Basic Single Equation Analysis

The following sections describe the EViews features for basic single equation and single series analysis.

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“Basic Regression Analysis” outlines the basics of ordinary least squares estimation in EViews.

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“Additional Regression Tools” discusses special equation terms such as PDLs and automatically generated dummy variables, robust standard errors, weighted least squares, nonlinear least square, and the analysis of outliers and breaks indicator selection techniques.

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“Regression Variable Selection” discusses tools that may be used to automatically determine the variables used as regressors in a least squares regression.

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“Instrumental Variables and GMM” describes estimation of single equation Two-stage Least Squares (TSLS), Limited Information Maximum Likelihood (LIML) and K-Class Estimation, and Generalized Method of Moments (GMM) models.

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“Time Series Regression” describes a number of basic tools for analyzing and working with time series regression models: testing for serial correlation, estimation of ARMAX and ARIMAX models, and diagnostics for equations estimated using ARMA terms.

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“Forecasting from an Equation” outlines the fundamentals of using EViews to forecast from estimated equations.

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“Specification and Diagnostic Tests” describes specification testing in EViews.

The sections describing advanced single equation techniques for autoregressive conditional heteroskedasticity, and discrete and limited dependent variable models are listed in
Part VI. “Advanced Single Equation Analysis”.

Multiple equation estimation is described in the chapters listed in
Part VIII. “Multiple Equation Analysis”.

Part IX. “Panel and Pooled Data” describes estimation in pooled data settings and panel structured workfiles.