手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 最小二乗法 (OLS) 回帰× | Theta法× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2019 | 2000 |
| 提唱者≠ | Wooldridge (textbook treatment); classical least squares | Assimakopoulos & Nikolopoulos |
| 種類≠ | Linear regression | Univariate time-series forecasting model |
| 原典≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Assimakopoulos, V. & Nikolopoulos, K. (2000). The Theta Model: A Decomposition Approach to Forecasting. International Journal of Forecasting, 16(4), 521-530. DOI ↗ |
| 別名≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | theta model, theta forecasting, Theta Yöntemi — M3 Tahmin Yarışması Birincisi |
| 関連≠ | 5 | 4 |
| 概要≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | The Theta Method is a univariate time-series forecasting model introduced by Assimakopoulos and Nikolopoulos in 2000. It decomposes a series into two theta lines that capture its long-run trend and its short-run dynamics, forecasts each line separately, and combines them by a weighted average. Its simplicity and accuracy made it the winner of the M3 forecasting competition. |
| ScholarGateデータセット ↗ |
|
|