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

Periklis Gogas, Theophilos Papadimitriou (), Vasilios Plakandaras () and Rangan Gupta ()
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Periklis Gogas: Department of Economics, Democritus University of Thrace, Greece

No 201548, Working Papers from University of Pretoria, Department of Economics

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 (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 E31 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-for, nep-mac, nep-mon and nep-ore
Date: 2015-06
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Related works:
Working Paper: The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach (2019) Downloads
Journal Article: The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach (2017) Downloads
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