Part IV. Basic Single Equation Analysis

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

•Chapter 18. “Basic Regression Analysis,” beginning on page 5 outlines the basics of ordinary least squares estimation in EViews.

•Chapter 19. “Additional Regression Tools,” on page 23 discusses special equation terms such as PDLs and automatically generated dummy variables, robust standard errors, weighted least squares, and nonlinear least square estimation techniques.

•Chapter 20. “Instrumental Variables and GMM,” on page 55 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.

•Chapter 21. “Time Series Regression,” on page 85 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.

•Chapter 22. “Forecasting from an Equation,” beginning on page 111 outlines the fundamentals of using EViews to forecast from estimated equations.

•Chapter 23. “Specification and Diagnostic Tests,” beginning on page 139 describes specification testing in EViews.

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

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

Part VIII. “Panel and Pooled Data” on page 563 describes estimation in pooled data settings and panel structured workfiles.

4—Part IV. Basic Single Equation Analysis