Basic Econometrics PDF - Gujarati 4th Edition

Gujarati: Basic Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition...

Gujarati: Basic Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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. 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 sophisti- cated and user-friendly statistical packages, such as Eviews, Limdep, 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 sev- eral other disciplines, such as politics, international relations, agriculture, and health sciences. Students in these disciplines will find the expanded dis- cussion 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 tra- ditional 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 eco- nomic variables. It is important to know whether the variables are ratio xxv  Gujarati: Basic Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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 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 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 comparatively simple methods of estimating nonlinear-in-parameter regres- 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 logit Gujarati: Basic Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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 Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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.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 Front Matter Preface © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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, 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 sup- port 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 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, Pushpa, and 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 Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition INTRODUCTION I.1 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.1... 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.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 eco- nomic phenomena.3 Econometrics is concerned with the empirical determination of economic laws.4 1 Gerhard Tintner, Methodology of Mathematical Economics and Econometrics, The Univer- sity 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. 1  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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 eco- nomic theory and actual measurements, using the theory and technique of statis- tical inference as a bridge pier.7 I.2 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- tative 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- trician to provide such numerical estimates. Stated differently, economet- rics 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 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 skill. Economic statistics is mainly concerned with collecting, processing, and presenting economic data in the form of charts and tables. These are the 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 Publish- ing, Hants, England, 1990, p. 54. 7 T. Haavelmo, “The Probability Approach in Econometrics,” Supplement to Econometrica, vol. 12, 1944, preface p. iii. Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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.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 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. 8 Aris Spanos, Probability Theory and Statistical Inference: Econometric Modeling with Obser- vational 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.  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 4 BASIC ECONOMETRICS 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 relation- ship between the two. For simplicity, a mathematical economist might sug- gest the following form of the Keynesian consumption function: Y = β1 + β2 X 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- Y Consumption expenditure β2 = MPC 1 β1 X Income FIGURE I.1 Keynesian consumption function. 10 John Maynard Keynes, The General Theory of Employment, Interest and Money, Harcourt Brace Jovanovich, New York, 1936, p. 96. Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 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 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 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. (I.3.1) because, in addition 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 (I.3.1) as follows: Y = β1 + β2 X + u (I.3.2) 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 (I.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 shown in Figure I.2.  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 6 BASIC ECONOMETRICS Consumption expenditure Y u X Income FIGURE I.2 Econometric model of the Keynesian consumption function. 4. Obtaining Data To estimate the econometric model given in (I.3.2), that is, to obtain the numerical values of β1 and β2 , we need data. Although we will have more to say about the crucial importance of data for economic analysis in the next chapter, for now let us look at the data given in Table I.1, which relate to TABLE I.1 DATA ON Y (PERSONAL CONSUMPTION EXPENDITURE) AND X (GROSS DOMESTIC PRODUCT, 1982–1996), BOTH IN 1992 BILLIONS OF DOLLARS Year Y X 1982 3081.5 4620.3 1983 3240.6 4803.7 1984 3407.6 5140.1 1985 3566.5 5323.5 1986 3708.7 5487.7 1987 3822.3 5649.5 1988 3972.7 5865.2 1989 4064.6 6062.0 1990 4132.2 6136.3 1991 4105.8 6079.4 1992 4219.8 6244.4 1993 4343.6 6389.6 1994 4486.0 6610.7 1995 4595.3 6742.1 1996 4714.1 6928.4 Source: Economic Report of the President, 1998, Table B–2, p. 282. Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition INTRODUCTION 7 5000 4500 PCE (Y) 4000 3500 3000 4000 5000 6000 7000 GDP (X) FIGURE I.3 Personal consumption expenditure (Y ) in relation to GDP (X ), 1982–1996, both in billions of 1992 dollars. the U.S. economy for the period 1981–1996. The Y variable in this table is the aggregate (for the economy as a whole) personal consumption expen- diture (PCE) and the X variable is gross domestic product (GDP), a measure of aggregate income, both measured in billions of 1992 dollars. Therefore, the data are in “real” terms; that is, they are measured in constant (1992) prices. The data are plotted in Figure I.3 (cf. Figure I.2). For the time being neglect the line drawn in the figure. 5. Estimation of the Econometric Model Now that we have the data, our next task is to estimate the parameters of the consumption function. The numerical estimates of the parameters give empirical content to the consumption function. The actual mechanics of es- timating the parameters will be discussed in Chapter 3. For now, note that the statistical technique of regression analysis is the main tool used to obtain the estimates. Using this technique and the data given in Table I.1, we obtain the following estimates of β1 and β2 , namely, −184.08 and 0.7064. Thus, the estimated consumption function is: Ŷ = −184.08 + 0.7064Xi (I.3.3) The hat on the Y indicates that it is an estimate.11 The estimated consump- tion function (i.e., regression line) is shown in Figure I.3. 11 As a matter of convention, a hat over a variable or parameter indicates that it is an esti- mated value.  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 8 BASIC ECONOMETRICS As Figure I.3 shows, the regression line fits the data quite well in that the data points are very close to the regression line. From this figure we see that for the period 1982–1996 the slope coefficient (i.e., the MPC) was about 0.70, suggesting that for the sample period an increase in real income of 1 dollar led, on average, to an increase of about 70 cents in real consumption expenditure.12 We say on average because the relationship between con- sumption and income is inexact; as is clear from Figure I.3; not all the data points lie exactly on the regression line. In simple terms we can say that, ac- cording to our data, the average, or mean, consumption expenditure went up by about 70 cents for a dollar’s increase in real income. 6. Hypothesis Testing Assuming that the fitted model is a reasonably good approximation of reality, we have to develop suitable criteria to find out whether the esti- mates obtained in, say, Eq. (I.3.3) are in accord with the expectations of the theory that is being tested. According to “positive” economists like Milton Friedman, a theory or hypothesis that is not verifiable by appeal to empiri- cal evidence may not be admissible as a part of scientific enquiry.13 As noted earlier, Keynes expected the MPC to be positive but less than 1. In our example we found the MPC to be about 0.70. But before we accept this finding as confirmation of Keynesian consumption theory, we must en- quire whether this estimate is sufficiently below unity to convince us that this is not a chance occurrence or peculiarity of the particular data we have used. In other words, is 0.70 statistically less than 1? If it is, it may support Keynes’ theory. Such confirmation or refutation of economic theories on the basis of sample evidence is based on a branch of statistical theory known as statis- tical inference (hypothesis testing). Throughout this book we shall see how this inference process is actually conducted. 7. Forecasting or Prediction If the chosen model does not refute the hypothesis or theory under consid- eration, we may use it to predict the future value(s) of the dependent, or forecast, variable Y on the basis of known or expected future value(s) of the explanatory, or predictor, variable X. To illustrate, suppose we want to predict the mean consumption expen- diture for 1997. The GDP value for 1997 was 7269.8 billion dollars.14 Putting 12 Do not worry now about how these values were obtained. As we show in Chap. 3, the statistical method of least squares has produced these estimates. Also, for now do not worry about the negative value of the intercept. 13 See Milton Friedman, “The Methodology of Positive Economics,” Essays in Positive Eco- nomics, University of Chicago Press, Chicago, 1953. 14 Data on PCE and GDP were available for 1997 but we purposely left them out to illustrate the topic discussed in this section. As we will discuss in subsequent chapters, it is a good idea to save a portion of the data to find out how well the fitted model predicts the out-of-sample observations. Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition INTRODUCTION 9 this GDP figure on the right-hand side of (I.3.3), we obtain: Ŷ1997 = −184.0779 + 0.7064 (7269.8) (I.3.4) = 4951.3167 or about 4951 billion dollars. Thus, given the value of the GDP, the mean, or average, forecast consumption expenditure is about 4951 billion dol- lars. The actual value of the consumption expenditure reported in 1997 was 4913.5 billion dollars. The estimated model (I.3.3) thus overpredicted the actual consumption expenditure by about 37.82 billion dollars. We could say the forecast error is about 37.82 billion dollars, which is about 0.76 percent of the actual GDP value for 1997. When we fully discuss the linear regression model in subsequent chapters, we will try to find out if such an error is “small” or “large.” But what is important for now is to note that such forecast errors are inevitable given the statistical nature of our analysis. There is another use of the estimated model (I.3.3). Suppose the Presi- dent decides to propose a reduction in the income tax. What will be the ef- fect of such a policy on income and thereby on consumption expenditure and ultimately on employment? Suppose that, as a result of the proposed policy change, investment ex- penditure increases. What will be the effect on the economy? As macroeco- nomic theory shows, the change in income following, say, a dollar’s worth of change in investment expenditure is given by the income multiplier M, which is defined as 1 M= (I.3.5) 1 − MPC If we use the MPC of 0.70 obtained in (I.3.3), this multiplier becomes about M = 3.33. That is, an increase (decrease) of a dollar in investment will even- tually lead to more than a threefold increase (decrease) in income; note that it takes time for the multiplier to work. The critical value in this computation is MPC, for the multiplier depends on it. And this estimate of the MPC can be obtained from regression models such as (I.3.3). Thus, a quantitative estimate of MPC provides valuable in- formation for policy purposes. Knowing MPC, one can predict the future course of income, consumption expenditure, and employment following a change in the government’s fiscal policies. 8. Use of the Model for Control or Policy Purposes Suppose we have the estimated consumption function given in (I.3.3). Suppose further the government believes that consumer expenditure of about 4900 (billions of 1992 dollars) will keep the unemployment rate at its  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 10 BASIC ECONOMETRICS Economic theory Mathematical model of theory Econometric model of theory Data Estimation of econometric model Hypothesis testing Forecasting or prediction Using the model for control or policy purposes FIGURE I.4 Anatomy of econometric modeling. current level of about 4.2 percent (early 2000). What level of income will guarantee the target amount of consumption expenditure? If the regression results given in (I.3.3) seem reasonable, simple arith- metic will show that 4900 = −184.0779 + 0.7064X (I.3.6) which gives X = 7197, approximately. That is, an income level of about 7197 (billion) dollars, given an MPC of about 0.70, will produce an expendi- ture of about 4900 billion dollars. As these calculations suggest, an estimated model may be used for con- trol, or policy, purposes. By appropriate fiscal and monetary policy mix, the government can manipulate the control variable X to produce the desired level of the target variable Y. Figure I.4 summarizes the anatomy of classical econometric modeling. Choosing among Competing Models When a governmental agency (e.g., the U.S. Department of Commerce) col- lects economic data, such as that shown in Table I.1, it does not necessarily have any economic theory in mind. How then does one know that the data really support the Keynesian theory of consumption? Is it because the Keynesian consumption function (i.e., the regression line) shown in Fig- ure I.3 is extremely close to the actual data points? Is it possible that an- Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition INTRODUCTION 11 other consumption model (theory) might equally fit the data as well? For ex- ample, Milton Friedman has developed a model of consumption, called the permanent income hypothesis.15 Robert Hall has also developed a model of consumption, called the life-cycle permanent income hypothesis.16 Could one or both of these models also fit the data in Table I.1? In short, the question facing a researcher in practice is how to choose among competing hypotheses or models of a given phenomenon, such as the consumption–income relationship. As Miller contends: No encounter with data is step towards genuine confirmation unless the hypoth- esis does a better job of coping with the data than some natural rival.... What strengthens a hypothesis, here, is a victory that is, at the same time, a defeat for a plausible rival.17 How then does one choose among competing models or hypotheses? Here the advice given by Clive Granger is worth keeping in mind:18 I would like to suggest that in the future, when you are presented with a new piece of theory or empirical model, you ask these questions: (i) What purpose does it have? What economic decisions does it help with? and; (ii) Is there any evidence being presented that allows me to evaluate its qual- ity compared to alternative theories or models? I think attention to such questions will strengthen economic research and discussion. As