Gujarati: Basic T Front Matter Preface T ② The McG econometrics. Fourth 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 intro- duction to econometrics without resorting to matrix algebra, calculus, or statistics beyond the elementary level 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 sophist ed and user-friendly statistica h Microfit, Minitab, PcGive, SAS, Shazam, and Stata, it is now possible to dis cuss several econometric techniques that could not be included in the pre- vious 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 eco- nomics and business students but also by students and researchers in set eral other discipl ch as polit ulture and health sciences. Students in these disciplines will find the expanded dis cussion of several topics very usefu THE FOURTH EDITION The major changes in this edition are as follows: 1. In the introductory chapter, after discussing the steps involved in tra ditional econometric methodology, I discuss the very important question of cic model 2. In Chapter 1, i discuss very briefly the measurement scale of eco- lomic variables. It is important to know whether the variables are ratio
Gujarati: Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 xxv 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 EDITION 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
Gujarati: Basic T Front Matter Preface T ② The McG 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 include 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 ar 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 ac count 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 compae act 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 Chows prediction failure test. This chapter concludes with some cau- tionary advice to the practitioner about econometric theory and economet ric 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 esti mated by iterative methods. This chapter discusses and illustrates some omparatively simple methods of estimating nonlinear-in-parameterregres- sion models 12. Chapter 15, on qualitative response regression models, which re places 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 logi
Gujarati: Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 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
Gujarati: Basic T Front Matter Preface T ② The McG econometrics. Fourth PREFACE XXVI 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 dis- cussion 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 in- creasing availability of panel data in the social sciences, panel data regres sion 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 ex- tended 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 under- lying time series is stationary 15. Except for new problems and minor extensions of the existing esti mation techniques, Chapters 18, 19, and 20 on simultaneous equation mod els are basically unchanged. This reflects the fact that interest in such mod els 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 appli cation 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 autoregres sive conditional heteroscedasticity(ARCH)and generalized autoregressive con ditional heteroscedasticity(GARCH) 18. Appendix A, on statistical concepts, has been slightly expanded. Ap- pendix 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 con crete data from various disciplines. The end-of-chapter questions and prob- lems 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
Gujarati: Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 PREFACE xxvii 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
Gujarati: Basic T Front Matter Preface T ② The McG econometrics. Fourth PREFACE 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 wo-semester course for economics majors: Appendices A, B, C, Chapters 1 to 22 Chapters 14 and 16 may be co optional basi Some of the technical appendices may be omitted Graduate and postgraduate students and researchers: This book is handy reference book on the major themes in econometrics. SUPPLEMENTS Data CD Every text is packaged with a CD that contains the data from the text in ASCII or text format and can be read by most software packages Student solutions manual Free to instructors and salable to students is a student solutions manual (IsBN 0072427922)that contains detailed solutions to the 475 questions and problems in the text With this fourth edition we are pleased to provide Eviews Student Ver sion 3. 1 on a cd along with all of the data from the text. This software is available from the publisher packaged with the text(ISBN: 0072565705) Eviews Student Version is available separately from QMS. Go to http://www.eviews.comforfurtherinformation Web site A comprehensive web site provides additional material to support the study ofeconometricsgOtowww.mhhe.com/econometrics/gujarati4. ACKNOWLEDGMENTS Since the publication of the first edition of this book in 1978, I have received valuable advice, comments, criticism, and suggestions from a variety of people. In particular, I would like to acknowledge the help i have received
