kingkong_jin 发表于 2011-12-31 01:34 
麻烦楼主给个简介？
PREFACE
BACKGROUND AND PURPOSE
As in the previous three editions, the primary objective of the fourth edition
of Basic Econometrics is to provide an elementary but comprehensive introduction
to econometrics without resorting to matrix algebra, calculus, or
statistics beyond the elementary level.
In this edition I have attempted to incorporate some of the developments
in the theory and practice of econometrics that have taken place since the
publication of the third edition in 1995. With the availability of sophisticated
and user-friendly statistical packages, such as Eviews, Limdep,
Microfit, Minitab, PcGive, SAS, Shazam, and Stata, it is now possible to discuss
several econometric techniques that could not be included in the previous
editions of the book. I have taken full advantage of these statistical
packages in illustrating several examples and exercises in this edition.
I was pleasantly surprised to find that my book is used not only by economics
and business students but also by students and researchers in several
other disciplines, such as politics, international relations, agriculture,
and health sciences. Students in these disciplines will find the expanded discussion
of several topics very useful.
THE FOURTH EDITI
The major changes in this edition are as follows:
1. In the introductory chapter, after discussing the steps involved in traditional
econometric methodology, I discuss the very important question of
how one chooses among competing econometric models.
2. In Chapter 1, I discuss very briefly the measurement scale of economic
variables. It is important to know whether the variables are ratio
scale, interval scale, ordinal scale, or nominal scale, for that will determine
the econometric technique that is appropriate in a given situation.
3. The appendices to Chapter 3 now include the large-sample properties
of OLS estimators, particularly the property of consistency.
4. The appendix to Chapter 5 now brings into one place the properties
and interrelationships among the four important probability distributions
that are heavily used in this book, namely, the normal, t, chi square, and F.
5. Chapter 6, on functional forms of regression models, now includes a
discussion of regression on standardized variables.
6. To make the book more accessible to the nonspecialist, I have moved
the discussion of the matrix approach to linear regression from old Chapter 9
to Appendix C. Appendix C is slightly expanded to include some advanced
material for the benefit of the more mathematically inclined students. The
new Chapter 9 now discusses dummy variable regression models.
7. Chapter 10, on multicollinearity, includes an extended discussion of
the famous Longley data, which shed considerable light on the nature and
scope of multicollinearity.
8. Chapter 11, on heteroscedasticity, now includes in the appendix an
intuitive discussion of White’s robust standard errors.
9. Chapter 12, on autocorrelation, now includes a discussion of the
Newey–West method of correcting the OLS standard errors to take into account
likely autocorrelation in the error term. The corrected standard errors
are known as HAC standard errors. This chapter also discusses briefly the
topic of forecasting with autocorrelated error terms.
10. Chapter 13, on econometric modeling, replaces old Chapters 13 and
14. This chapter has several new topics that the applied researcher will find
particularly useful. They include a compact discussion of model selection
criteria, such as the Akaike information criterion, the Schwarz information
criterion, Mallows’s Cp criterion, and forecast chi square. The chapter also
discusses topics such as outliers, leverage, influence, recursive least squares,
and Chow’s prediction failure test. This chapter concludes with some cautionary
advice to the practitioner about econometric theory and econometric
practice.
11. Chapter 14, on nonlinear regression models, is new. Because of the
easy availability of statistical software, it is no longer difficult to estimate
regression models that are nonlinear in the parameters. Some econometric
models are intrinsically nonlinear in the parameters and need to be estimated
by iterative methods. This chapter discusses and illustrates some
comparatively simple methods of estimating nonlinear-in-parameter regression
models.
12. Chapter 15, on qualitative response regression models, which replaces
old Chapter 16, on dummy dependent variable regression models,
provides a fairly extensive discussion of regression models that involve a
dependent variable that is qualitative in nature. The main focus is on logit
and probit models and their variations. The chapter also discusses the
Poisson regression model, which is used for modeling count data, such as the
number of patents received by a firm in a year; the number of telephone
calls received in a span of, say, 5 minutes; etc. This chapter has a brief discussion
of multinomial logit and probit models and duration models.
13. Chapter 16, on panel data regression models, is new. A panel data
combines features of both time series and cross-section data. Because of increasing
availability of panel data in the social sciences, panel data regression
models are being increasingly used by researchers in many fields. This
chapter provides a nontechnical discussion of the fixed effects and random
effects models that are commonly used in estimating regression models
based on panel data.
14. Chapter 17, on dynamic econometric models, has now a rather extended
discussion of the Granger causality test, which is routinely used (and
misused) in applied research. The Granger causality test is sensitive to the
number of lagged terms used in the model. It also assumes that the underlying
time series is stationary.
15. Except for new problems and minor extensions of the existing estimation
techniques, Chapters 18, 19, and 20 on simultaneous equation models
are basically unchanged. This reflects the fact that interest in such models
has dwindled over the years for a variety of reasons, including their poor
forecasting performance after the OPEC oil shocks of the 1970s.
16. Chapter 21 is a substantial revision of old Chapter 21. Several concepts
of time series econometrics are developed and illustrated in this chapter. The
main thrust of the chapter is on the nature and importance of stationary
time series. The chapter discusses several methods of finding out if a given
time series is stationary. Stationarity of a time series is crucial for the application
of various econometric techniques discussed in this book.
17. Chapter 22 is also a substantial revision of old Chapter 22. It discusses
the topic of economic forecasting based on the Box–Jenkins (ARIMA) and
vector autoregression (VAR) methodologies. It also discusses the topic of
measuring volatility in financial time series by the techniques of autoregressive
conditional heteroscedasticity (ARCH) and generalized autoregressive conditional
heteroscedasticity (GARCH).
18. Appendix A, on statistical concepts, has been slightly expanded. Appendix
C discusses the linear regression model using matrix algebra. This is
for the benefit of the more advanced students.
As in the previous editions, all the econometric techniques discussed in
this book are illustrated by examples, several of which are based on concrete
data from various disciplines. The end-of-chapter questions and problems
have several new examples and data sets. For the advanced reader,
there are several technical appendices to the various chapters that give
proofs of the various theorems and or formulas developed in the text.
ORGANIZATION AND OPTIONS
Changes in this edition have considerably expanded the scope of the text. I
hope this gives the instructor substantial flexibility in choosing topics that
are appropriate to the intended audience. Here are suggestions about how
this book may be used.
One-semester course for the nonspecialist: Appendix A, Chapters 1
through 9, an overview of Chapters 10, 11, 12 (omitting all the proofs).
One-semester course for economics majors: Appendix A, Chapters 1
through 13.
Two-semester course for economics majors: Appendices A, B, C,
Chapters 1 to 22. Chapters 14 and 16 may be covered on an optional basis.
Some of the technical appendices may be omitted.
Graduate and postgraduate students and researchers: This book is a
handy reference book on the major themes in econometrics.
............................
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