Prediction of Dow Jones Index, US Inflation, and Interest Rate with Kernel Estimator and Vector Error Correction Model

The Dow Jones Industrial Average (DJIA) is the oldest running U.S. stock market index, established by Dow Jones & Company under Charles Dow. Comprising thirty major publicly traded companies, the DJIA is a key indicator of macroeconomic health, reflecting investor confidence and economic stability. This study applies a quantitative research approach to forecast DJIA stock prices, inflation, and U.S. interest rates using time series analysis. Two forecasting methods are compared: Vector Error Correction Model (VECM) and Kernel regression. VECM, a parametric approach, estimates both short- and long-term relationships among economic variables, while Kernel regression, a nonparametric technique, effectively captures complex, nonlinear relationships without strict model assumptions. The results indicate that the Gaussian Kernel method provides the most accurate predictions, achieving a Mean Absolute Percentage Error (MAPE) of 5.72%. The analysis also shows that despite annual fluctuations, the DJIA has exhibited a steady growth trend from 2009 to 2024, with both its starting and ending prices increasing over time. This research is significant for investors, policymakers, and financial analysts, offering insights into market trends and economic indicators. By providing a reliable forecasting model, it aids in better decision-making regarding stock market investments and economic policies.