Gujarati: Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 xxviii PREFACE 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. SUPPLEMENTS Data CD Every text is packaged with a CD that contains the data from the text in ASCII or text format and can be read by most software packages. Student Solutions Manual Free to instructors and salable to students is a Student Solutions Manual (ISBN 0072427922) that contains detailed solutions to the 475 questions and problems in the text. EViews With this fourth edition we are pleased to provide Eviews Student Version 3.1 on a CD along with all of the data from the text. This software is available from the publisher packaged with the text (ISBN: 0072565705). Eviews Student Version is available separately from QMS. Go to http://www.eviews.com for further information. Web Site A comprehensive web site provides additional material to support the study of econometrics. Go to www.mhhe.com/econometrics/gujarati4. ACKNOWLEDGMENTS Since the publication of the first edition of this book in 1978, I have received valuable advice, comments, criticism, and suggestions from a variety of people. In particular, I would like to acknowledge the help I have received
Gujarati: Basic T Front Matter Preface T ② The McG econometrics. Fourth PREFACE XXIX from Michael McAleer of the University of Western Australia, Peter Kennedy Virginia, and Paul Offner, Georgetown University, Washington, D. versi I am also grateful to several people who have influenced me by their scholarship. I especially want to thank Arthur Goldberger of the University of Wisconsin, William Greene of New York University, and the late G. s Maddala. For this fourth edition I am especially grateful to these reviewers who provided their invaluable insight, criticism, and suggestions: Michael A Grove at the University of Oregon, Harumi Ito at Brown University, Han Kim at South Dakota University, Phanindra V. Wunnava at Middlebury Col lege, and George K. Zestos of Christopher Newport University Several authors have influenced my writing. In particular, I am grateful to these authors: Chandan Mukherjee, director of the Centre for Development Studies, Trivandrum, India; Howard White and Marc Wuyts, both at the Institute of Social Studies in the Netherlands: Badi H. Baltagi, Texas A&M University; B. Bhaskara Rao, University of New South Wales, Australia R. Carter Hill, Louisiana University; William E. Griffiths, University of New England; George G. Judge, University of California at Berkeley: Marno Verbeek, Center for Economic Studies, KU Leuven; Jeffrey Wooldridge, Francis X Diebold, Wharton School, University of Pennsylvania; Wojciech W. Charemza and derek F. Deadman, both of the University of Leicester, U.K.: Gary Koop, University of Glasgo I am very grateful to several of my colleagues at West Point for their sup port and encour agement over the years. In particular, I am grateful to Brigadier General Daniel Kaufman, Colonel Howard Russ, Lieutenant Colonel Mike Meese, Lieutenant Colonel Casey Wardynski, Major David Trybulla, Major Kevin Foster, Dean Dudley, and Dennis Smallwood. I would like to thank students and teachers all over the world who have not only used my book but have communicated with me about various as- pects of the book For their behind the scenes help at McGraw-Hill, I am grateful to lucille Sutton, Aric Bright, and Catherine R. schultz George F. Watson, the copyeditor, has done a marvellous job in editing a rather lengthy and demanding manuscript. For that, I am much obliged to him Finally, but not least important, I would like to thank my wife, Pushy nd my daughters, Joan and Diane, for their constant support and encour- agement in the preparation of this and the previous editions Damodar N gujarati
