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題名 Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error Correction Forecasting Models
作者 陳樹衡
Binner, Jane M. ; Gazely, Alicia M. ; Chen, Shu‐Heng ; Chie, Bin‐Tzong
貢獻者 政大經濟系
日期 2004-04
上傳時間 9-Jan-2009 12:16:26 (UTC+8)
摘要 In this article a Divisia monetary index is constructed for the Taiwan economy, and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. The Divisia index is adjusted in two ways to allow for the financial liberalization that Taiwan has experienced since the 1970s. The powerful artificial intelligence technique of neural networks is used and is found to beat the conventional econometric techniques in a simple inflation forecasting experiment. The preferred inflation forecasting model is achieved using networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. The explanatory power of the two innovation‐adjusted Divisia aggregates dominates that of the simple sum counterpart in the majority of cases
關聯 Journal of Contemporary Economic Policy,22(2),213-224
資料類型 article
DOI http://dx.doi.org/10.1093/cep/byh015
dc.contributor 政大經濟系-
dc.creator (作者) 陳樹衡zh_TW
dc.creator (作者) Binner, Jane M. ; Gazely, Alicia M. ; Chen, Shu‐Heng ; Chie, Bin‐Tzong-
dc.date (日期) 2004-04en_US
dc.date.accessioned 9-Jan-2009 12:16:26 (UTC+8)-
dc.date.available 9-Jan-2009 12:16:26 (UTC+8)-
dc.date.issued (上傳時間) 9-Jan-2009 12:16:26 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/23266-
dc.description.abstract (摘要) In this article a Divisia monetary index is constructed for the Taiwan economy, and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. The Divisia index is adjusted in two ways to allow for the financial liberalization that Taiwan has experienced since the 1970s. The powerful artificial intelligence technique of neural networks is used and is found to beat the conventional econometric techniques in a simple inflation forecasting experiment. The preferred inflation forecasting model is achieved using networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. The explanatory power of the two innovation‐adjusted Divisia aggregates dominates that of the simple sum counterpart in the majority of cases-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Journal of Contemporary Economic Policy,22(2),213-224en_US
dc.title (題名) Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error Correction Forecasting Modelsen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1093/cep/byh015en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1093/cep/byh015en_US