Methoden vergelijken

	ARDL Bounds Test	Dynamische Kleinste-Kwadraten-Schatting (DOLS)	Gewone Kleinste Kwadraten (GKK) Regressie
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	2001	1993	2019
Grondlegger	Pesaran, Shin & Smith	Stock & Watson (1993); panel extension Kao & Chiang (2001)	Wooldridge (textbook treatment); classical least squares
Type	Cointegration test / Autoregressive distributed lag model	Cointegrating regression estimator	Linear regression
Oorspronkelijke bron	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.	Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliassen	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Verwant	4	5	5
Samenvatting	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.	Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares.	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).