A conditionally heteroskedastic global inflation model

Abstract

PurposeThe purpose of this paper is to propose and test empirically an inflation model containing permanent and transitory heteroskedastic components for the G7 countries. More specifically, recent evidences from the literature are gathered to construct a model with a heteroskedastic global component capturing comovements amongst G7 economies. Moreover, evidence of asymmetric generalized autoregressive conditionally heteroskedastic effects both in the transitory and in the permanent components are taken into account, and the time‐varying variance of each component allows their influence over the observable inflation to change over time. Out‐of‐sample forecasting exercises are used to test the model validity.Design/methodology/approachThe model is written in state‐space form and estimation is carried out in one step via quasi‐maximum likelihood using the augmented Kalman filter, which allows us to compute smoothed estimates of permanent and of transitory components of inflation rates. Out‐of‐sample forecasts are compared against a random walk (RW) and an autoregressive (AR) model of order one. The significance of the differences in forecast accuracy is tested using the Diebold‐Marino test, the forecast encompassing test, and the Pesaran and Timmermann test.FindingsThe proposed model fits the data quite well and has good forecasting capabilities when compared to RW and to AR models of order one. The volatility of the global inflation trend extracted from the model captures the international effects of the “Great Moderation” and of the “Great Recession”. An increase in correlation of inflation for certain country pairs since the start of the “Great Recession” is observed. Moreover, there is evidence of asymmetry in inflation volatility, which is consistent with the idea that higher inflation levels lead to greater uncertainty about future inflation.Originality/valueThis article introduces a new global inflation model with permanent and transitory heteroskedastic components incorporating many recent findings of the literature, and proposes a one step estimation procedure for it. The model fits very well the data and produces good out‐of‐sample forecasts.