Predicting US CPI-Inflation in the presence of asymmetries, persistence, endogeneity, and conditional heteroscedasticity
Afees Salisu () and
Kazeem Isah ()
No 26, Working Papers from Centre for Econometric and Allied Research, University of Ibadan
In this paper, we construct a multi-predictor framework for US inflation by augmenting the traditional Phillips curve-based inflation model with symmetric and asymmetric oil price changes. We show that the underlying predictors of US inflation exhibit persistence, endogeneity and conditional heteroscedasticity effects which have implications on forecast performance. Thus, we employ the Westerlund and Narayan (WN hereafter) (2014) estimator which allows for these effects in the predictive model. Also, we follow the linear multi-predictor set-up by Makin et al. (2014) which is an extension of the bivariate predictive model of WN (2014). Thereafter, we extend the former in order to construct a nonlinear multi-predictor model that allows for asymmetries based on Shin et al. (2014) approach. Using historical quarterly data for relevant variables ranging from 1957 to 2017, we demonstrate that US inflation is better modelled with the proposed multi-predictor model suggesting the significance of oil price in the predictive model for US inflation. In addition, we find that the US inflation forecast is episodic and asymmetric. Among the competing multi-predictor variants, the positive oil price-based variant outperforms all other variants both for in-sample and out-of-sample forecasts. The proposed model also outperforms the autoregressive process for a longer out-of-sample period. Our results are robust to different measures of inflation, multiple in-sample periods and forecast horizon.
Keywords: OECD; Phillips curve; Asymmetries; Inflation forecasts; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: C53 E31 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mac
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Journal Article: Predicting US inflation: Evidence from a new approach (2018)
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