Forecasting core inflation: the case of South Africa. Issue 28 (14th June 2020)

• Record Type: Journal Article
• Authors: Ruch, Franz; Balcilar, Mehmet; Gupta, Rangan; Modise, Mampho P.
• Is Part Of: Applied economics. Volume 52:Issue 28(2020)
• Page Start: 3004
• Page End: 3022
• Publication Date: 2020-06-14
• Subjects: Core inflation -- forecasting -- small- and large-scale vector autoregressive models -- constant and time-varying parameters; C32 -- E3 -- 2E37 -- C22; Economics -- Periodicals; 330
• DOI: 10.1080/00036846.2019.1701181
• Languages: English
• ISSNs: 0003-6846

Abstract

ABSTRACT: Underlying, or core, inflation is likely the most important variable for monetary policy. It is considered to be the optimal nominal anchor as it is stable, excludes relative price shocks, and reflects underlying trends in the behaviour of price-setters and demand conditions in the economy. Despite its importance, there is sparse literature on estimating and forecasting core inflation in South Africa, with the focus still on measuring it. This paper emphasizes predicting core inflation from time-varying parameter vector autoregressive models (TVP-VARs), factor-augmented VARs (FAVAR), and structural break models using quarterly data from 1981Q1 to 2013Q4. We use mean squared forecast errors (MSFE) and predictive likelihoods to evaluate the forecasts. In general, we find that (i) time-varying parameter models consistently outperform constant coefficient models (ii) small TVP-VARs outperform all other models; (iii) models with heteroscedastic errors do better than models with homoscedastic errors; and (iv) allowing for structural breaks does not improve the predictability of core inflation. Overall, our results imply that additional information on the growth rate of the economy and the interest rate is sufficient to forecast core inflation accurately, but the relationship between these three variables needs to be modelled in a time-varying fashion.