विधियों की तुलना करें

	अरैखिक चल माध्य (NMA) मॉडल	गार्छ मॉडल (अस्थिरता पूर्वानुमान)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1978	1986
प्रवर्तक	Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory)	Tim Bollerslev
प्रकार	Nonlinear time series model	Conditional volatility model
मौलिक स्रोत	Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
उपनाम	NMA model, nonlinear moving average, NLMA model, nonlinear MA	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
संबंधित	4	5
सारांश	The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.