Sammenlign metoder

	Vektor feilkorreksjonsmodell (VECM)	ARDL grensetest (Pesaran grensetest)	ARIMA (Autoregressive Integrated Moving Average) Modell
Fagfelt	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1987	2001	2015
Opphavsperson	Engle & Granger	Pesaran, Shin & Smith	Box & Jenkins (Box-Jenkins methodology)
Type	Multivariate time-series model	Cointegration test / Autoregressive distributed lag model	Univariate time-series model
Opprinnelig kilde	Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.	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.	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
Alias	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
Relaterte	4	4	5
Sammendrag	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).