Gujarati: Basic Econometrics, Fourth Edition Front Matter Preface © The McGraw−Hill Companies, 2004 PREFACE xxix from Michael McAleer of the University of Western Australia, Peter Kennedy of Simon Frazer University in Canada, and Kenneth White, of the University of British Columbia, George K. Zestos of Christopher Newport University, Virginia, and Paul Offner, Georgetown University, Washington, D.C. I am also grateful to several people who have influenced me by their scholarship. I especially want to thank Arthur Goldberger of the University of Wisconsin, William Greene of New York University, and the late G. S. Maddala. For this fourth edition I am especially grateful to these reviewers who provided their invaluable insight, criticism, and suggestions: Michael A. Grove at the University of Oregon, Harumi Ito at Brown University, Han Kim at South Dakota University, Phanindra V. Wunnava at Middlebury College, and George K. Zestos of Christopher Newport University. Several authors have influenced my writing. In particular, I am grateful to these authors: Chandan Mukherjee, director of the Centre for Development Studies, Trivandrum, India; Howard White and Marc Wuyts, both at the Institute of Social Studies in the Netherlands; Badi H. Baltagi, Texas A&M University; B. Bhaskara Rao, University of New South Wales, Australia; R. Carter Hill, Louisiana University; William E. Griffiths, University of New England; George G. Judge, University of California at Berkeley; Marno Verbeek, Center for Economic Studies, KU Leuven; Jeffrey Wooldridge, Michigan State University; Kerry Patterson, University of Reading, U.K.; Francis X. Diebold, Wharton School, University of Pennsylvania; Wojciech W. Charemza and Derek F. Deadman, both of the University of Leicester, U.K.; Gary Koop, University of Glasgow. I am very grateful to several of my colleagues at West Point for their support and encouragement over the years. In particular, I am grateful to Brigadier General Daniel Kaufman, Colonel Howard Russ, Lieutenant Colonel Mike Meese, Lieutenant Colonel Casey Wardynski, Major David Trybulla, Major Kevin Foster, Dean Dudley, and Dennis Smallwood. I would like to thank students and teachers all over the world who have not only used my book but have communicated with me about various aspects of the book. For their behind the scenes help at McGraw-Hill, I am grateful to Lucille Sutton, Aric Bright, and Catherine R. Schultz. George F. Watson, the copyeditor, has done a marvellous job in editing a rather lengthy and demanding manuscript. For that, I am much obliged to him. Finally, but not least important, I would like to thank my wife, Pushpa, and my daughters, Joan and Diane, for their constant support and encouragement in the preparation of this and the previous editions. Damodar N. Gujarati
Introduction ② The McG econometrics. Fourth INTRODUCTION L1 WHAT IS ECONOMETRICS? Literally interpreted, econometrics means "economic measurement. " Al though measurement is an important part of econometrics, the scope of econometrics is much broader, as can be seen from the following quotations: Econometrics, the result of a certain outlook on the role of economics. consists of the application of mathematical statistics to economic data to lend empirical sup- port to the models constructed by mathematical economics and to obtain numerical results I econometrics may be defined as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, re- lated by appropriate methods of inference. Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of eco- nomic phenomena Econometrics is concerned with the empirical determination of economic Gerhard Tintner, Methodology of Mathematical Economics and Econometrics, The Univer- sity of Chicago Press, Chicago, 1968, p. 74 2P. A Samuelson, T. C. Koopmans, and J. R N. Stone, "Report of the Evaluative Committee for Econometrica, "Econometrica, vol 22, no. 2, April 1954, pp. 141-146 H. Theil, Principles of Econometrics, John Wiley Sons, New York, 1971,P./.p.I
Gujarati: Basic Econometrics, Fourth Edition Front Matter Introduction © The McGraw−Hill Companies, 2004 1 INTRODUCTION I.1 WHAT IS ECONOMETRICS? Literally interpreted, econometrics means “economic measurement.” Although measurement is an important part of econometrics, the scope of econometrics is much broader, as can be seen from the following quotations: Econometrics, the result of a certain outlook on the role of economics, consists of the application of mathematical statistics to economic data to lend empirical support to the models constructed by mathematical economics and to obtain numerical results.1 . . . econometrics may be defined as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference.2 Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena.3 Econometrics is concerned with the empirical determination of economic laws.4 1 Gerhard Tintner, Methodology of Mathematical Economics and Econometrics, The University of Chicago Press, Chicago, 1968, p. 74. 2 P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone, “Report of the Evaluative Committee for Econometrica,” Econometrica, vol. 22, no. 2, April 1954, pp. 141–146. 3 Arthur S. Goldberger, Econometric Theory, John Wiley & Sons, New York, 1964, p. 1. 4 H. Theil, Principles of Econometrics, John Wiley & Sons, New York, 1971, p. 1
