Search results

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This paper aims to forecast the availability of used but operational electric vehicle (EV) batteries to integrate them into a circular economy concept of EVs' end-of-life (EOL) phase. Since EVs currently on the roads will become obsolete after 2030, this study focuses on the 2030–2040 period and links future renewable electricity production with the potential for storing it into used EVs' batteries. Even though battery capacity decreases by 80% or less, these batteries will remain operational and can still be seen as a valuable solution for storing peaks of renewable energy production beyond EV EOL.

Storing renewable electricity is gaining as much attention as increasing its production and share. However, storing it in new batteries can be expensive as well as material and energy-intensive; therefore, existing capacities should be considered. The use of battery electric vehicles (BEVs) is among the most exciting concepts on how to achieve it. Since reduced battery capacity decreases car manufacturers' interest in battery reuse and recycling is environmentally hazardous, these batteries should be integrated into the future electricity storage system. Extending the life cycle of batteries from EVs beyond the EV's life cycle is identified as a potential solution for both BEVEOL and electricity storage.

Results revealed a rise of photovoltaic (PV) solar power plants and an increasing number of EVs EOL that will have to be considered. It was forecasted that 6.27–7.22% of electricity from PV systems in scenario A (if EV lifetime is predicted to be 20 years) and 18.82–21.68% of electricity from PV systems in scenario B (if EV lifetime is predicted to be 20 years) could be stored in batteries. Storing electricity in EV batteries beyond EV EOL would significantly decrease the need for raw materials, increase energy system and EV sustainability performance simultaneously and enable leaner and more efficient electricity production and distribution network.

Storing electricity in used batteries would significantly decrease the need for primary materials as well as optimizing lean and efficient electricity production network.

Energy storage is one of the priorities of energy companies but can be expensive as well as material and energy-intensive. The use of BEV is among the most interesting concepts on how to achieve it, but they are considered only when in the use phase as vehicle to grid (V2G) concept. Because reduced battery capacity decreases the interest of car manufacturers to reuse batteries and recycling is environmentally risky, these batteries should be used for storing, especially renewable electricity peaks. Extending the life cycle of batteries beyond the EV's life cycle is identified as a potential solution for both BEV EOL and energy system sustainability, enabling more efficient energy management performance. The idea itself along with forecasting its potential is the main novelty of this paper.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipated maize shortage.

Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test.

The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application.

The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results.

This study focuses on forecasting the price of the most important export crops of vegetables and fruits in Egypt from 2016 to 2030.

The study applied generalized autoregressive conditional heteroskedasticity (GARCH) model and autoregressive integrated moving average (ARIMA) model.

The results show that ARIMA (1,1,1), ARIMA (2.1,2), ARIMA (1,1,0), ARIMA (1,1,2), ARIMA (0,1,0) and ARIMA (1,1,1) are the most appropriate fitted models to evaluate the volatility of price of green beans, tomatoes, onions, oranges, grapes and strawberries, respectively. The results also revealed the presence of ARCH effect only in the case of Potatoes, hence it is suggested that the GARCH approach be used instead. The GARCH (1,1) is found to be a better model in forecasting price of potatoes.

The study of food price volatility in developing countries is essential, since a significant share of household budgets is spent on food in these economies, so forecasting agricultural prices is a substantial requirement for drawing up many economic plans in the fields of agricultural production, consumption, marketing and trade.