Economics
Md Junayed Hossain
Abstract
Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate, considering ...
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Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate, considering historical GDP and unemployment data collected from the Office of National Statistics (ONS). Both SARIMAX and ARIMA models indicate a negative relationship between GDP and the unemployment rate, although the coefficients are not statistically significant. On the other hand, the Random Forest model has shown its supremacy when it comes to the accuracy of prediction. The results suggest that other factors may have a stronger influence on GDP fluctuations based on the empirical findings. Future research should consider additional variables and advanced modelling techniques to further explore the relationship between GDP and the unemployment rate, contributing to a deeper understanding of the UK economy and informing effective economic management.
Economics
Laban Gasper; George Andwilile Abrahamu
Abstract
Petroleum is one of the vital sources of energy for economic activities and the most traded commodity worldwide. It is crucial to industry and civilization and as it meets a substantial portion of the world's energy requirements, it has a big impact on global politics and intergovernmental relations. ...
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Petroleum is one of the vital sources of energy for economic activities and the most traded commodity worldwide. It is crucial to industry and civilization and as it meets a substantial portion of the world's energy requirements, it has a big impact on global politics and intergovernmental relations. Given the importance of oil to the economy, projecting crude prices has received a lot of focus in the literature. The primary goal of this research is to assess how well Holt's technique and Autoregressive Integrated Moving Average (ARIMA) forecast the petroleum prices in Tanzania. To determine whether the model is more reliable at predicting the prices of petrol in Tanzania, a comparative analysis was perfumed. Monthly data on petroleum prices were extracted from the bank of Tanzania website between February, 2004 to May, 2023. The mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) were used to evaluate the predictive ability of the ARIMA and double exponential smoothing models. The findings indicated that ARIMA (1,1,1) outperformed double exponential smoothing model for forecasting the prices of petrol in Tanzania. The result of this study will guide policy makers and investors in the energy sector to make wise decisions through accurate prediction of the price of petroleum in the future.
Economics
Laban Gasper; Enid Kebby Ernest
Abstract
Consumer price index (CPI) is a socioeconomic statistic that tracks changes over time in the average price of consumer goods and services such as household purchases of fuel, transportation, food and so on that consumers buy, use, or pay for. The purchasing power of everyone is impacted by rising costs, ...
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Consumer price index (CPI) is a socioeconomic statistic that tracks changes over time in the average price of consumer goods and services such as household purchases of fuel, transportation, food and so on that consumers buy, use, or pay for. The purchasing power of everyone is impacted by rising costs, especially if salaries stay the same. Our ability to purchase more things with our TZS reduces when the CPI increases more quickly than earnings, which has an impact on our cost of living. The aim of this study is to use the CPI monthly data from IMF website for the period from Jan 2010 to Dec 2022 to develop a forecasting model by using Holt Winter’s approach. Holt Winter's model based on four equations and popularly known as Triple exponential smoothing is commonly used in forecasting data with trends and seasonality. Holt Winter’s model is composed of four equations relating to level, trend, seasonal and forecast. The results revealed that the Holt winter’s model with smoothing parameters, 0.9 for level, 0.12 for trend, and 0.03 for seasonal was the best model in forecasting Consumer Price Index. The CPI for Tanzania is predicted for the next eighteen months and it has been observed that the trend of CPI is likely to increase in the next eighteen months.
Economics
Laban Gasper
Abstract
People must be well-informed on market swings in today's difficult economic times in order to cut excessive spending. Rising expenditures in a variety of sectors, including business, education, and healthcare can be burdensome for consumers, and accurate forecasting of household is necessary given the ...
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People must be well-informed on market swings in today's difficult economic times in order to cut excessive spending. Rising expenditures in a variety of sectors, including business, education, and healthcare can be burdensome for consumers, and accurate forecasting of household is necessary given the current technological innovation. The Consumer Price Index (CPI) is one of the statistical indicators used to estimate the changes in prices for commodities. Forecasting CPI can assist individuals in developing a plan for making decisions on their daily consumption. This study seeks to develop a SARIMA model for forecasting consumer price indices (CPI) in Tanzania by using data collected from International Monetary Fund (IMF) website from January 2010 to December 2022. Data were evaluated using time series methods such as time plots and stationarity tests. It was discovered that there is seasonality in the CPI index. However, a serial correlogram test was performed using a residual correlogram after which the variable was estimated using the SARIMA model and SARIMA (0, 1, 0) (1, 1, 1)12 was fitted to the time series variable. The residual analysis was explored and because almost all correlations are zero, the SARIMA (1,1,1) (0,1,2)12 model was appropriate for forecasting CPI index in Tanzania. Consumer price index was predicted for the next eighteen months and it has been observed that the trend of CPI is likely to increase in the next eighteen months.
