Predicting the Federal Funds Rate: A Linear Regression Analysis

Abstract

In this research, we aim to utilize linear regression estimated by ordinary least squares (OLS) to construct a predictive model for the federal funds rate in the U.S.. It is a crucial instrument of implementing monetary policy, including financial institutions, investors, and policymakers. To construct the model, we will obtain data on various economic and financial indicators that affect the federal funds rate. This will include macroeconomic variables such as inflation and GDP growth, as well as financial market indicators such as the yield on government bonds and the reserve balance level held by Federal Reserve banks. We will then use this data to fit a linear regression model and evaluate its performance using various statistical metrics. Once the model has been developed, we will use it to make predictions about future federal funds rate actions and determine the leading causes of these movements. Additionally, we will test the model's accuracy given changes in the underlying data and assumptions. The findings of this study will be helpful to a wide range of academic and practical audiences and will offer insightful information on the factors affecting the federal funds rate.