Loan demand forecasting using compositional time series analysis - Jumawan, Krisna Ian Lou Pineda (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
Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2008
Keywords: Additive log ratio; Compositional time series; Vector autoregressive process; Loan demand forecasting