Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| ARFIMA: Modelo Autoregressivo de Média Móvel Fracionariamente Integrado× | Regressão por Mínimos Quadrados Ordinários (MQO)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1980 | 2019 |
| Autor original≠ | Granger & Joyeux (1980); Hosking (1981) | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Long-memory time series model | Linear regression |
| Fonte seminal≠ | 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 |
| Outros nomes≠ | 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 |
| Relacionados | 5 | 5 |
| Resumo≠ | 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). |
| ScholarGateConjunto de dados ↗ |
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