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 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.

Big data forecasting of South African inflation

“…Similar results are provided in Gupta and Kabundi (2010), where it is noted that large-scale data-rich models are better suited to forecasting key macroeconomic variables, relative to small-scale models. As an alternative, Kanda et al (2016) is one of the few studies that make use of monthly data to focus on evaluating the performance of a suite of univariate nonlinear models, which include a locally linear model tree, neuro-fuzzy, multilayered perceptron, artificial neural network, nonlinear autoregressive, and genetic algorithm-based forecasting model. 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.…”

Estudo da influência de eventos sobre a estrutura do mercado brasileiro de ações a partir de redes ponderadas por correlações de Pearson, Spearman e Kendall

“…Numa rede de aeroportos, o número de voos que operam entre aeroportos (BAGLER, 2008). No mercado financeiro, a proporção total do mercado de ativos controlados por um banco através de relações societárias (PECORA; SPELTA, 2015), as taxas inflação de um período (KANDA et al, 2016) ou ainda os coeficientes de correlação entre variáveis (MANTEGNA, 1999;BONANNO;LILLO;MANTEGNA, 2001b;BONANNO;LILLO;MANTEGNA, 2001a;VANDEWALLE et al, 2001;ONNELA et al, 2003;KASKI;KERTÉSZ, 2004;BONANNO et al, 2004;JUNG et al, 2006;COELHO et al, 2007;TUMMINELLO et al, 2007a;TUMMINELLO et al, 2007b;ZHUANG;YAO, 2009;TABAK;SERRA;CAJUEIRO, 2010;GAN, 2015;GAN, 2016;DE-VIREN;DEVIREN, 2016;HUANG et al, 2017).…”