Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель коррекции ошибок вектора (VECM)× | Тест границ ARDL (Pesaran Bounds Test)× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|---|
| Область | Эконометрика | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model | Regression model |
| Год появления≠ | 1987 | 2001 | 2019 |
| Автор метода≠ | Engle & Granger | Pesaran, Shin & Smith | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Multivariate time-series model | Cointegration test / Autoregressive distributed lag model | Linear regression |
| Основополагающий источник≠ | Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗ | Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli) | Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Связанные≠ | 4 | 4 | 5 |
| Сводка≠ | The Vector Error Correction Model is a multivariate time-series model for cointegrated series that captures both their short-run dynamics and their long-run equilibrium relationship. It was introduced by Engle and Granger in 1987 as part of the cointegration and error-correction framework. | The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations. | 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). |
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