Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Njia ya Muda-Tofauti ya Vigezo (TVP-WLS)× | Weighted Least Squares (WLS)× | |
|---|---|---|
| Nyanja≠ | Ekonometriki | Takwimu |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1976–1990 | 1935 |
| Mwanzilishi≠ | Cooley & Prescott (1976); Harvey (1990) | Alexander Craig Aitken |
| Aina≠ | Time-varying coefficient regression with observation weights | Weighted linear estimator |
| Chanzo asilia≠ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737 | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Majina mbadala | TVP-WLS, time-varying coefficient WLS, locally weighted time-varying regression, TVP weighted regression | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Zinazohusiana≠ | 2 | 3 |
| Muhtasari≠ | Time-Varying Parameter WLS is a regression technique for time-series data in which the slope and intercept coefficients are allowed to change over time while observations are weighted to account for heteroscedasticity or to discount distant data. It combines the flexibility of state-space coefficient evolution with the variance-correcting power of weighted least squares. | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
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