Introduction ② The McG Econometrics. Fourth 2 BASIC ECONOMETRICS The art of the econometrician consists in finding the set of assumptions that are both sufficiently specific and sufficiently realistic to allow him to take the best possible advantage of the data available to him.5 Econometricians. are a positive help in trying to dispel the poor public image of economics(quantitative or otherwise) as a subject in which empty boxes are pened by assuming the existence of can- openers to reveal contents which any ten economists will interpret in 11 ways. The method of econometric research aims, essentially, at a conjunction of eco- nomic theory and actual measurements, using the theory and technique of statis- L2 WHY A SEPARATE DISCIPLINE? As the preceding definitions suggest, econometrics is an amalgam of eco- nomic theory, mathematical economics, economic statistics, and mathe matical statistics. Yet the subject deserves to be studied in its own right for the following reasons. Economic theory makes statements or hypotheses that are mostly quali ative in nature. For example, microeconomic theory states that, other things remaining the same, a reduction in the price of a commodity is ex pected to increase the quantity demanded of that commodity. Thus, eco- nomic theory postulates a negative or inverse relationship between the price and quantity demanded of a commodity. But the theory itself does not pro- vide any numerical measure of the relationship between the two; that is, it does not tell by how much the quantity will go up or down as a result of a certain change in the price of the commodity. It is the job of the econome- rician to provide such numerical estimates. Stated differently, economet- rics gives empirical content to most economic theory lho he main concern of mathematical economics is to express economic eory in mathematical form(equations)without regard to measurability or empirical verification of the theory. Econometrics, as noted previously, is mainly interested in the empirical verification of economic theory. As we shall see, the econometrician often uses the mathematical equations pro- posed by the mathematical economist but puts these equations in such a form that they lend themselves to empirical testing. And this conversion of mathematical into econometric equations requires a great deal of ingenuity and practical skil Economic statistics is mainly concerned with collecting, processing, and presenting economic data in the form of charts and tables. These are the ud, Statistical Methods of Econometrics, Rand McNally, Chicago, 1966, P. 514 Adrian C. Darnell and J. Lynne Evans, The Limits of Econometrics, Edward Elgar Publish ing, Hants, England, 1990, p. 54 T Haavelmo, "The Probability Approach in Econometrics, "Supplement to Econometrica, vol.12,1944,pr
Gujarati: Basic Econometrics, Fourth Edition Front Matter Introduction © The McGraw−Hill Companies, 2004 2 BASIC ECONOMETRICS 5 E. Malinvaud, Statistical Methods of Econometrics, Rand McNally, Chicago, 1966, p. 514. 6 Adrian C. Darnell and J. Lynne Evans, The Limits of Econometrics, Edward Elgar Publishing, Hants, England, 1990, p. 54. 7 T. Haavelmo, “The Probability Approach in Econometrics,” Supplement to Econometrica, vol. 12, 1944, preface p. iii. The art of the econometrician consists in finding the set of assumptions that are both sufficiently specific and sufficiently realistic to allow him to take the best possible advantage of the data available to him.5 Econometricians . . . are a positive help in trying to dispel the poor public image of economics (quantitative or otherwise) as a subject in which empty boxes are opened by assuming the existence of can-openers to reveal contents which any ten economists will interpret in 11 ways.6 The method of econometric research aims, essentially, at a conjunction of economic theory and actual measurements, using the theory and technique of statistical inference as a bridge pier.7 I.2 WHY A SEPARATE DISCIPLINE? As the preceding definitions suggest, econometrics is an amalgam of economic theory, mathematical economics, economic statistics, and mathematical statistics. Yet the subject deserves to be studied in its own right for the following reasons. Economic theory makes statements or hypotheses that are mostly qualitative in nature. For example, microeconomic theory states that, other things remaining the same, a reduction in the price of a commodity is expected to increase the quantity demanded of that commodity. Thus, economic theory postulates a negative or inverse relationship between the price and quantity demanded of a commodity. But the theory itself does not provide any numerical measure of the relationship between the two; that is, it does not tell by how much the quantity will go up or down as a result of a certain change in the price of the commodity. It is the job of the econometrician to provide such numerical estimates. Stated