nonlinear programming methods

This study addresses the limitations of traditional Consumer Price Index (CPI) calculation methods by proposing an improved framework that incorporates optimized weighting techniques.  The proposed approach utilizes nonlinear programming to better reflect consumer spending behavior in Malaysia, enhancing the accuracy and relevance of inflation measurement.  Empirical analyses reveal fluctuations between the newly generated weights and those provided by the Department of Statistics Malaysia (DOSM), largely influenced by consumer behavior, economic conditions, and supply-

This study explores factors influencing the Consumer Price Index (CPI) through an analysis of economic indicators and predictive models.  It begins with normality testing and correlation analysis to identify significant variables, followed by model fitting using Linear Regression Model (LRM), Nonlinear Regression Model (NRM), and Nonlinear Programming (NLP).  The results show strong positive correlations between CPI and variables like the Coincident Index, Labour, and Volume.  Model comparisons indicate that NRM is the most effective predictor of CPI, with slightly lowe