User’s Guide : Advanced Single Equation Analysis
  
Advanced Single Equation Analysis
The following sections describe EViews tools for the estimation and analysis of advanced single equation models and time series analysis:
• “ARCH and GARCH Estimation”, outlines the EViews tools for ARCH and GARCH modeling of the conditional variance, or volatility, of a variable.
• “Cointegrating Regression” describes EViews’ tools for estimating and testing single equation cointegrating relationships. Multiple equation tests for cointegration are described in “Vector Autoregression (VAR) Models”.
• “ARDL and Quantile ARDL” describes the specification and estimation of Autoregressive Distributed Lag (ARDL) models.
• “Midas Regression” documents EViews tools for Mixed Data Sampling (MIDAS) regression, an estimation technique which allows for data sampled at different frequencies to be used in the same regression.
• “Discrete and Limited Dependent Variable Models” documents EViews tools for estimating qualitative and limited dependent variable models. EViews provides estimation routines for binary or ordered (probit, logit, gompit), censored or truncated (tobit, etc.), Heckman selection models, and integer valued (count data).
• “Generalized Linear Models” documents describes EViews tools for the class of Generalized Linear Models.
• “Robust Least Squares” describes tools for robust least squares estimation which are designed to be robust, or less sensitive, to outliers.
• “Least Squares with Breakpoints” outlines the EViews estimator for equations with one or more structural breaks.
• “Discrete Threshold Regression” describes the analysis of discrete threshold regressions and autoregressions.
• “Smooth Transition Regression” describes the analysis of smooth threshold regressions and autoregressions.
• “Elastic Net and Lasso” describes the estimation of elastic net and LASSO regularization models.
• “Functional Coefficient Regression” describes tools for estimating semi-parametric functional coefficients models in EViews.
• “Switching Regression” describes estimation of regression models with nonlinearities arising from discrete changes in unobserved regimes.
• “Quantile Regression” describes the estimation of quantile regression and least absolute deviations estimation in EViews.
• “The Log Likelihood (LogL) Object” describes techniques for using EViews to estimate the parameters of maximum likelihood models where you may specify the form of the likelihood.
• “Univariate Time Series Analysis” describes tools for univariate time series analysis, including unit root tests in both conventional and panel data settings, variance ratio tests, and the BDS test for independence.