13858 10 71991 2 199274 document 71991 Weighting schemes in global VAR modelling a forecasting exercise.pdf application/pdf e17890c4ad6a3552236f37e553247b5d MD5 424972 2016-10-24 09:03:07 https://pure.iiasa.ac.at/id/eprint/13858/1/Weighting%20schemes%20in%20global%20VAR%20modelling%20a%20forecasting%20exercise.pdf 13858 1 1 application/pdf text en public cc_by

Weighting schemes in global VAR modelling a forecasting exercise.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/71991 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/71991 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/71991 71996 1 199281 document 71996 indexcodes.txt text/x-c++ 260deb92539d01515fb20cf94ed3cb96 MD5 7406 2016-10-24 09:03:31 https://pure.iiasa.ac.at/id/eprint/13858/6/indexcodes.txt 13858 6 6 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/71991 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/71991 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/71991 archive 5 disk0/00/01/38/58 2016-10-03 09:57:13 2021-08-27 17:41:36 2016-10-03 09:57:13 article show Martin F. Crespo Cuaresma J. 1838 pub prog_pop Global VAR modelling; forecasting; global spillovers We provide a comprehensive analysis of the out-of-sample predictive accuracy of different global vector autoregressive (GVAR) specifications based on alternative weighting schemes to address global spillovers across countries. In addition to weights based on bilateral trade, we entertain schemes based on different financial variables and geodesic distance. Our results indicate that models based on trade weights, which are standard in the literature, are systematically outperformed in terms of predictive accuracy by other specifications. We find that, while information on financial linkages helps improve the forecasting accuracy of GVAR models, averaging predictions by means of simple predictive likelihood weighting does not appear to systematically lead to lower forecast errors. 2017-03 published Springer Berlin Heidelberg 10.1007/s12076-016-0170-x 58 public Letters in Spatial and Resource Sciences 10 1 45-56 TRUE 1864-4031 FALSE no info:eu-repo/semantics/article