we progress through this book, we will come across several competing hypotheses trying to explain various economic phenomena. For example, students of economics are familiar with the concept of the production func- tion, which is basically a relationship between output and inputs (say, capi- tal and labor). In the literature, two of the best known are the Cobb–Douglas and the constant elasticity of substitution production functions. Given the data on output and inputs, we will have to find out which of the two pro- duction functions, if any, fits the data well. The eight-step classical econometric methodology discussed above is neutral in the sense that it can be used to test any of these rival hypotheses. Is it possible to develop a methodology that is comprehensive enough to include competing hypotheses? This is an involved and controversial topic. 15 Milton Friedman, A Theory of Consumption Function, Princeton University Press, Princeton, N.J., 1957. 16 R. Hall, “Stochastic Implications of the Life Cycle Permanent Income Hypothesis: Theory and Evidence,” Journal of Political Economy, 1978, vol. 86, pp. 971–987. 17 R. W. Miller, Fact and Method: Explanation, Confirmation, and Reality in the Natural and Social Sciences, Princeton University Press, Princeton, N.J., 1978, p. 176. 18 Clive W. J. Granger, Empirical Modeling in Economics, Cambridge University Press, U.K., 1999, p. 58.  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 12 BASIC ECONOMETRICS Econometrics Theoretical Applied Classical Bayesian Classical Bayesian FIGURE I.5 Categories of econometrics. We will discuss it in Chapter 13, after we have acquired the necessary econometric theory. I.4 TYPES OF ECONOMETRICS As the classificatory scheme in Figure I.5 suggests, econometrics may be divided into two broad categories: theoretical econometrics and applied econometrics. In each category, one can approach the subject in the clas- sical or Bayesian tradition. In this book the emphasis is on the classical approach. For the Bayesian approach, the reader may consult the refer- ences given at the end of the chapter. Theoretical econometrics is concerned with the development of appro- priate methods for measuring economic relationships specified by econo- metric models. In this aspect, econometrics leans heavily on mathematical statistics. For example, one of the methods used extensively in this book is least squares. Theoretical econometrics must spell out the assumptions of this method, its properties, and what happens to these properties when one or more of the assumptions of the method are not fulfilled. In applied econometrics we use the tools of theoretical econometrics to study some special field(s) of economics and business, such as the produc- tion function, investment function, demand and supply functions, portfolio theory, etc. This book is concerned largely with the development of econometric methods, their assumptions, their uses, their limitations. These methods are illustrated with examples from various areas of economics and business. But this is not a book of applied econometrics in the sense that it delves deeply into any particular field of economic application. That job is best left to books written specifically for this purpose. References to some of these books are provided at the end of this book. I.5 MATHEMATICAL AND STATISTICAL PREREQUISITES Although this book is written at an elementary level, the author assumes that the reader is familiar with the basic concepts of statistical estimation and hypothesis testing. However, a broad but nontechnical overview of the basic statistical concepts used in this book is provided in Appendix A for Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition INTRODUCTION 13 the benefit of those who want to refresh their knowledge. Insofar as mathe- matics is concerned, a nodding acquaintance with the notions of differential calculus is desirable, although not essential. Although most graduate level books in econometrics make heavy use of matrix algebra, I want to make it clear that it is not needed to study this book. It is my strong belief that the fundamental ideas of econometrics can be conveyed without the use of matrix algebra. However, for the benefit of the mathematically inclined stu- dent, Appendix C gives the summary of basic regression theory in matrix notation. For these students, Appendix B provides a succinct summary of the main results from matrix algebra. I.6 THE ROLE OF THE COMPUTER Regression analysis, the bread-and-butter tool of econometrics, these days is unthinkable without the computer and some access to statistical soft- ware. (Believe me, I grew up in the generation of the slide rule!) Fortunately, several excellent regression packages are commercially available, both for the mainframe and the microcomputer, and the list is growing by the day. Regression software packages, such as ET, LIMDEP, SHAZAM, MICRO TSP, MINITAB, EVIEWS, SAS, SPSS, STATA, Microfit, PcGive, and BMD have most of the econometric techniques and tests discussed in this book. In this book, from time to time, the reader will be asked to conduct Monte Carlo experiments using one or more of the statistical packages. Monte Carlo experiments are “fun” exercises that will enable the reader