Constructing a Composite Vegetable Price Index using Modified Factor Analysis - Couverture souple

Siriwardhana, S. M. C. P.; Thattil, Raphel O.; Abeynayake, Rupika

Couverture souple

ISBN 10 : 3668982775 ISBN 13 : 9783668982772

Editeur : Grin Verlag, 2019

Synopsis

Master's Thesis from the year 2018 in the subject Biology - Miscellaneous, , language: English, abstract: Imagine a world where Sri Lankan vegetable farmers can predict market fluctuations with unprecedented accuracy, minimizing waste and maximizing profits. This groundbreaking study unveils a novel approach to understanding the complexities of the agricultural market in Sri Lanka through the development of a composite vegetable price index. By employing a modified factor analysis method, this research transcends the limitations of traditional data collection, offering a holistic view of price dynamics across a diverse range of vegetables. Delve into the intricacies of Sri Lanka's vegetable sub-sector, where the transformation from traditional farming to agribusiness demands sophisticated market intelligence. Explore the challenges posed by numerous intermediaries, significant post-harvest losses, and the pressing need for improved decision-making tools for farmers, traders, and policymakers alike. This investigation not only addresses the existing gaps in available market data from institutions like the Central Bank of Sri Lanka and HARTI but also contributes to the broader statistical theory of weighted factor analysis. Discover how this innovative index construction enhances internal consistency, explains variance more effectively, and ultimately empowers stakeholders to navigate the volatile landscape of vegetable prices with confidence. This research offers a pathway towards a more efficient and sustainable agricultural economy in Sri Lanka, paving the way for reduced post-harvest losses and increased profitability for farmers. Uncover the power of data-driven decision-making and its potential to revolutionize the vegetable market, ensuring food security and economic stability for the nation. The composite index and its methodology will interest those in agricultural economics, statistics, and policy-making, offering valuable insights for emerging markets and the study of agricultural price dynamics. Explore the intricacies of indicator variables and weighted factor analysis within the context of a real-world application, making it a valuable resource for both academics and practitioners.

A propos du livre

À propos de la quatrième de couverture

Master's Thesis from the year 2018 in the subject Biology - Miscellaneous, , language: English, abstract: This study introduces a modified factor analysis approach to develop a composite vegetable price index. The new method uses scaling by dividing the original variables with its mean, a specific weight for each individual indicator variable and the index assigns a specific numerical value to prices of vegetables for a given month. Initially monthly wholesale prices of nineteen vegetables were considered. As some vegetable prices were highly correlated, ten representative variables for highly correlated variables were retained based on variable-cluster analysis and correlation analysis. Green Beans, Leeks, Cabbage, Tomatoes, Brinjals, Pumpkin, Cucumber, Luffa, Ash Plantains and Green Chili were the indicator variables considered in the index building process. Initially, the grouping pattern in the data was identified through a Preliminary Factor Analysis. This resulted in a single factor explaining a substantial amount of the total variance. The original variables were divided by their means to scale the variables. The weight corresponding to a particular indicator variable was defined by squaring the Eigen vector coefficient of the given variable of the first Principle Component. Then the scaled variables were weighted and used in the final Factor Analysis. A single factor explaining 69.8% of total variance was selected as the composite index. First, the Vegetable Price Index was defined as a linear function of the composite index. Then it was converted into a function of original indicator variables by summarizing constant terms to make it easy to update. Cronbach's alpha was used to verify the internal consistency of the indicator variables. Scaling in mean and weighting improved internal consistency of the variables.

Détails de l'édition

• Éditeur: Grin Verlag
• Date d'édition: 2019
• Langue: anglais
• ISBN 10: 3668982775
• ISBN 13: 9783668982772
• Reliure: Broché
• Nombre de pages: 44
• Coordonnées du fabricant: GRIN Publishing GmbH; info@grin.com; ; Waltherstraße 23; München; 80337; Allemagne