Loan demand forecasting using compositional time series analysis / Krisna Ian Lou Pineda Jumawan.
Material type: TextLanguage: English Publication details: 2008Description: 67 leavesSubject(s): Dissertation note: Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008 Abstract: This study focused on modeling the proportion of the different types of cooperative loan in order to forecast the frequency needed in each type of loan to address the demand of loans with their available limited resources. The data used in this study is a 7-year loan releases of six types, namely, regular loan, emergency loan, petty cash loan, credit line, micro-finance, and teachers and employees? salary loan starting from January 2000 to December 2006. In this study, compositional time series was employed specifically the used of additive log ratio for the transformation of the data and vector autoregressive process (VAR) as its standard time series technique. Accuracy of the predicted proportion of loans from the observed values are 91%, 48%, 83%, 97%, 96%, and 71% for regular loan, emergency loan, petty cash loan, credit line, micro-finance, and teachers and employees? salary loan respectively.Cover image | Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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Thesis | University Library Theses | Room-Use Only | LG993.5 2008 A64 J84 (Browse shelf(Opens below)) | Not For Loan | 3UPML00012193 | |
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Thesis | University Library Archives and Records | Preservation Copy | LG993.5 2008 A64 J84 (Browse shelf(Opens below)) | Not For Loan | 3UPML00012192 |
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008
This study focused on modeling the proportion of the different types of cooperative loan in order to forecast the frequency needed in each type of loan to address the demand of loans with their available limited resources. The data used in this study is a 7-year loan releases of six types, namely, regular loan, emergency loan, petty cash loan, credit line, micro-finance, and teachers and employees? salary loan starting from January 2000 to December 2006. In this study, compositional time series was employed specifically the used of additive log ratio for the transformation of the data and vector autoregressive process (VAR) as its standard time series technique. Accuracy of the predicted proportion of loans from the observed values are 91%, 48%, 83%, 97%, 96%, and 71% for regular loan, emergency loan, petty cash loan, credit line, micro-finance, and teachers and employees? salary loan respectively.
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