The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that...

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are...

This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at...

Recent work by Medeiros et al. (2019, Journal of Business & Economic Statistics) shows that point forecasts of the random forest machine learning algorithm systematically outperform well-established benchmarks at predicting U.S. inflation. This article extends their work from point to density...

We present a first assessment of the predictive ability of machine learning methods for inflation forecasting in Costa Rica. We compute forecasts using two variants of k-nearest neighbors, random forests, extreme gradient boosting and a long short-term memory (LSTM) network. We evaluate their...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

This study examines the effect of bank-specific and macroeconomic key determinants of Islamic retail banks profitability in Bahrain. It used panel data of six Islamic retail banks from 2013 to 2019, and it employed an explanatory research with secondary financial data. Return on Assets (ROA) and...

A novel Bayesian method for inference in dynamic regression models is proposed where both the values of the regression coefficients and the importance of the variables are allowed to change over time. We focus on forecasting and so the parsimony of the model is important for good performance. A...

Inflation is defined as an increase in the general price level of goods and services within a period of time. For any economic agent to formulate policy, it must taken into consideration inflation and the aim of this study is to use ARIMA model to predict inflation in Ghana. In order to fulfill...