![autoregressive distributed lag eviews autoregressive distributed lag eviews](https://i.ytimg.com/vi/xl5weqwX6pk/mqdefault.jpg)
Sized VARs require estimation of a large number of parameters. The basic $k$-variable VAR(p) specification has $k(pk+d)$ coefficients so that even moderate \hfill \textX$, $\delta_t$ and $\gamma_t$ are discount factors, and $G$ is the number of clusters. Specifically, EViews supports the following estimators and weight choices: The estimators differ in their choice of observation-specific weights used to improve the finite sample properties of the residual error covariance. The class of estimators supported belong to the HC family described by Long and Ervin, 2000, and Cribari-Neto and da Silva, 2011. Heteroskedastic Consistent (HC) Covariance EstimatorsĮViews 10 increases the options for heteroskedastic consistent covariance estimators beyond the familiar White estimator available in previous versions. Specification of both regieme varying and regieme non-varying regressors.ĮViews has included both White and Heteroskedasticity and Autocorrelation Consistent Covariance (HAC) estimators of the least-squares covariance matrix for over twenty years.ĮViews 10 expands upon these robust standard error options with the addition of a family of heteroskedastic consistent covariance, and clustered standard errors.Model selection for the threshold variable.Estimation of parameters for both shape and location of the smooth threshold.As a result, STR models are often considered to have more “realistic” dynamics that their discrete TR model counterparts.ĮViews' implementation of STR includes features such as: In STR models the regime switching that occurs when an observed variable crosses unknown thresholds happens smoothly. EViews 10 New Econometrics and Statistics: Estimation Smooth Threshold Regression (STR and STAR)ĮViews 9 introduced Threshold Regression (TR) and Threshold Autoregression (TAR) models, and EViews 10 expands up these model by adding Smooth Threshold Regression and Smooth Threshold Autoregression as options.