Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms
International Journal of Emerging Markets
ISSN: 1746-8809
Article publication date: 3 February 2021
Issue publication date: 24 November 2022
Abstract
Purpose
The main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.
Design/methodology/approach
This paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.
Findings
Results suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.
Originality/value
Turkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.
Keywords
Citation
Özgür, Ö. and Akkoç, U. (2022), "Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms", International Journal of Emerging Markets, Vol. 17 No. 8, pp. 1889-1908. https://doi.org/10.1108/IJOEM-05-2020-0577
Publisher
:Emerald Publishing Limited
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