differently, econometrics gives empirical content to most economic theory. The main concern of mathematical economics is to express economic theory in mathematical form (equations) without regard to measurability or empirical verification of the theory. Econometrics, as noted previously, is mainly interested in the empirical verification of economic theory. As we shall see, the econometrician often uses the mathematical equations proposed by the mathematical economist but puts these equations in such a form that they lend themselves to empirical testing. And this conversion of mathematical into econometric equations requires a great deal of ingenuity and practical skill. Economic statistics is mainly concerned with collecting, processing, and presenting economic data in the form of charts and tables. These are the
Introduction ② The McG econometrics. Fourth INTRODUCTIon 3 jobs of the economic statistician. It is he or she who is primarily responsible for collecting data on gross national product(GNP), employment, unem- ployment, prices, etc. The data thus collected constitute the raw data for econometric work. But the economic statistician does not go any further, not being concerned with using the collected data to test economic theories Of course. one who does that becomes an econometrician. Although mathematical statistics provides many tools used in the trade the econometrician often needs special methods in view of the unique na- ture of most economic data, namely, that the data are not generated as the result of a controlled experiment. The econometrician, like the meteorolo- gist, generally depends on data that cannot be controlled directly. As Spanos correctly observes: In econometrics the modeler is often faced with observational as opposed to experimental data. This has two important implications for empirical modeling in econometrics. First, the modeler is required to master very different skills than those needed for analyzing experimental data. . Second, the separation of the data collector and the data analyst requires the modeler to familiarize himself/herself thoroughly with the nature and structure of data in question. 3 METHODOLOGY OF ECONOMETRICS How do econometricians proceed in their analysis of an economic problem? That is, what is their methodology? Although there are several schools of thought on econometric methodology, we present here the traditional or classical methodology, which still dominates empirical research in eco- nomics and other social and behavioral sciences .9 Broadly speaking, traditional econometric methodology proceeds along the following lines 1. Statement of theory or hypothesis 2. Specification of the mathematical model of the theory 3. Specification of the statistical, or econometric, model 4. Obtaining the data 5. Estimation of the parameters of the econometric model 6. Hypothesis testing 7. Forecasting or prediction 8. Using the model for control or policy purposes To illustrate the preceding steps, let us consider the well-known Keynesian theory of consumption SAris Spanos, Probability Theory and Statistical Inference: Econometric Modeling with Obser vational Data, Cambridge University Press, United Kingdom, 1999, p. 21 For an enlightening, if advanced, discussion on econometric methodology, see david F. Hendry, Dynamic Econometrics, Oxford University Press, New York, 1995. See also Aris Spanos, op cit
Gujarati: Basic Econometrics, Fourth Edition Front Matter Introduction © The McGraw−Hill Companies, 2004 INTRODUCTION 3 8 Aris Spanos, Probability Theory and Statistical Inference: Econometric Modeling with Observational Data, Cambridge University Press, United Kingdom, 1999, p. 21. 9 For an enlightening, if advanced, discussion on econometric methodology, see David F. Hendry, Dynamic Econometrics, Oxford University Press, New York, 1995. See also Aris Spanos, op. cit. jobs of the economic statistician. It is he or she who is primarily responsible for collecting data on gross national product (GNP), employment, unemployment, prices, etc. The data thus collected constitute the raw data for econometric work. But the economic statistician does not go any further, not being concerned with using the collected data to test economic theories. Of course, one who does that becomes an econometrician. Although mathematical statistics provides many tools used in the trade, the econometrician often needs special methods in view of the unique nature of most economic data, namely, that the data are not generated as the result of a controlled experiment. The econometrician, like the meteorologist, generally depends on data that cannot be controlled directly. As Spanos correctly observes: In econometrics the modeler is often faced with observational as opposed to