03979ntm a2200337 i 4500 0 0 lcc 0 0 10000 10000 2025-05-02 0 CD13573 T000003171 2025-05-02 00:00:00 2025-05-02 PSM 102483 102489 MY-KuUP 20251125111037.0 t||||fr|||| 000 0 ta 250502t20232023my a|||fs|||| 000 0 eng d THE0010018 (Local) Hardback UMPSA eng UMPSA rda PSM .A79 2023 r Bc. Aryssa Aqilah Binti Hamizul, author. Comparison analysis of pre-post pandemic on macroeconomic : a focus on poverty in Malaysia / Aryssa Aqilah Binti Hamizul Kuantan, Pahang : UMPSA, 2023 © 2023 xi, 111 pages : illustrations ; 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023 Includes bibliographical references When the World Health Organization (WHO) announced the outbreak of the Novel Coronavirus disease (Covid-19) as a pandemic in 2020, it linked both the spread of the disease and economic implications. The pandemic has resulted in numerous health issues and deaths, as well as numerous severe economic devastations worldwide, including global poverty. Unfortunately, the public's understanding of poverty has not kept up with changes in poverty, and their low-level poverty concerns demonstrate that they are unaware that poverty has become the primary cause of the economic downturn. If the public continues to ignore this issue, our economy will undoubtedly suffer as the poverty rate rises, which can destabilise an entire country. Thus, this study aims to improve the understanding of the impact of the Covid-19 pandemic on poverty in Malaysia by conducting several analysis. Additionally, the study will utilize macroeconomic indicators such as GDP growth, GDP per capita, the unemployment rate, and inflation to analyze the poverty trends. Exploratory Data Analysis (EDA) will be used to analyze the issue of poverty before and after the pandemic using visualization dashboards. Regression analysis, including multiple linear regression, Ridge regression, and Lasso regression, will be employed to identify the macroeconomic indicators that significantly affect poverty in Malaysia. Moreover, time series models, including Autoregressive Integrated Moving Average (ARIMA) and Vector Autoregressive (VAR), will be used to predict poverty in Malaysia. The analysis revealed fluctuations and significant increases in absolute poverty during certain periods. Absolute poverty has increased between 2016 and 2020, which can be related to the beginning of the Covid-19 pandemic in 2020. Furthermore, the analysis highlights the unemployment rate as the most significant factor influencing poverty levels in Malaysia. This study will also forecast the absolute poverty in Malaysia for the next five years. The forecasted values of absolute poverty using the ARIMA model suggest a relatively stable trend with minor fluctuations. However, it is concerning to note that the forecasted values show an increasing trend in absolute poverty rates, particularly in 2025 and 2026. Based on these findings, recommendations are provided to address the poverty rate and mitigate its impacts, including strengthening social safety nets, enhancing education, and prioritizing the creation of employment opportunities. The study's findings will be of great importance to economists and policymakers in understanding the pandemic's impact on Malaysia's poverty and in formulating strategies to improve the economy and reduce poverty in the country. Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project Dissertations lcc PSM