data
DC Field | Value | Language |
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dc.contributor.author | Seong, Byeongchan | - |
dc.contributor.author | Ahn, Sung K. | - |
dc.contributor.author | Zadrozny, Peter A. | - |
dc.date.available | 2019-03-09T02:01:50Z | - |
dc.date.issued | 2013-03 | - |
dc.identifier.issn | 0143-9782 | - |
dc.identifier.issn | 1467-9892 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/14795 | - |
dc.description.abstract | Vector autoregressive (VAR) models with error-correction structures (VECMs) that account for cointegrated variables have been studied extensively and used for further analyses such as forecasting, but only with single-frequency data. Both unstructured and structured VAR models have been estimated and used with mixed-frequency data. However, VECMs have not been studied or used with mixed-frequency data. The article aims partly to fill this gap by estimating a VECM using the expectation-maximization (EM) algorithm and US data on four monthly coincident indicators and quarterly real GDP and, then, using the estimated model to compute in-sample monthly smoothed estimates and out-of-sample monthly forecasts of GDP. Because the model is treated as operating at the highest monthly frequency and the monthly-quarterly data are used as given (neither interpolated to all-monthly data, nor aggregated to all-quarterly data), the application is expected to be unbiased and efficient. A Monte Carlo analysis compares the accuracy of VECMs estimated with the given mixed-frequency data vs. with their single-frequency temporal aggregate. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.title | Estimation of vector error correction models with mixed-frequency data | - |
dc.type | Article | - |
dc.identifier.doi | 10.1111/jtsa.12001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF TIME SERIES ANALYSIS, v.34, no.2, pp 194 - 205 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000315302800006 | - |
dc.identifier.scopusid | 2-s2.0-84874189608 | - |
dc.citation.endPage | 205 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 194 | - |
dc.citation.title | JOURNAL OF TIME SERIES ANALYSIS | - |
dc.citation.volume | 34 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Missing data | - |
dc.subject.keywordAuthor | cointegration | - |
dc.subject.keywordAuthor | state-space model | - |
dc.subject.keywordAuthor | Kalman filter | - |
dc.subject.keywordAuthor | expectation maximization algorithm | - |
dc.subject.keywordAuthor | smoothing | - |
dc.subject.keywordPlus | TEMPORAL AGGREGATION CONSTRAINTS | - |
dc.subject.keywordPlus | DYNAMIC-FACTOR | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordPlus | COINTEGRATION ANALYSIS | - |
dc.subject.keywordPlus | MAXIMUM-LIKELIHOOD | - |
dc.subject.keywordPlus | REGRESSION-MODELS | - |
dc.subject.keywordPlus | INTERPOLATION | - |
dc.subject.keywordPlus | GDP | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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