Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/46245
Title: | Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms | Authors: | Ozgur, Onder Akkoc, Ugur |
Keywords: | Forecasting Emerging economies Inflation Prophet model Shrinkage methods Phillips-Curve Models Expectations Regression Market |
Publisher: | Emerald Group Publishing Ltd | 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. | URI: | https://doi.org/10.1108/IJOEM-05-2020-0577 https://hdl.handle.net/11499/46245 |
ISSN: | 1746-8809 1746-8817 |
Appears in Collections: | İktisadi ve İdari Bilimler Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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