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 |