Purpose - The purpose of this research is to develop a predictive model that can estimate the volume of remittances channeled toward Yemen's economic reconstruction efforts. Design/methodology/approach - This study utilized a time-series dataset encompassing remittance inflows into Yemen's...

The COVID-19 outbreak in 2020 has fostered in many countries the development of new weekly economic indices for the timely tracking of pandemic-related turmoils and other forms of rapid economic changes. Such indices often utilise information from daily and weekly economic time series that...

This paper introduces a new predictive analytics model for forecasting stock price trends in financial assets traded on major stock exchanges worldwide and the Colombian Stock Exchange. The model is built on a probability space definition that consists of a measurable space derived from...

This note puts forward a new modelling approach that includes both fractional integration and autoregressive processes in a unified framework. The proposed model is very general and includes other more standard approaches such as the AR(F)IMA models. Some Monte Carlo evidence shows that the...

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We...

This paper explores the evolution of non-working time in Spain over recent years by analysing the results of two surveys conducted by the National Statistics Institute: the Quarterly Survey on Labor Costs and the Labor Force Survey. Using time series models and intervention analysis, potential...

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their...

The objective of this study is to predict unemployment in Indonesia in the wake of the demographic dividend. The sample used in this study is the unemployment data from 1990 to 2022 from the Indonesian Central Bureau of Statistics database. Using non-seasonal ARIMA (Autoregressive Integrated...

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration framework. The proposed model is based on a specific test on fractional integration that is more general than the standard methods, which allow for only...

The equivalence of the Beveridge-Nelson decomposition and the trend-cycle decomposition is well established. In this paper we argue that this equivalence is almost immediate when a Gaussian score-driven location model is considered. We also provide a natural extension towards heavy-tailed...

The current study aims to model the South African crude oil prices using the hybrid of Box-Jenkins autoregressive integrated moving average (ARIMA) and Neural Networks (NNs). This study introduces a hybrid approach to forecasting methods aimed at resolving the issues of lack of precision in...

China's economy is very interesting to analyze because it is recognized as the highest GDP in  the world. Despite the ability of China's economy to reform and grow, China shows fluctuation  in its economy especially after the crisis in 1997 and 2008. When China was able to counter  the...

Accurate forecasting of Gross Domestic Product (GDP) is crucial for policymakers, businesses, and investors. This research explores the use of SARIMAX, ARIMA, and Random Forest models to forecast GDP in the UK. The study investigates the relationship between GDP and the unemployment rate,...

This paper introduces a new modelling approach that incorporates nonlinear, exponential deterministic terms into a fractional integration model. The proposed model is based on a specific version of Robinson's (1994) tests and is more general that standard time series models, which only allow for...

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an...

In this paper, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminished by introducing a further fractional integration parameter to spatial autoregressive moving average models (SARMA)....

The proper forecasting of listed companies' earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of...

Sustainability is a key topic nowadays, mostly because in the last decade the pollution levels have reached an all-time high. National governments are searching for sustainable and environmentally friendly solutions to decrease the amount of pollution. This study is a cross-sectional study on 27...

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...

The article analyses the level of digitalization in the European Union (EU) and correlates the results with the Sustainable Development Goals formulated by the United Nations. The paper provides figures on the number of enterprises receiving orders online, the share of enterprises' turnover on...