experimental data. This has two important implications for empirical modeling in econometrics. First, the modeler is required to master very different skills than those needed for analyzing experimental data. . . . Second, the separation of the data collector and the data analyst requires the modeler to familiarize himself/herself thoroughly with the nature and structure of data in question.8 I.3 METHODOLOGY OF ECONOMETRICS How do econometricians proceed in their analysis of an economic problem? That is, what is their methodology? Although there are several schools of thought on econometric methodology, we present here the traditional or classical methodology, which still dominates empirical research in economics and other social and behavioral sciences.9 Broadly speaking, traditional econometric methodology proceeds along the following lines: 1. Statement of theory or hypothesis. 2. Specification of the mathematical model of the theory 3. Specification of the statistical, or econometric, model 4. Obtaining the data 5. Estimation of the parameters of the econometric model 6. Hypothesis testing 7. Forecasting or prediction 8. Using the model for control or policy purposes. To illustrate the preceding steps, let us consider the well-known Keynesian theory of consumption
Introduction ② The McG Econometrics. Fourth 4 BASIC ECONOMETRICS Statement of Theory or Hypothesis Keynes stated The fundamental psychological law. . is that men [women] are disposed, as a rule and on average, to increase their consumption as their income increases, but not as much as the increase in their income In short, Keynes postulated that the marginal propensity to consume (MPC), the rate of change of consumption for a unit(say, a dollar)change in income, is greater than zero but less than 1 2. Specification of the Mathematical Model of Consumption Although Keynes postulated a positive relationship between consumption and income, he did not specify the precise form of the functional relation- ship between the two. For simplicity, a mathematical economist might sug gest the following form of the Keynesian consumption function Y=B1+B2X0<B2<1 (I3.1) where Y= consumption expenditure and X= income, and where B1 and B2 known as the parameters of the model, are, respectively, the intercept and slope coefficients The slope coefficient B2 measures the MPC. Geometrically, Eq (I.3.1)is as shown in Figure I 1. This equation, which states that consumption is lin β2=MPC FIGURE L1 Keynesian consumption function John Maynard Keynes, The General Theory of Employment, Interest and Money, Harcourt
Gujarati: Basic Econometrics, Fourth Edition Front Matter Introduction © The McGraw−Hill Companies, 2004 4 BASIC ECONOMETRICSConsumption expenditure X Income 1 β2 = MPC β1 Y FIGURE I.1 Keynesian consumption function. 10John Maynard Keynes, The General Theory of Employment, Interest and Money, Harcourt Brace Jovanovich, New York, 1936, p. 96. 1. Statement of Theory or Hypothesis Keynes stated: The fundamental psychological law . . . is that men [women] are disposed, as a rule and on average, to increase their consumption as their income increases, but not as much as the increase in their income.10 In short, Keynes postulated that the marginal propensity to consume (MPC), the rate of change of consumption for a unit (say, a dollar) change in income, is greater than zero but less than 1. 2. Specification of the Mathematical Model of Consumption Although Keynes postulated a positive relationship between consumption and income, he did not specify the precise form of the functional relationship between the two. For simplicity, a mathematical economist might suggest the following form of the Keynesian consumption function: Y = β1 + β2X 0 < β2 < 1 (I.3.1) where Y = consumption expenditure and X = income, and where β1 and β2, known as the parameters of the model, are, respectively, the intercept and slope coefficients. The slope coefficient β2 measures the MPC. Geometrically, Eq. (I.3.1) is as shown in Figure I.1. This equation, which states that consumption is lin-
Introduction ② The McG econometrics. Fourth INTRODUCTION 5 early related to income, is an example of a mathematical model of the rela tionship between consumption and income that is called the consumption function in economics. A model is simply a set of mathematical equations If the model has only one equation, as in the preceding example, it is called a single-equation model, whereas if it has more than one equation, it is known as a multiple-equation model (the latter will be considered later the book) In Eq(I.3. 1) the variable appearing on the left side of is called the dependent variable and the variable(s) on the right side are called the independent, or explanatory, variable(s). Thus, in the Keynesian consumption function, Eq (I.3. 