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| Μοντέλο AR Fourier× | Μοντέλο ARMA (Αυτοπαλινδρομικής Κινητού Μέσου)× | |
|---|---|---|
| Πεδίο | Οικονομετρία | Οικονομετρία |
| Οικογένεια | Regression model | Regression model |
| Έτος προέλευσης≠ | 2012 | 1970 |
| Δημιουργός≠ | Enders & Lee | George E. P. Box and Gwilym M. Jenkins |
| Τύπος≠ | Time series model with Fourier augmentation | Time series model |
| Θεμελιώδης πηγή≠ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Εναλλακτικές ονομασίες | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Συναφείς≠ | 6 | 5 |
| Σύνοψη≠ | The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly. | 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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