Salīdzināt metodes

	Modelis NL-SVAR (Nonlinear Structural Vector Autoregression)	Nelineārais ARDL (NARDL) modelis
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2010s	2014
Autors	Extensions by Koop, Potter, Auerbach, Gorodnichenko and others	Shin, Yu & Greenwood-Nimmo
Tips	Multivariate nonlinear structural time series model	Nonlinear cointegration model
Pirmavots	Koop, G., & Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4), 267–358.	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.
Citi nosaukumi	nonlinear structural VAR, NL-SVAR, threshold SVAR, regime-switching SVAR	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Saistītās	6	5
Kopsavilkums	The Nonlinear Structural VAR model extends the standard SVAR framework to allow structural relationships and dynamic responses to vary across economic regimes or states of the world. By imposing nonlinear transition mechanisms — such as threshold switching or smooth regime change — it captures asymmetric responses to shocks that a linear SVAR cannot detect.	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.