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020			_aTHE0010018 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .A79 2023 r Bc.
100	0		_aAryssa Aqilah Binti Hamizul, _eauthor.
245	1	0	_aComparison analysis of pre-post pandemic on macroeconomic : _ba focus on poverty in Malaysia / _cAryssa Aqilah Binti Hamizul
264		1	_aKuantan, Pahang : _bUMPSA, _c2023
264		4	_c© 2023
300			_axi, 111 pages : _billustrations ; _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCentre for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aWhen 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.
610	2	0	_aCentre for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Project _xDissertations
942			_2lcc _cPSM