Institutional-Repository, University of Moratuwa.  

Modeling Sri Lankan gdp using macroeconomic indicators: an approach using principal component analysis

Show simple item record

dc.contributor.author Karunarathne, AWSP
dc.contributor.author Piyatilake, ITS
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-06T05:53:48Z
dc.date.available 2024-02-06T05:53:48Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22181
dc.description.abstract Economics is conventionally divided into two parts, namely, microeconomics and macroeconomics. While microeconomics delves into individual and business decisions, macroeconomics examines the broader decisions made at the county and government levels, providing a comprehensive understanding of the economy as a whole. The macroeconomic indicators are crucial reflectors of the country’s economic status as they underscore their pivotal role in sustaining economic growth. This study focuses on analyzing the relationship between macroeconomic indicators and the economic growth of Sri Lanka. Nineteen macroeconomic indicators were extracted from the CBSL reports and the data were collected for the period of 1976-2018 from the World Bank website. The choice of PCA is strategic due to the pronounced high correlation among the variables. Subsequently, forward regression analysis is conducted to model relationships with identified principal components, aiming to determine the most influential macroeconomic indicators impacting GDP and to identify the most reliable model with the highest predictive power for GDP. The two principal components extracted from the analysis are found to closely mirror government activities and human capital involvement in the economy. The robust predictive power of these two principal components in forecasting GDP is evident, with an impressive R-squared value of 99.74%. This underscores their reliability and effectiveness in predicting economic growth. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject Macroeconomic indicators en_US
dc.subject PCA en_US
dc.subject Forward regression analysis en_US
dc.title Modeling Sri Lankan gdp using macroeconomic indicators: an approach using principal component analysis en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email sachinikarunarathne94@gmail.com en_US
dc.identifier.email thilinisp@uom.lk en_US


Files in this item

This item appears in the following Collection(s)

  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

Show simple item record