방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 푸리에 ARMA 모형× | 비선형 ARMA 모형(NARMA)× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2004–2006 | 1980s–1990s |
| 창시자≠ | Becker, Enders, and Hurn | Tong (1990); Granger & Terasvirta (1993) |
| 유형≠ | Time series model with smooth structural change | Nonlinear time series model |
| 원전≠ | Becker, R., Enders, W., & Hurn, S. (2006). A general test for time dependence in parameters. Journal of Applied Econometrics, 21(7), 1005–1028. link ↗ | Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198522300 |
| 별칭 | Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA | NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average |
| 관련≠ | 5 | 2 |
| 요약≠ | The Fourier ARMA model augments the classical Autoregressive Moving Average framework with low-frequency Fourier (sine and cosine) terms to capture smooth, gradual shifts in the mean or trend of a time series. Unlike dummy-variable approaches, it requires no prior knowledge of when structural change occurred, approximating change with flexible trigonometric functions. | The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial time series. |
| ScholarGate데이터셋 ↗ |
|
|