Forecasting core inflation: the case of South Africa

Abstract

Forecasting and estimating core inflation has recently gained attention, especially for inflation targeting countries, following research showing that targeting headline inflation may not be optimal; a Central Bank can miss the signal due to the noise. Despite its importance there is sparse literature on estimating and forecasting core inflation in South Africa, with the focus still on measuring core inflation. This paper emphasises predicting core inflation using large time-varying parameter vector autoregressive models (TVP-VARs), factor augmented VAR, 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) small TVP-VARs consistently outperform all other models; (ii) models where the errors are heteroscedastic do better than models with homoscedastic errors; (iii) models assuming that the forgetting factor remains 0.99 throughout the forecast period outperforms models that allow for the forgetting factors to change with time; and (iv) allowing for structural break does not improve the predictability of core inflation. Overall, our results imply that additional information on the growth rate of the economy and interest rate is sufficient to forecast core inflation accurately, but the relationship between these three variables needs to be modelled in a time-varying (nonlinear) fashion.JEL Classification: C22, C32, E27, E31.

Big data forecasting of South African inflation

“…Their findings suggest that the locally linear model tree provides forecasts that can compete with the linear autoregressive model and is generally superior over longer horizons. In addition, Ruch et al (2020) derive forecasts for quarterly measures of core inflation in South Africa with the aid of time-varying parameter vector autoregressive models (TVP-VARs), factor-augmented VARs, and structural break models to show that small TVP-VARs outperform all their other models, where additional information on the growth rate of the economy and the interest rate is sufficient to forecast core inflation accurately.…”

Structural breaks, Twitter, and the stock liquidity of internet Dot-com company: Evidence from US companies

“…That is why academia, policy makers, and other concerned stakeholders conduct ex ante or ex post research to investigate theses structural changes in numerous financial and economic variables. Ruch et al (2020), Anguyo et al (2020), Hegerty (2020), Nath and Sarkar (2019), Gil-Alana (2019), Orlowski (2017) examine the break dates and the impact of structural changes on inflation series with regards to other variables. Ruch et al (2020) predict inflation variables using factor-augmented VARs (FAVAR), time-varying parameter vector autoregressive models (TVP-VARs), and structural break models.…”

“…Ruch et al (2020), Anguyo et al (2020), Hegerty (2020), Nath and Sarkar (2019), Gil-Alana (2019), Orlowski (2017) examine the break dates and the impact of structural changes on inflation series with regards to other variables. Ruch et al (2020) predict inflation variables using factor-augmented VARs (FAVAR), time-varying parameter vector autoregressive models (TVP-VARs), and structural break models. They found that models with heteroscedastic errors performed better than models with homoscedastic errors.…”

Analysis and Forecasting of Consumer Price Index (CPI) in Kenya and South Africa using Holt Winter Model

“…Franz et al [17] compared time-varying parameter VAR, factor-augmented VAR and structural break models to analyze core inflation in South Africa using quarterly data from 1981Q1 to 2013Q4. The study showed time varying parameter model having consistence performance over constant coefficient model.…”