مقایسهٔ روشها
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| مدل تصحیح خطای برداری (VECM)× | آزمون حدود ARDL (آزمون حدود پزاران)× | مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)× | |
|---|---|---|---|
| حوزه | اقتصادسنجی | اقتصادسنجی | اقتصادسنجی |
| خانواده | Regression model | Regression model | Regression model |
| سال پیدایش≠ | 1987 | 2001 | 2015 |
| پدیدآور≠ | Engle & Granger | Pesaran, Shin & Smith | Box & Jenkins (Box-Jenkins methodology) |
| نوع≠ | Multivariate time-series model | Cointegration test / Autoregressive distributed lag model | Univariate time-series model |
| منبع بنیادین≠ | 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 ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| نامهای دیگر≠ | 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) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| مرتبط≠ | 4 | 4 | 5 |
| خلاصه≠ | 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateمجموعهداده ↗ |
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