ScholarGate
Асистент

Порівняння методів

Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.

ARFIMA: Модель дробово інтегрованої ARMA×Регресія звичайно найменших квадратів (ЗНК)×Панельна векторна авторегресія (Panel VAR)×
ГалузьЕконометрикаЕконометрикаЕконометрика
РодинаRegression modelRegression modelRegression model
Рік появи198020191988
Автор методуGranger & Joyeux (1980); Hosking (1981)Wooldridge (textbook treatment); classical least squaresHoltz-Eakin, Newey & Rosen
ТипLong-memory time series modelLinear regressionPanel vector autoregression
Основоположне джерелоGranger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗
Інші назвиfractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing modelordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonuPVAR, panel vector autoregression, Panel VAR (PVAR)
Пов'язані553
ПідсумокARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level.
ScholarGateНабір даних
  1. v1
  2. 2 Джерела
  3. PUBLISHED
  1. v1
  2. 1 Джерела
  3. PUBLISHED
  1. v1
  2. 2 Джерела
  3. PUBLISHED

Перейти до пошуку Завантажити слайди

ScholarGateПорівняння методів: ARFIMA Model · OLS Regression · Panel VAR. Отримано 2026-06-18 з https://scholargate.app/uk/compare