Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| ARFIMA: Muundo wa Mfumo wa ARMA wenye Viwango vya Nusu× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | Uchanganuzi wa Vector Autoregression wa Paneli (Panel VAR)× | |
|---|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model | Regression model |
| Mwaka wa asili≠ | 1980 | 2019 | 1988 |
| Mwanzilishi≠ | Granger & Joyeux (1980); Hosking (1981) | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen |
| Aina≠ | Long-memory time series model | Linear regression | Panel vector autoregression |
| Chanzo asilia≠ | Granger, 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: 978-1337558860 | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ |
| Majina mbadala≠ | fractionally integrated ARMA, long-memory time series model, ARFIMA / FIGARCH, fractional differencing model | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) |
| Zinazohusiana≠ | 5 | 5 | 3 |
| Muhtasari≠ | ARFIMA 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. |
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