1), consumption(expenditure)is the depen dent variable and income is the explanatory variable 3. Specification of the Econometric Model of Consumption The purely mathematical model of the consumption function given in Eq.(I.3. 1) is of limited interest to the econometrician, for it assumes that there is an exact or deterministic relationship between consumption and income. But relationships between economic variables are generally inexact Thus, if we were to obtain data on consumption expenditure and disposable (i.e, aftertax) income of a sample of, say, 500 American families and plot these data on a graph paper with consumption expenditure on the vertical axis and disposable income on the horizontal axis, we would not expect all 500 observations to lie exactly on the straight line of Eq(1.3.1) because, in ddition to income, other variables affect consumption expenditure. For ex- ample, size of family, ages of the members in the family, family religion, etc are likely to exert some influence on consumption. To allow for the inexact relationships between economic variables, the econometrician would modify the deterministic consumption function (1.3. 1) as follows Y=B1+B2X+u (I3.2) where u, known as the disturbance, or error, term, is a random(stochas- tic)variable that has well-defined probabilistic properties. The disturbance term u may well represent all those factors that affect consumption but are not taken into account explicitly Equation(1. 3. 2)is an example of an econometric model. More techni cally, it is an example of a linear regression model, which is the major concern of this book. The econometric consumption function hypothesizes that the dependent variable Y(consumption) is linearly related to the ex planatory variable X (income) but that the relationship between the two is not exact; it is subject to individual variation The econometric model of the consumption function can be depicted as hown in Figure 1.2
Gujarati: Basic Econometrics, Fourth Edition Front Matter Introduction © The McGraw−Hill Companies, 2004 INTRODUCTION 5 early related to income, is an example of a mathematical model of the relationship between consumption and income that is called the consumption function in economics. A model is simply a set of mathematical equations. If the model has only one equation, as in the preceding example, it is called a single-equation model, whereas if it has more than one equation, it is known as a multiple-equation model (the latter will be considered later in the book). In Eq. (I.3.1) the variable appearing on the left side of the equality sign is called the dependent variable and the variable(s) on the right side are called the independent, or explanatory, variable(s). Thus, in the Keynesian consumption function, Eq. (I.3.1), consumption (expenditure) is the dependent variable and income is the explanatory variable. 3. Specification of the Econometric Model of Consumption The purely mathematical model of the consumption function given in Eq. (I.3.1) is of limited interest to the econometrician, for it assumes that there is an exact or deterministic relationship between consumption and income. But relationships between economic variables are generally inexact. Thus, if we were to obtain data on consumption expenditure and disposable (i.e., aftertax) income of a sample of, say, 500 American families and plot these data on a graph paper with consumption expenditure on the vertical axis and disposable income on the horizontal axis, we would not expect all 500 observations to lie exactly on the straight line of Eq. (I.3.1) because, in addition to income, other variables affect consumption expenditure. For example, size of family, ages of the members in the family, family religion, etc., are likely to exert some influence on consumption. To allow for the inexact relationships between economic variables, the econometrician would modify the deterministic consumption function (I.3.1) as follows: Y = β1 + β2X + u (I.3.2) where u, known as the disturbance, or error, term, is a random (stochastic) variable that has well-defined probabilistic properties. The disturbance term u may well represent all those factors that affect consumption but are not taken into account explicitly. Equation (I.3.2) is an example of an econometric model. More technically, it is an example of a linear regression model, which is the major concern of this book. The econometric consumption function hypothesizes that the dependent variable Y (consumption) is linearly related to the explanatory variable X (income) but that the relationship between the two is not exact; it is subject to individual variation. The econometric model of the consumption function can be depicted as shown in Figure I.2