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Використання мови R для ідентифікації параметрів ARFIMA-моделі [PDF]
, 2016 This work is devoted to the identification of the parameters of ARFIMA(p,d,q) time series model. The algorithm of the parameters estimation using R programming language functions from fracdiff and forecast packages is discussed in ...
Рабик, Василь Григорович +2 more
core +6 more sources
A Forecasting Model for Japan's Unemployment Rate [PDF]
Eurasian Journal of Business and Economics, 2010 This note aims to achieve a parsimonious fractionally-integrated autoregressive and moving average (ARFIMA) model for recent time series data of Japan's unemployment rate. A brief review of the ARFIMA model is provided, leading to econometric modeling of
Takamitsu KURITA
doaj
Inflation Dynamics in the ABC (Argentina, Brazil and Chile) countries
Ensayos de Política Económica, 2020 Este trabajo evalúa la inercia y persistencia de la inflación y sus componentes para Argentina (dos períodos), Brasil y Chile utilizando modelos estacionales y fraccionalmente integrados autorregresivo de promedios móviles (modelo S-ARFIMA).
Fernando Zarzosa Valdivia
doaj +1 more source
Evaluating the Effectiveness of GARCH Models in the Estimation of Systematic Risk in listed companies of the Tehran Stock Exchange [PDF]
Journal of Asset Management and Financing, 2020 The stock market of each country, in addition to reflecting its economic structure, is considered as an important source of capital Circulation of that country.
nemat rastgoo, Hossein panahian
doaj +1 more source
Kubik, 2015 Model ARFIMA (Autoregressive Fractionally Integrated Moving Average) merupakan pengembangan dari model ARIMA yang pertama kali dikenalkan oleh Granger dan Joyeux (1980). Sedangkan Hosking (1981) memperkenalkan sifat jangka panjang (long memory) pada data
Rini Cahyandari, Rima Erviana
doaj +1 more source
SMALL-SAMPLE LIKELIHOOD-BASED INFERENCE IN THE ARFIMA MODEL [PDF]
Econometric Theory, 2000 The autoregressive fractionally integrated moving average (ARFIMA) model has become a popular approach for analyzing time series that exhibit long-range dependence. For the Gaussian case, there have been substantial advances in the area of likelihood-based inference, including development of the asymptotic properties of the maximum likelihood ...
Offer Lieberman +2 more
openaire +4 more sources
Journal of Hydrology and Hydromechanics, 2014 Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting.
Szolgayová Elena +3 more
doaj +1 more source
Evaluation of Dual Long Memory Properties with Emphasizing the Skewed and Fat-Tail Distribution: Evidence from Tehran Stock Exchange [PDF]
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī, 2014 This paper investigates the presence of long memory in the Tehran stock market, using the ARFIMA, GPH, GSP and FIGARCH models. The data set consists of daily returns, and long memory tests are carried out both for the returns and volatilities of TEPIX ...
Mohammad Javad Mohagheghnia +3 more
doaj
InPrime, 2019 Wind speed is one of the most important weather factors in the landing and takeoff process of airplane because it can affect the airplane's lift. Therefore, we need a model to predict the wind speed in an area.
Devi Ila Octaviyani +2 more
doaj +1 more source
Análisis de contagios SARS COV-2 por medio de herramientas de series de tiempo
Dictamen LibreEl presente artículo propone analizar y desarrollar herramientas de series de tiempo con las cuales se pueda estimar la curva de contagios de SARS-COV2 en países de Latinoamérica.
David Santiago Álzate Vélez +3 more
doaj +1 more source