Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Nelineārs VAR modelis× | Vektora kļūdu labojuma modelis (VECM)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990s–2000s | 1987 |
| Autors≠ | Tsay (1998); Krolzig (1997); Tong (1990) for threshold framework | Robert F. Engle and Clive W. J. Granger |
| Tips≠ | Multivariate nonlinear time series model | Multivariate time-series model |
| Pirmavots≠ | Tsay, R. S. (1998). Testing and modeling multivariate threshold models. Journal of the American Statistical Association, 93(443), 1188–1202. DOI ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| Citi nosaukumi | NLVAR, nonlinear vector autoregression, threshold VAR, TVAR | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | The Nonlinear VAR (NLVAR) model extends the standard vector autoregression by allowing the dynamic relationships among multiple time series to switch or change smoothly depending on an observed threshold variable, a latent regime state, or a smooth transition function. It is used when economic systems exhibit asymmetric responses, regime shifts, or state-dependent dynamics that a linear VAR cannot capture. | The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. |
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