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| 01732nam 2200337zi 4500 |
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| 001 | 9.908838 |
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| 003 | CaOODSP |
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| 005 | 20230928105306 |
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| 006 | m o d f |
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| 007 | cr |n||||||||| |
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| 008 | 220308t20222022oncd ob f|0| 0 eng d |
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| 040 | |aCaOODSP|beng|erda|cCaOODSP |
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| 043 | |an-cn--- |
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| 086 | 1 |aFB3-5/2022-10E-PDF |
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| 100 | 1 |aChapman, James T. E., |eauthor. |
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| 245 | 10|aMacroeconomic predictions using payments data and machine learning / |cby James T. E. Chapman and Ajit Desai. |
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| 264 | 1|aOttawa, Ontario, Canada : |bBank of Canada = Banque du Canada, |c2022. |
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| 264 | 4|c©2022 |
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| 300 | |a1 online resource (ii, 44 pages) : |bcharts. |
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| 336 | |atext|btxt|2rdacontent |
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| 337 | |acomputer|bc|2rdamedia |
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| 338 | |aonline resource|bcr|2rdacarrier |
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| 490 | 1 |aStaff working paper = |aDocument de travail du personnel, |x1701-9397 ; |v2022-10 |
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| 500 | |a"Last updated: March 3, 2022." |
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| 504 | |aIncludes bibliographical references (pages 27-31). |
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| 520 | |a"This paper demonstrates: (a) that payments systems data which capture a variety of economic transactions can assist in estimating the state of the economy in real time and (b) that machine learning can provide a set of econometric tools to effectively handle a wide variety in payments data and capture sudden and large effects from a crisis"--Abstract. |
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| 650 | 0|aBusiness cycles|xEconometric models. |
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| 650 | 6|aCycles économiques|xModèles économétriques. |
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| 710 | 2 |aBank of Canada, |eissuing body. |
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| 830 | #0|aStaff working paper (Bank of Canada)|v2022-10.|w(CaOODSP)9.806221 |
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| 856 | 40|qPDF|s1.96 MB|uhttps://publications.gc.ca/collections/collection_2022/banque-bank-canada/FB3-5-2022-10-eng.pdf |
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