Sammenlign metoder

	ARCH-LM-testen for volatilitetsklustering	Minste kvadraters metode (OLS)
Fagfelt	Økonometri	Økonometri
Familie	Regression model	Regression model
Opprinnelsesår	1982	2019
Opphavsperson	Robert F. Engle	Wooldridge (textbook treatment); classical least squares
Type	Lagrange multiplier diagnostic test for conditional heteroscedasticity	Linear regression
Opprinnelig kilde	Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	ARCH-LM Testi ve Volatilite Kümelenmesi Analizi, ARCH LM test, Engle's ARCH test, test for autoregressive conditional heteroscedasticity	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterte	6	5
Sammendrag	The ARCH-LM test is Robert Engle's (1982) Lagrange multiplier diagnostic for autoregressive conditional heteroscedasticity in the residuals of a fitted time-series model. It checks whether the error variance changes over time and clusters into calm and turbulent periods, and it is the standard pre-test run before fitting a GARCH-family volatility model.	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).