Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis ar nelineāru slīdošo vidējo (NMV)× | ARMA modelis (Autoregresīvs vidējais aritmētiskais)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1978 | 1970 |
| Autors≠ | Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory) | George E. P. Box and Gwilym M. Jenkins |
| Tips≠ | Nonlinear time series model | Time series model |
| Pirmavots≠ | Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen. link ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Citi nosaukumi | NMA model, nonlinear moving average, NLMA model, nonlinear MA | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
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