  This paper aims to reevaluate the Taylor Rule, through a linear and a
nonlinear method, such that its estimated federal funds rates match those
actually previously implemented by the Federal Reserve Bank. In the linear
method, this paper uses an OLS regression model to find more accurate
coefficients within the same Taylor Rule equation in which the dependent
variable is the federal funds rate, and the independent variables are the
inflation rate, the inflation gap, and the output gap. The intercept in the OLS
regression model would capture the constant equilibrium target real interest
rate set at 2. The linear OLS method suggests that the Taylor Rule
overestimates the output gap and standalone inflation rate's coefficients for
the Taylor Rule. The coefficients this paper suggests are shown in equation
(2). In the nonlinear method, this paper uses a machine learning system in
which the two inputs are the inflation rate and the output gap and the output
is the federal funds rate. This system utilizes gradient descent error
minimization to create a model that minimizes the error between the estimated
federal funds rate and the actual previously implemented federal funds rate.
Since the machine learning system allows the model to capture the more
realistic nonlinear relationship between the variables, it significantly
increases the estimation accuracy as a result. The actual and estimated federal
funds rates are almost identical besides three recessions caused by bubble
bursts, which the paper addresses in the concluding remarks. Overall, the first
method provides theoretical insight while the second suggests a model with
improved applicability.
