5/17/2023 0 Comments Caret timeslice step size![]() ![]() Results suggest that shrinkage methods perform better for variable selection. 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. 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. Also, recommended is the need for the monetary authorities to focus more on ways to improve food production by improving security. The study recommends that ridge regression and Artificial Neural Network machine learning techniques be used in forecasting the inflation rate in Nigeria. ![]() The study further reveals that the major drivers of headline inflation in Nigeria were food inflation, core inflation, prime lending rate, maximum lending rate, and the inter-bank rate. The study found that ridge regression and Artificial Neural Networks are the best in forecasting inflation in Nigeria when compared with the LASSO, elastic net, and PLS. Data was sourced from CBN statistical bulletin (2021) on monthly basis. Different traditional methods were used to forecast inflation with little or no attention given to the area of forecasting the inflation rate in Nigeria using machine learning techniques. Inflation forecasting is key in achieving the Central Bank mandate of price stability the world over. So, this is the first study in the field, which uses both linear and nonlinear ML methods to make a comparison with the time series inflation forecasts for Turkey. Originality: We evaluate the forecasting performance of ML models against each other and a time series model, and investigate possible improvements upon the naive model. Implication: The findings are expected to be useful as a guide for central banks and policy-makers in emerging economies with volatile inflation rates. It is also predicted that it can be considered as an alternative method as the amount of data and computational power increase. In this direction, non-linear machine learning models are thought to be a reliable complementary method for estimating inflation in emerging economies. Findings: According to our findings, although the linear-based Ridge and Lasso regression algorithms perform worse than the VAR model, the multilayer perceptron algorithm gives satisfactory results that are close to the results of the time series algorithm. The data is spanning from the period 2006:M1 to 2020:M12. Methods: This paper forecasts inflation in Turkey by using time-series and machine learning (ML) models. ![]() Purpose: This paper aims to test the accuracy of some Machine Learning (ML) models in forecasting inflation in the case of Turkey and to give a new and also complementary approach to time series models. ![]()
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