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 xxvi PREFACE
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.
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