Author(s) :

Michelle Li.

Volume/Issue :

Abstract :

This study investigates the temporal dynamics of campaign disbursements during the 2016 United States presidential election using daily expenditure data from the Federal Election Commission (FEC), compiled through Kaggle’s public campaign finance dataset. Focusing on six major federal candidates, the research employs linear trend analysis, quadratic trend analysis, and seasonality (residual) analysis to examine how disbursement patterns evolve across the election cycle. Results reveal that overall spending trends are weakly linear, with low explanatory power (R² ≤ 0.08), while quadratic models modestly improve fit (R² ≤ 0.12), uncovering nonlinear behaviors such as mid-period surges and late-cycle accelerations. Seasonality analysis highlights substantial differences in volatility, with one candidate exhibiting pronounced episodic bursts and others maintaining steady expenditure pacing. Collectively, the findings suggest that campaign spending is event-driven rather than time-driven, responding to strategic milestones rather than following smooth temporal growth. Policy recommendations emphasize the need for real-time expenditure monitoring, proportional pacing regulations, and data-driven budgeting frameworks to enhance transparency and efficiency. Future research should incorporate stochastic and autoregressive models (e.g., random walk) and link financial trajectories to key political events to better capture the dynamic and strategic nature of modern campaign finance behavior.