Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Vektor fejlkorrektionsmodel (VECM)× | ARDL-grænsetesten (Pesaran Bounds Test)× | Almindelig mindste kvadraters metode (OLS) regression× | |
|---|---|---|---|
| Fagområde | Økonometri | Økonometri | Økonometri |
| Familie | Regression model | Regression model | Regression model |
| Oprindelsesår≠ | 1987 | 2001 | 2019 |
| Ophavsperson≠ | Engle & Granger | Pesaran, Shin & Smith | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Multivariate time-series model | Cointegration test / Autoregressive distributed lag model | Linear regression |
| Oprindelig kilde≠ | 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 |
| Aliasser | 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 |
| Relaterede≠ | 4 | 4 | 5 |
| Resumé≠ | 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). |
| ScholarGateDatasæt ↗ |
|
|
|