EViews_Illustrated_Chapter_3

EViews_Illustrated_Chapter_3 - EViews Illustrated for...

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EViews Illustrated for Version 7 Richard Startz University of Washington
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EViews Illustrated for Version 7 Copyright © 2007, 2009 Quantitative Micro Software, LLC All Rights Reserved Printed in the United States of America ISBN: 978-1-880411-44-5 Disclaimer 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. Trademarks 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 Keil- lor. All other product names mentioned in this manual may be trademarks or registered trade- marks 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
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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 vol- ume 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 coef- ficient 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 regres- sion 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 fea- tures. 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 Estima- tion” ). 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.
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