EFFECTS OF PARAMETRIC UNCERTAINTY AND TECHNOLOGICAL CHANGE ON INPUT-OUTPUT MODELS.
The article focuses on effects of parametric uncertainty and technological change on input-output models in the U.S. Parameters of input-output (I-O) models are not known exactly; they are subject to two major types of uncertainty. First, statistical errors in compiling the massive data base can int...
| Publicado en: | Review of Economics & Statistics Vol. 59; no. 1; pp. 75 - 82 |
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| Autores principales: | , |
| Formato: | Artículo |
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MIT Press
Feb77
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=4644040&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 4644040 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346535 RMS jtl: Review of Economics & Statistics issn: 00346535 maglogo: N pubinfo: dt: Feb77 vid: 59 iid: 1 pid: 776 pub: MIT Press artinfo: ui: 4644040 10.2307/1924906 ppf: 75 ppct: 7 formats: tig: atl: EFFECTS OF PARAMETRIC UNCERTAINTY AND TECHNOLOGICAL CHANGE ON INPUT-OUTPUT MODELS. aug: au: Bullard III, Clark W. Sebald, Anthony V. su: Input-output analysis Technological innovations Parameter estimation Econometric models Economic models Mathematical economics Uncertainty Errors Statistics United States sug: subj: United States Input-output analysis Technological innovations Parameter estimation Econometric models Economic models Mathematical economics Uncertainty Errors Statistics ab: The article focuses on effects of parametric uncertainty and technological change on input-output models in the U.S. Parameters of input-output (I-O) models are not known exactly; they are subject to two major types of uncertainty. First, statistical errors in compiling the massive data base can introduce uncertainty into all analyses, even those for the base year; and second, uncertainty arises from the fact that I-O coefficients do not remain constant over time, violating an assumption made in most forecasting applications. Since their introduction over thirty years ago, most I-O models have been based on data necessarily aggregated to less than 150 sectors because of computational limitations. Recently, however, it has become possible to manipulate models having as many as 300 to 500 sectors. The problem of parametric forecasting has been the subject of much analysis since the Cambridge Growth Project (1963) introduced the disproportional RAS method of adjusting I-O coefficients to be consistent with updated control totals. In this paper, authors demonstrate a method for restructuring I-O models so parameters more accurately reflect real technological options, a framework in which parametric forecasting may be done with greater certainty. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 1977 holdings: @attributes: islocal: N |
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