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ARFIMA: Daļēji integrēts ARMA modelis×Panelu vektorautoregresijas (Panel VAR) modelis×Kvantīļu regresija×
NozareEkonometrijaEkonometrijaEkonometrija
SaimeRegression modelRegression modelRegression model
Izcelsmes gads198019881978
AutorsGranger & Joyeux (1980); Hosking (1981)Holtz-Eakin, Newey & RosenKoenker & Bassett
TipsLong-memory time series modelPanel vector autoregressionConditional quantile regression
PirmavotsGranger, 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 ↗Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Citi nosaukumifractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing modelPVAR, panel vector autoregression, Panel VAR (PVAR)conditional quantile regression, regression quantiles, Kantil Regresyon
Saistītās535
KopsavilkumsARFIMA 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.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.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateSalīdzināt metodes: ARFIMA Model · Panel VAR · Quantile Regression. Izgūts 2026-06-18 no https://scholargate.app/lv/compare