Skip to content
All library documents

Using Regression and Machine Learning to Reevaluate the Taylor Rule

Article arXiv papers · Author: Alper Deniz Karakas
Read the original

Summary

This paper reevaluates the Taylor Rule by estimating federal funds rates with both a linear regression and a nonlinear machine learning model. In the linear approach, ordinary least squares estimates coefficients for inflation, the inflation gap, and the output gap, with the intercept representing the equilibrium real-rate target. The paper reports that the conventional rule assigns overly large coefficients to the output gap and standalone inflation rate.

The nonlinear system uses inflation and the output gap as inputs and the federal funds rate as its output, with gradient descent minimizing the difference between estimated and historically implemented rates. It reports a closer fit than the linear approach, except during three recessions associated with bubble bursts. The abstract provides no sample dates, detailed performance measures, or model design specifics, limiting assessment of generalization. It frames the regression as theoretically informative and the nonlinear model as more applicable to rate estimation.

Key ideas

  • The paper compares a linear Taylor Rule regression with a nonlinear machine learning model.
  • The regression estimates coefficients for inflation, the inflation gap, and the output gap.
  • The nonlinear model maps inflation and the output gap to the federal funds rate.
  • The paper reports a closer historical fit from the nonlinear model, with exceptions during three bubble-related recessions.
  • The abstract does not provide performance metrics or enough detail to assess generalization.

Tags

Machine learningStatisticsFixed incomeUS markets
Full text
# Reevaluating the Taylor Rule with Machine Learning


# Reevaluating the Taylor Rule with Machine Learning









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.