to appreciate the properties of several statistical methods discussed in this book. The details of the Monte Carlo experiments will be discussed at ap- propriate places. I.7 SUGGESTIONS FOR FURTHER READING The topic of econometric methodology is vast and controversial. For those interested in this topic, I suggest the following books: Neil de Marchi and Christopher Gilbert, eds., History and Methodology of Econometrics, Oxford University Press, New York, 1989. This collection of readings discusses some early work on econometric methodology and has an extended discussion of the British approach to econometrics relating to time series data, that is, data collected over a period of time. Wojciech W. Charemza and Derek F. Deadman, New Directions in Econo- metric Practice: General to Specific Modelling, Cointegration and Vector Auto- gression, 2d ed., Edward Elgar Publishing Ltd., Hants, England, 1997. The authors of this book critique the traditional approach to econometrics and give a detailed exposition of new approaches to econometric methodology. Adrian C. Darnell and J. Lynne Evans, The Limits of Econometrics, Edward Elgar Publishers Ltd., Hants, England, 1990. The book provides a somewhat  Gujarati: Basic Front Matter Introduction © The McGraw−Hill Econometrics, Fourth Companies, 2004 Edition 14 BASIC ECONOMETRICS balanced discussion of the various methodological approaches to economet- rics, with renewed allegiance to traditional econometric methodology. Mary S. Morgan, The History of Econometric Ideas, Cambridge University Press, New York, 1990. The author provides an excellent historical perspec- tive on the theory and practice of econometrics, with an in-depth discussion of the early contributions of Haavelmo (1990 Nobel Laureate in Economics) to econometrics. In the same spirit, David F. Hendry and Mary S. Morgan, The Foundation of Econometric Analysis, Cambridge University Press, U.K., 1995, have collected seminal writings in econometrics to show the evolution of econometric ideas over time. David Colander and Reuven Brenner, eds., Educating Economists, Univer- sity of Michigan Press, Ann Arbor, Michigan, 1992, present a critical, at times agnostic, view of economic teaching and practice. For Bayesian statistics and econometrics, the following books are very useful: John H. Dey, Data in Doubt, Basic Blackwell Ltd., Oxford University Press, England, 1985. Peter M. Lee, Bayesian Statistics: An Introduction, Oxford University Press, England, 1989. Dale J. Porier, Intermediate Statis- tics and Econometrics: A Comparative Approach, MIT Press, Cambridge, Massachusetts, 1995. Arnold Zeller, An Introduction to Bayesian Inference in Econometrics, John Wiley & Sons, New York, 1971, is an advanced reference book. Gujarati: Basic I. Single−Equation Introduction © The McGraw−Hill Econometrics, Fourth Regression Models Companies, 2004 Edition PART ONE SINGLE-EQUATION REGRESSION MODELS Part I of this text introduces single-equation regression models. In these models, one variable, called the dependent variable, is expressed as a linear function of one or more other variables, called the explanatory variables. In such models it is assumed implicitly that causal relationships, if any, between the dependent and explanatory variables flow in one direction only, namely, from the explanatory variables to the dependent variable. In Chapter 1, we discuss the historical as well as the modern interpreta- tion of the term regression and illustrate the difference between the two in- terpretations with several examples drawn from economics and other fields. In Chapter 2, we introduce some fundamental concepts of regression analysis with the aid of the two-variable linear regression model, a model in which the dependent variable is expressed as a linear function of only a single explanatory variable. In Chapter 3, we continue to deal with the two-variable model and intro- duce what is known as the classical linear regression model, a model that makes several simplifying assumptions. With these assumptions, we intro- duce the method of ordinary least squares (OLS) to estimate the parameters of the two-variable regression model. The method of OLS is simple to apply, yet it has some very desirable statistical properties. In Chapter 4, we introduce the (two-variable) classical normal linear re- gression model, a model that assumes that the random dependent variable follows the normal probability distribution. With this assumption, the OLS estimators obtained in Chapter 3 possess some stronger statistical proper- ties than the nonnormal classical linear regression model—properties that enable us to engage in statistical inference, namely, hypothesis testing. 