Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2023 International Conference on Management Research and Economic Development
Series Vol. 20 , 13 September 2023
Open
Access | Article
Predicting the Federal Funds Rate: A Linear Regression Analysis
* Author to whom correspondence should be addressed.
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.
Keywords
linear regression, ordinary least squares, predictive model, federal funds rate
References
1. Hamilton, J.D., Jorda, O. (2002) A Model of the Federal Funds Rate Target. J. Political Economy, 110(5), 1135–1167.
2. Ferrando, L., et al. (2015) Interest Rate Sensitivity of Spanish Industries: A Quantile Regression Approach. The Manchester School, 85(2), 212–242.
3. Estrella, A., Mishkin, F.S. (1998) Predicting U.S. Recessions: Financial Variables as Leading Indicators. The Review of Economics and Statistics, 80(1), 45–61.
4. Labonte, M., Makinen, G.E. (2008) Monetary policy and the Federal Reserve: current policy and conditions. Congressional Research Service, Library of Congress.
5. Seber, G.A.F., Lee, A.J. (2012) Linear regression analysis. John Wiley & Sons.
6. Campbell, J.R., et al. (2012) Macroeconomic effects of federal reserve forward guidance. Brookings papers on economic activity, 1-80.
7. Moore, B.J. (1983) Unpacking the Post Keynesian Black Box: Bank Lending and the Money Supply. Journal of Post Keynesian Economics, 5(4), 537–556.
8. Faust, J., Swanson, E.T., Wright, J.H. (2004) Do Federal Reserve Policy Surprises Reveal Superior Information About the Economy? Contributions to Macroeconomics, 4(1), 10.
9. Maier, H., Morgan, N., Chow, C.W.K. (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environmental Modelling & Software, 19(5), 485-494.
10. Kim, J.H. (2019) Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology, 72(6), 558-569.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 2023 International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-915371-83-6
- ISBN (Online)
- 978-1-915371-84-3
- Published Date
- 13 September 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/20/20230172
- Copyright
- 13 September 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited