Essentials of Econometrics

This text provides a simple and straightforward introduction to econometrics for the beginner. The author's intent is to provide the student with a "user friendly," non-intimidating introduction to econometric theory and techniques. The book motives students to understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. The audience is undergraduate economics, agricultural economics, and business administration majors, MBA students and others in the social and behavioral sciences where econometric techniques, especially the techniques of linear regression analysis, are used. Fully Updated: Most of the chapters contain new examples, new data sets and updating of the old data sets. The book is as fresh and relevant as ever. Some of the key updates include: Chapters 2-5 on probability and statistics have been rearranged and revised with additional examples. Bayes' Theorem and Chebyshev's Inequality are discussed by example. Also, Chapter 6 on Basic Ideas of Regression Analysis has been rewritten with new examples. The topic of restricted least-squares is discussed in detail in Chapter 8. Chapter 10 on Dummy Variables now includes a discussion of the linear probability model (LPM), a model in which the dependent variable is a dummy variable. The LPM has some conceptual and statistical problems, which can be handled by the Logit model, which is discussed in Chapter 16.|Chapter 16 Expanded to Include Optional Topics: Chapter 16 discusses several topics in single equation regression models that the instructor can discuss on an optional basis. These topics include: dynamic regression models, spurious regression, tests of stationarity, cointegrated time series, the random walk model and the logit model. The discussion is often heuristic and is amply illustrated.|Expanded Integration of Computers & Technology: The Third Edition includes a New Appendix B which discusses the standard output produced by four statistical packages, namely, Eviews, Minitab, Excel and Stata. In addition, Gujarati now includes computer printouts to enhance understanding. |Data Disk Included: All the data sets included in the various chapters as well as in the exercises are available in text (ASCII) and Excel format on the data disk packaged with the text.|Little algebra or calculus is used. The author believes econometrics can be taught to the beginner in an intuitive manner without a heavy dose of matrix algebra or calculus.|Proofs are not included unless they are easily understood. The non-specialist does not need intensive coverage of proofs. The author's other book, Basic Econometrics, includes proofs.|The author's problem solving approach is evident in the 300 end of chapter questions and problems. Answers to odd-numbered problems are in the text. The Instructor's Manual has detailed solutions to all questions and problems. In Appendix B the author shows the outputs of EVIEWS, EXCEL, MINITAB, and STATE, using a common data set. All data sets included in the chapters and in text exercises are on a data diskette, included with the text. |User-friendly computer statistical packages make econometrics accessible to the beginner. The illustrative problems are solved using these packages e.g. EVIEWS, MINITAB, EXCEL and STATA. The data diskette can be read by many standard statistical packages such as LIMDEP, RATS, SAS, and SPSS. |Chapter 1 has a section on methodology of econometrics emphasizing early that one should check for model adequacy before using the estimated model for hypothesis testing and for forecasting.|An appendix of web sites in Chapter 1 provides a variety of economic data.|Discussions on skewness and kurtosis are introduced in Chapter 3 and are later used in the text to determine if a random variable is normally distributed; the normal distribution lies at the core of econometric theory.|Chapter 4 has an extended discussion of how one obtains a random sample from a normal distribution. Also included is bootstrap sampling and an extension of sampling or probability distribution of a random variable and the Central Limit Theorem. To develop this discussion, the Monte Carlo (simulation) experiments are featured. Also included is a treatment of the interrelationships among the normal, t, Chi-square, and F distributions, which form the foundation of most statistical theory and practice. |In Chapter 7, Monte Carlo experiments demonstrate certain properties of the ordinary least-squares estimators. Also included is an extended discussion of normality tests and more numerical examples.|The topic of structural stability of regression models (ch.8) and how the Chow test answers this question is discussed. Dummy variables are shown as an alternative to the Chow test.|The chapter on heteroscedasticity (ch.13.) includes White's general test of heteroscedasticity and White's heteroscedasticity-corrected standard errors, and t-statistics. In the topic of Model Selection, two frequently used model selection criteria for forecasting purposes viz.: the Akaike information criterion and Schwartz information criterion are discussed. |The chapter on topics in single equation regression models (ch.16) includes: spurious regression, Spurious regression: Nonstationary Time Series, tests of stationarity, Cointegrated time series, the random walk models, and the Logit Model. These problems are of particular use in using time series.|Chapter 15, Simultaneous Equation Models, introduces such concepts as: the simultaneity problem, the identification problem, indirect least-squares (ILS), two-stage least-squares (2SLS). These concepts are accompanied with concrete macro-economic data relating to the U.S. economy.