15  Gujarati: Basic I. Single−Equation Introduction © The McGraw−Hill Econometrics, Fourth Regression Models Companies, 2004 Edition Chapter 5 is devoted to the topic of hypothesis testing. In this chapter, we try to find out whether the estimated regression coefficients are compatible with the hypothesized values of such coefficients, the hypothesized values being suggested by theory and/or prior empirical work. Chapter 6 considers some extensions of the two-variable regression model. In particular, it discusses topics such as (1) regression through the origin, (2) scaling and units of measurement, and (3) functional forms of regression models such as double-log, semilog, and reciprocal models. In Chapter 7, we consider the multiple regression model, a model in which there is more than one explanatory variable, and show how the method of OLS can be extended to estimate the parameters of such models. In Chapter 8, we extend the concepts introduced in Chapter 5 to the multiple regression model and point out some of the complications arising from the introduction of several explanatory variables. Chapter 9 on dummy, or qualitative, explanatory variables concludes Part I of the text. This chapter emphasizes that not all explanatory variables need to be quantitative (i.e., ratio scale). Variables, such as gender, race, re- ligion, nationality, and region of residence, cannot be readily quantified, yet they play a valuable role in explaining many an economic phenomenon. 16 Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition 1 THE NATURE OF REGRESSION ANALYSIS As mentioned in the Introduction, regression is a main tool of econometrics, and in this chapter we consider very briefly the nature of this tool. 1.1 HISTORICAL ORIGIN OF THE TERM REGRESSION The term regression was introduced by Francis Galton. In a famous paper, Galton found that, although there was a tendency for tall parents to have tall children and for short parents to have short children, the average height of children born of parents of a given height tended to move or “regress” to- ward the average height in the population as a whole.1 In other words, the height of the children of unusually tall or unusually short parents tends to move toward the average height of the population. Galton’s law of universal regression was confirmed by his friend Karl Pearson, who collected more than a thousand records of heights of members of family groups.2 He found that the average height of sons of a group of tall fathers was less than their fathers’ height and the average height of sons of a group of short fathers was greater than their fathers’ height, thus “regressing” tall and short sons alike toward the average height of all men. In the words of Galton, this was “regression to mediocrity.” 1 Francis Galton, “Family Likeness in Stature,” Proceedings of Royal Society, London, vol. 40, 1886, pp. 42–72. 2 K. Pearson and A. Lee, “On the Laws of Inheritance,’’ Biometrika, vol. 2, Nov. 1903, pp. 357–462. 17  Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition 18 PART ONE: SINGLE-EQUATION REGRESSION MODELS 1.2 THE MODERN INTERPRETATION OF REGRESSION The modern interpretation of regression is, however, quite different. Broadly speaking, we may say Regression analysis is concerned with the study of the dependence of one vari- able, the dependent variable, on one or more other variables, the explanatory vari- ables, with a view to estimating and/or predicting the (population) mean or aver- age value of the former in terms of the known or fixed (in repeated sampling) values of the latter. The full import of this view of regression analysis will become clearer as we progress, but a few simple examples will make the basic concept quite clear. Examples 1. Reconsider Galton’s law of universal regression. Galton was inter- ested in finding out why there was a stability in the distribution of heights in a population. But in the modern view our concern is not with this expla- nation but rather with finding out how the average height of sons changes, given the fathers’ height. In other words, our concern is with predicting the average height of sons knowing the height of their fathers. To see how this can be done, consider Figure 1.1, which is a scatter diagram, or scatter- 75 × Mean value × × × × × × × × × 70 × × × × × × × × × × Son's height, inches × × × × × × × × × × × × × × × × × × × × 65 × × × × × × × × × × × × × × × × × × × × × × × × × × 60 × × × × 60 65 70 75 Father's height, inches FIGURE 1.1 Hypothetical distribution of sons’ heights corresponding to given heights of fathers. Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition CHAPTER ONE: THE NATURE OF REGRESSION ANALYSIS 19 gram. This figure shows the distribution of heights of sons in a hypothetical population corresponding to the given or fixed values of the father’s height. Notice that corresponding to any given height of a father is a range or dis- tribution of the heights of the sons. However, notice that despite the vari- ability of the height of sons for a given value of father’s height, the average height of sons generally increases as the height of the father increases. To show this clearly, the circled crosses in the figure indicate the average height of sons corresponding to a given height of the father. Connecting these averages, we obtain the line shown in the figure. This line, as we shall see, is known as the regression line. It shows how the average height of sons increases with the father’s height.3 2. Consider the scattergram in Figure 1.2, which gives the distribution in a hypothetical population of heights of boys measured at fixed ages. Corresponding to any given age, we have a range, or distribution, of heights. Obviously, not all boys of a given age are likely to have identical heights. But height on the average increases with age (of course, up to a certain age), which can be seen clearly if we draw a line (the regression line) through the 70 Mean value 60 Height, inches 50 40 10 11 12 13 14 Age, years FIGURE 1.2 Hypothetical distribution of heights corresponding to selected ages. 3 At this stage of the development of the subject matter, we shall call this regression line sim- ply the line connecting the mean, or average, value of the dependent variable (son’s height) corre- sponding to the given value of the explanatory variable (father’s height). Note that this line has a positive slope but the slope is less than 1, which is in conformity with Galton’s regression to mediocrity. (Why?)  Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition 20 PART ONE: SINGLE-EQUATION REGRESSION MODELS circled points that represent the average height at the given ages. Thus, knowing the age, we may be able to predict from the regression line the average height corresponding to that age. 3. Turning to economic examples, an economist may be interested in studying the dependence of personal consumption expenditure on after- tax or disposable real personal income. Such an analysis may be helpful in estimating the marginal propensity to consume (MPC), that is, average change in consumption expenditure for, say, a dollar’s worth of change in real income (see Figure I.3). 4. A monopolist who can fix the price or output (but not both) may want to find out the response of the demand for a product to changes in price. Such an experiment may enable the estimation of the price elasticity (i.e., price responsiveness) of the demand for the product and may help deter- mine the most profitable price. 5. A labor economist may want to study the rate of change of money wages in relation to the unemployment rate. The historical data are shown in the scattergram given in Figure 1.3. The curve in Figure 1.3 is an example of the celebrated Phillips curve relating changes in the money wages to the unemployment rate. Such a scattergram may enable the labor economist to predict the average change in money wages given a certain unemployment rate. Such knowledge may be helpful in stating something about the infla- tionary process in an economy, for increases in money wages are likely to be reflected in increased prices. + Rate of change of money wages 0 Unemployment rate, % – FIGURE 1.3 Hypothetical Phillips curve. Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition CHAPTER ONE: THE NATURE OF REGRESSION ANALYSIS 21 Money k= Income 0 π Inflation rate FIGURE 1.4 Money holding in relation to the inflation rate π. 6. From monetary economics it is known that, other things remaining the same, the higher the rate of inflation π, the lower the proportion k of their income that people would want to hold in the form of money, as de- picted in Figure 1.4. A quantitative analysis of this relationship will enable the monetary economist to predict the amount of money, as a proportion of their income, that people would want to hold at various rates of inflation. 7. The marketing director of a company may want to know how the de- mand for the company’s product is related to, say, advertising expenditure. Such a study will be of considerable help in finding out the elasticity of demand with respect to advertising expenditure, that is, the percent change in demand in response to, say, a 1 percent change in the advertising budget. This knowledge may be helpful in determining the “optimum” advertising budget. 8. Finally, an agronomist may be interested in studying the dependence of crop yield, say, of wheat, on temperature, rainfall, amount of sunshine, and fertilizer. Such a dependence analysis may enable the prediction or forecasting of the average crop yield, given information about the explana- tory variables. The reader can supply scores of such examples of the dependence of one variable on one or more other variables. The techniques of regression analy- sis discussed in this text are specially designed to study such dependence among variables.  Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition 22 PART ONE: SINGLE-EQUATION REGRESSION MODELS 1.3 STATISTICAL VERSUS DETERMINISTIC RELATIONSHIPS From the examples cited in Section 1.2, the reader will notice that in re- gression analysis we are concerned with what is known as the statistical, not functional or deterministic, dependence among variables, such as those of classical physics. In statistical relationships among variables we essentially deal with random or stochastic4 variables, that is, variables that have prob- ability distributions. In functional or deterministic dependency, on the other hand, we also deal with variables, but these variables are not random or stochastic. The dependence of crop yield on temperature, rainfall, sunshine, and fertilizer, for example, is statistical in nature in the sense that the explana- tory variables, although certainly important, will not enable the agronomist to predict crop yield exactly because of errors involved in measuring these variables as well as a host of other factors (variables) that collectively affect the yield but may be difficult to identify individually. Thus, there is bound to be some “intrinsic” or random variability in the dependent-variable crop yield