The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed for Osun State monthly rainfall data and the analysis was based on probability time series modeling approach. The Plot of the original data shows that the time series is stationary and the Augmented Dickey-Fuller...

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...

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value-weighted US decile portfolios sorted by market size, book-to-market, and...

As part of the Eurosystem’s annual banknote production planning, the national central banks draw up forecasts estimating the volumes of national-issued banknotes in circulation for the three years ahead. As at the end of 2021, more than 80 per cent of euro banknotes in circulation (cumulated...

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...

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)....

This paper studies the self-weighted least squares estimator (SWLSE) of the ARMA model with GARCH noises. It is shown that the SWLSE is consistent and asymptotically normal when the GARCH noise does not have a finite fourth moment. Using the residuals from the estimated ARMA model, it is shown...

We develop methods to obtain optimal forecast under long memory in the presence of a discrete structural break based on different weighting schemes for the observations. We observe significant changes in the forecasts when long-range dependence is taken into account. Using Monte Carlo...

This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to...

We study the out-of-sample predictability of the real price of crude oil using forecast combinations constructed from several individual predictors. We find that forecasts of themonthly average price of oil are more accurate than the no-change forecast at horizons ranging from 1 to 24 months...

The paper presents a Bayes analysis of an autoregressive-moving average model and its components based on exact likelihood and weak priors for the parameters where the priors are defined so that they incorporate stationarity and invertibility restrictions naturally. A Gibbs-Metropolis hybrid...

The purpose of this paper is to compare the accuracy of the three types of models: Autoregressive Integrated Moving Average (ARIMA) models, Holt-Winters models and Neural Network Auto-Regressive (NNAR) models in forcasting the Harmonized Index of Consumer Prices (HICP) for the countries of...

The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which...

An approach to the modelling of volatile time series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the stationary distribution of the time series and quantiles of the distribution of a...

This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent phenomenon is...

This study examined econometric modelling and forecasting foreign direct investment inflows in Nigeria over the next decade using Box-Jenkins ARIMA model approach. The scope of the study is from 1970 to 2020. The correlogram show that the net foreign direct investment inflow in Nigeria is...

Smart home is an important application of Internet of Things (IoT) that utilizes the Internet to monitor and control appliances in a home automation system by providing security, energy efficiency, low operating costs, convenience etc. The installation of smart products allows the user to...

The first official data releases of quarterly real GDP for the euro area are published about eight weeks after the end of the reference quarters. Meanwhile, ongoing economic developments must be assessed from various, more readily available, monthly indicators. We examine in the context of...

The purpose of this research paper is to understand the MCX Comdex index, the paper also attempts to forecast the index level, the level of index reflects the change in commodity prices. The index is based on commodity futures prices of an exchange. To study the same daily closing prices of...

The most important assumption about time series and econometrics data is stationarity. Therefore, this study focuses on behaviors of some parameters in stationarity of autoregressive (AR) and moving average (MA) models. Simulation studies were conducted using R statistical software to...

In Afghanistan, the novel coronavirus disease 2019 (COVID-19) is spreading rapidly. Currently, we are in third wave of pandemic, in Afghanistan. And recently government of Afghanistan recorded the highest confirmed cases since the start of pandemic. In order to prepare ourselves and make right...

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 2008...

The electricity demand has been steadily increasing throughout the years. A robust predictive model is required to prepare for future electricity consumption. This paper applied the ARIMA models to forecast electricity consumption in the Philippines. Dataset used was retrieved from the...

This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and...

A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect Inference, in the context of Autoregressive Moving Average (ARMA) processes for finite samples. Resulting objective functions are treated as test statistics, which are inverted...