《计量经济学》课程教学资源（国外经典教科书）Introductory Econometrics

JEFFREY M WOOLDRIDGE Introductory Econometrics A MO D RN A P PRO ACH 2 E

c h a pt rone The nature of econometrics and Economic Data Ca hapter l discusses the scope of econometrics and raises general issues that result from the application of econometric methods Section 1.3 examines the kinds of data sets that are used in business economics. and other social sciences. Section 1. 4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences 1.1 WHAT ISECONOMETRICSH Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program. Suppose this program teaches workers various ways to use computers in the manufacturing process. The twenty-week program offers courses during nonworking hours. Any hourly manufacturing worker may participate and enrollment in all or part of the program is voluntary. You are to determine what, if any, effect the training program has on each worker's subsequent hourly wage Now suppose you work for an investment bank. You are to study the returns on dif- ferent investment strategies involving short-term U.S. treasury bills to decide whether they comply with implied economic theories The task of answering such questions may seem daunting at first. At this point, you may only have a vague idea of the kind of data you would need to collect. By the end of this introductory econometrics course, you should know how to use econo- metric methods to formally evaluate a job training program or to test a simple eco- Econometrics is based upon the development of statistical methods for estimating economic relationships, testing economic theories, and evaluating and implementing government and business policy. The most common application of econometrics is the forecasting of such important macroeconomic variables as interest rates, inflation rates, and gross domestic product. While forecasts of economic indicators are highly visible and are often widely published, econometric methods can be used in economic areas hat have nothing to do with macroeconomic forecasting. For example, we will study the effects of political campaign expenditures on voting outcomes. We will consider the effect of school spending on student performance in the field of education. In addition, we will learn how to use econometric methods for forecasting economic time series

Chapter 1 discusses the scope of econometrics and raises general issues that result from the application of econometric methods. Section 1.3 examines the kinds of data sets that are used in business, economics, and other social sciences. Section 1.4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences. 1.1 WHAT IS ECONOMETRICS? Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program. Suppose this program teaches workers various ways to use computers in the manufacturing process. The twenty-week program offers courses during nonworking hours. Any hourly manufacturing worker may participate, and enrollment in all or part of the program is voluntary. You are to determine what, if any, effect the training program has on each worker’s subsequent hourly wage. Now suppose you work for an investment bank. You are to study the returns on dif￾ferent investment strategies involving short-term U.S. treasury bills to decide whether they comply with implied economic theories. The task of answering such questions may seem daunting at first. At this point, you may only have a vague idea of the kind of data you would need to collect. By the end of this introductory econometrics course, you should know how to use econo￾metric methods to formally evaluate a job training program or to test a simple eco￾nomic theory. Econometrics is based upon the development of statistical methods for estimating economic relationships, testing economic theories, and evaluating and implementing government and business policy. The most common application of econometrics is the forecasting of such important macroeconomic variables as interest rates, inflation rates, and gross domestic product. While forecasts of economic indicators are highly visible and are often widely published, econometric methods can be used in economic areas that have nothing to do with macroeconomic forecasting. For example, we will study the effects of political campaign expenditures on voting outcomes. We will consider the effect of school spending on student performance in the field of education. In addition, we will learn how to use econometric methods for forecasting economic time series. 1 Chapter One The Nature of Econometrics and Economic Data d 7/14/99 4:34 PM Page 1