that cannot be fully explained no matter how many explanatory vari- ables we consider. In deterministic phenomena, on the other hand, we deal with relationships of the type, say, exhibited by Newton’s law of gravity, which states: Every particle in the universe attracts every other particle with a force directly pro- portional to the product of their masses and inversely proportional to the square of the distance between them. Symbolically, F = k(m1 m2 /r 2 ), where F = force, m1 and m2 are the masses of the two particles, r = distance, and k = constant of proportionality. Another example is Ohm’s law, which states: For metallic conductors over a limited range of temperature the current C is proportional to the voltage V; that is, C = ( 1k )V where 1k is the constant of proportionality. Other examples of such deterministic relationships are Boyle’s gas law, Kirchhoff’s law of electricity, and Newton’s law of motion. In this text we are not concerned with such deterministic relationships. Of course, if there are errors of measurement, say, in the k of Newton’s law of gravity, the otherwise deterministic relationship becomes a statistical re- lationship. In this situation, force can be predicted only approximately from the given value of k (and m1 , m2 , and r), which contains errors. The variable F in this case becomes a random variable. 1.4 REGRESSION VERSUS CAUSATION Although regression analysis deals with the dependence of one variable on other variables, it does not necessarily imply causation. In the words of Kendall and Stuart, “A statistical relationship, however strong and however 4 The word stochastic comes from the Greek word stokhos meaning “a bull’s eye.” The out- come of throwing darts on a dart board is a stochastic process, that is, a process fraught with misses. Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edition CHAPTER ONE: THE NATURE OF REGRESSION ANALYSIS 23 suggestive, can never establish causal connection: our ideas of causation must come from outside statistics, ultimately from some theory or other.”5 In the crop-yield example cited previously, there is no statistical reason to assume that rainfall does not depend on crop yield. The fact that we treat crop yield as dependent on rainfall (among other things) is due to nonsta- tistical considerations: Common sense suggests that the relationship cannot be reversed, for we cannot control rainfall by varying crop yield. In all the examples cited in Section 1.2 the point to note is that a statisti- cal relationship in itself cannot logically imply causation. To ascribe causality, one must appeal to a priori or theoretical considerations. Thus, in the third example cited, one can invoke economic theory in saying that con- sumption expenditure depends on real income.6 1.5 REGRESSION VERSUS CORRELATION Closely related to but conceptually very much different from regression analysis is correlation analysis, where the primary objective is to measure the strength or degree of linear association between two variables. The cor- relation coefficient, which we shall study in detail in Chapter 3, measures this strength of (linear) association. For example, we may be interested in finding the correlation (coefficient) between smoking and lung cancer, between scores on statistics and mathematics examinations, between high school grades and college grades, and so on. In regression analysis, as al- ready noted, we are not primarily interested in such a measure. Instead, we try to estimate or predict the average value of one variable on the basis of the fixed values of other variables. Thus, we may want to know whether we can predict the average score on a statistics examination by knowing a student’s score on a mathematics examination. Regression and correlation have some fundamental differences that are worth mentioning. In regression analysis there is an asymmetry in the way the dependent and explanatory variables are treated. The dependent vari- able is assumed to be statistical, random, or stochastic, that is, to have a probability distribution. The explanatory variables, on the other hand, are assumed to have fixed values (in repeated sampling),7 which was made ex- plicit in the definition of regression given in Section 1.2. Thus, in Figure 1.2 we assumed that the variable age was fixed at given levels and height mea- surements were obtained at these levels. In correlation analysis, on the 5 M. G. Kendall and A. Stuart, The Advanced Theory of Statistics, Charles Griffin Publishers, New York, 1961, vol. 2, chap. 26, p. 279. 6 But as we shall see in Chap. 3, classical regression analysis is based on the assumption that the model used in the analysis is the correct model. Therefore, the direction of causality may be implicit in the model postulated. 7 It is crucial to note that the explanatory variables may be intrinsically stochastic, but for the purpose of regression analysis we assume that their values are fixed in repeated sampling (that is, X assumes the same values in various samples), thus rendering them in effect non- random or nonstochastic. But more on this in Chap. 3, Sec. 3.2.  Gujarati: Basic I. Single−Equation 1. The Nature of © The McGraw−Hill Econometrics, Fourth Regression Models Regression Analysis Companies, 2004 Edit