for Version 7
Richard Startz University of Washington
EViews Illustrated for Version 7
Copyright © 2007, 2009 Quantitative Micro Software, LLC All Rights Reserved Printed in the United States of America
The author and Quantitative Micro Software assume no responsibility for any errors that may appear in this book or the EViews program. The user assumes all responsibility for the selection of the program to achieve intended results, and for the installation, use, and results obtained from the program.
Windows, Word and Excel are trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corporation. Professional Organization of English Majors is a trademark of Garrison Keillor. All other product names mentioned in this manual may be trademarks or registered trademarks of their respective companies.
Quantitative Micro Software, LLC 4521 Campus Drive, #336, Irvine CA, 92612-2699 Telephone: (949) 856-3368 Fax: (949) 856-2044 web: www.eviews.com
First edition: 2007 Second edition: 2009 Editor: Meredith Startz Index: Palmer Publishing Services
Chapter 3. Getting the Most from Least Squares
Regression is the king of econometric tools. Regression’s job is to find numerical values for theoretical parameters. In the simplest case this means telling us the slope and intercept of a line drawn through two dimensional data. But EViews tells us lots more than just slope and intercept. In this chapter you’ll see how easy it is to get parameter estimates plus a large variety of auxiliary statistics. We begin our exploration of EViews’ regression tool with a quick look back at the NYSE volume data that we first saw in the opening chapter. Then we’ll talk about how to instruct EViews to estimate a regression and how to read the information about each estimated coefficient from the EViews output. In addition to regression coefficients, EViews provides a great deal of summary information about each estimated equation. We’ll walk through these items as well. We take a look at EViews’ features for testing hypotheses about regression coefficients and conclude with a quick look at some of EViews’ most important views of regression results. Regression is a big subject. This chapter focuses on EViews’ most important regression features. We postpone until later chapters various issues, including forecasting (Chapter 8, “Forecasting”), serial correlation (Chapter 13, “Serial Correlation—Friend or Foe?”), and heteroskedasticity and nonlinear regression (Chapter 14, “A Taste of Advanced Estimation”).
A First Regression
Returning to our earlier examination of trend growth in the volume of stock trades, we start with a scatter diagram of the logarithm of volume plotted against time. EViews has drawn a straight line—a regression line—through the cloud of points plotted with log ( volume ) on the vertical axis and time on the horizontal. The regression line can be written as an algebraic expression:
log ( volume t ) = a + bt
Using EViews to estimate a regression lets us replace a and b with numbers
62—Chapter 3. Getting the Most from Least Squares
based on the data in the workfile. In a bit we’ll see that EViews estimates the regression line to be:
log ( volume t ) = – 2.629649 + 0.017278t
In other words, the intercept a is estimated to be -2.6 and the slope b is estimated to be 0.017. Most data points in the scatter plot fall either above or below the regression line. For example, for observation 231 (which happens to be the first quarter of 1938) the actual trading volume was far below the predicted regression line. In other words, the regression line contains errors which aren’t accounted for in the estimated equation. It’s standard to write a regression model to include a term u t to account for these errors. (Econometrics texts sometimes use the Greek letter epsilon, e , rather than u for the error term.) A complete equation...
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