Patrick T. Kanda

Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation

ABSTRACT The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-of-sample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT’s performance in forecasting South African inflation.

DSGE Model-Based Forecasting of Modeled and Non-Modeled Inflation Variables in South Africa

Inflation forecasts are a key ingredient for monetary policymaking - especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, e.g. such as alternative measures of inflation that might be of interest to policymakers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4, and generate recursive forecasts over 2000Q1-2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and non-modeled) in comparison with forecasts reported by other models such as AR(1).

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.