Сравнение на методи
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| Модел ARIMA (Autoregressive Integrated Moving Average)× | Метод на най-малките квадрати (МНК)× | Векторен модел за корекция на грешката (VECM)× | |
|---|---|---|---|
| Област | Иконометрия | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2015 | 2019 | 1987 |
| Създател≠ | Box & Jenkins (Box-Jenkins methodology) | Wooldridge (textbook treatment); classical least squares | Engle & Granger |
| Тип≠ | Univariate time-series model | Linear regression | Multivariate time-series model |
| Основополагащ източник≠ | 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 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. DOI ↗ |
| Други названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | vector error correction model, error correction model, cointegration model, VECM (Vektör Hata Düzeltme Modeli) |
| Свързани≠ | 5 | 5 | 4 |
| Резюме≠ | 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). | 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 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. |
| ScholarGateНабор от данни ↗ |
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