Mathematics Senior Capstone Papers
Document Type
Article
Publication Date
Spring 2023
Abstract
This research was conducted to determine the weight certain taxes and expenditures have over state Gross Domestic Product(GDP) as well as how accurately these predictors can predict future GDP. The motivation behind this project comes from a desire to find the most efficient way to increase the GDP of states with poorer economies. This will improve the quality of life of citizens of these states. To come to a consensus as to what predictors are most influential, Hierarchical Clustering will be used to split the states into four groups. The average of each tax, expenditure and GDP from 2015-2020 will be calculated for each group. This data along with Time Series Forecasting Analysis will indicate which data point is most influential for each group. The Time Series Forecasting will result in an equation that can be used to predict the GDP of 2021 utilizing the GDP of 2020. I will then compare the predicted GDP to the actual GDP for each group. This will lead to future works where I will allow for more predictors and groupings to make more accurate predictions.
Recommended Citation
Nietfeld, Austin Dae, "State Gross Domestic Product Predictions using Hierarchical Clustering and Multivariate Time Series" (2023). Mathematics Senior Capstone Papers. 31.
https://digitalcommons.latech.edu/mathematics-senior-capstone-papers/31