The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach

25 Pages Posted: 22 Jun 2017

See all articles by Periklis Gogas

Periklis Gogas

Democritus University of Thrace - Department of Economics

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Vasilios Plakandaras

Democritus University of Thrace

Rangan Gupta

University of Pretoria - Department of Economics

Date Written: June 21, 2017

Abstract

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the U.S. inflation based on autoregressive and structural models of the term structure. We employ two nonlinear methodologies: the econometric Least Absolute Shrinkage and Selection Operator (LASSO) and the machine learning Support Vector Regression (SVR) method. The SVR has never been used before in inflation forecasting considering the term–spread as a regressor. In doing so, we use a long monthly dataset spanning the period 1871:1–2015:3 that covers the entire history of inflation in the U.S. economy. For comparison reasons we also use OLS regression models as benchmark. In order to evaluate the contribution of the term-spread in inflation forecasting in different time periods, we measure the out-of-sample forecasting performance of all models using rolling window regressions. Considering various forecasting horizons, the empirical evidence suggests that the structural models do not outperform the autoregressive ones, regardless of the model’s method. Thus we conclude that the term-spread models are not more accurate than autoregressive ones in inflation forecasting.

Keywords: U.S. Inflation, forecasting, Support Vector Regression, LASSO

JEL Classification: C22, C45, C53, E31, E37

Suggested Citation

Gogas, Periklis and Papadimitriou, Theophilos and Plakandaras, Vasilios and Gupta, Rangan, The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach (June 21, 2017). Available at SSRN: https://ssrn.com/abstract=2990336 or http://dx.doi.org/10.2139/ssrn.2990336

Periklis Gogas (Contact Author)

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

HOME PAGE: http://www.econ.duth.gr/personel/dep/gkogkas/index.en.shtml

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

HOME PAGE: http://econ.duth.gr/author/papadimi/

Vasilios Plakandaras

Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

Rangan Gupta

University of Pretoria - Department of Economics ( email )

Lynnwood Road
Hillcrest
Pretoria, 0002
South Africa

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