The Nature of econometrics and Economic data Econometrics has evolved as a separate discipline from mathematical statistics because the former focuses on the problems inherent in collecting and analyzing nonex perimental economic data. Nonexperimental data are not accumulated through con- trolled experiments on individuals, firms, or segments of the economy (Nonexperimental data are sometimes called observational data to emphasize the fact that the researcher is a passive collector of the data. )Experimental data are often collected in laboratory environments in the natural sciences, but they are much more difficult to obtain in the social sciences. While some social experiments can be devised, it is often impossible prohibitively expensive, or morally repugnant to conduct the kinds of controlled experi- ments that would be needed to address economic issues. We give some specific exam- ples of the differences between experimental and nonexperimental data in Section 1.4 Naturally, econometricians have borrowed from mathematical statisticians when ever possible. The method of multiple regression analysis is the mainstay in both fields, but its focus and interpretation can differ markedly. In addition, economists hay devised new techniques to deal with the complexities of economic data and to test the 1.2 STEPS IN EMPIRICAL ECONOMIC ANALYSIS Econometric methods are relevant in virtually every branch of applied economics. They come into play either when we have an economic theory to test or when we have a rela- tionship in mind that has some importance for business decisions or policy analysis. An empirical analysis uses data to test a theory or to estimate a relationship. How does one go about structuring an empirical economic analysis? It may seer obvious, but it is worth emphasizing that the first step in any empirical analysis is the careful formulation of the question of interest. The question might deal with testing a certain aspect of an economic theory, or it might pertain to testing the effects of a gov ernment policy. In principle, econometric methods can be used to answer a wide range In some case olve the mal economic model is constructed. An economic model consists of mathematical building of models to describe a vast array of behaviors. For example, in intermediate microeconomics, individual consumption decisions, subject to a budget constraint, are described by mathematical models. The basic premise underlying these models is util- ity maximization. The assumption that individuals make choices to maximize their well ng, subject to resource constraints, gives us a very powerful framework for creating tractable economic models and making clear predictions. In the context of consumption decisions, utility maximization leads to a set of demand equations. In a demand equa tion, the quantity demanded of each commodity depends on the price of the goods, the price of substitute and complementary goods, the consumer's income, and the individ- Is characteristics that affect taste. These equations can form the basis of an econo- etric analysis of consumer demand. Economists have used basic economic tools, such as the utility maximization frame- work, to explain behaviors that at first glance may appear to be noneconomic in nature A classic example is Becker's(1968)economic model of criminal beha

Econometrics has evolved as a separate discipline from mathematical statistics because the former focuses on the problems inherent in collecting and analyzing nonex￾perimental economic data. Nonexperimental data are not accumulated through con￾trolled experiments on individuals, firms, or segments of the economy. (Nonexperimental data are sometimes called observational data to emphasize the fact that the researcher is a passive collector of the data.) Experimental data are often collected in laboratory environments in the natural sciences, but they are much more difficult to obtain in the social sciences. While some social experiments can be devised, it is often impossible, prohibitively expensive, or morally repugnant to conduct the kinds of controlled experi￾ments that would be needed to address economic issues. We give some specific exam￾ples of the differences between experimental and nonexperimental data in Section 1.4. Naturally, econometricians have borrowed from mathematical statisticians when￾ever possible. The method of multiple regression analysis is the mainstay in both fields, but its focus and interpretation can differ markedly. In addition, economists have devised new techniques to deal with the complexities of economic data and to test the predictions of economic theories. 1.2 STEPS IN EMPIRICAL ECONOMIC ANALYSIS Econometric methods are relevant in virtually every branch of applied economics. They come into play either when we have an economic theory to test or when we have a rela￾tionship in mind that has some importance for business decisions or policy analysis. An empirical analysis uses data to test a theory or to estimate a relationship. How does one go about structuring an empirical economic analysis? It may seem obvious, but it is worth emphasizing that the first step in any empirical analysis is the careful formulation of the question of interest. The question might deal with testing a certain aspect of an economic theory, or it might pertain to testing the effects of a gov￾ernment policy. In principle, econometric methods can be used to answer a wide range of questions. In some cases, especially those that involve the testing of economic theories, a for￾mal economic model is constructed. An economic model consists of mathematical equations that describe various relationships. Economists are well-known for their building of models to describe a vast array of behaviors. For example, in intermediate microeconomics, individual consumption decisions, subject to a budget constraint, are described by mathematical models. The basic premise underlying these models is util￾ity maximization. The assumption that individuals make choices to maximize their well￾being, subject to resource constraints, gives us a very powerful framework for creating tractable economic models and making clear predictions. In the context of consumption decisions, utility maximization leads to a set of demand equations. In a demand equa￾tion, the quantity demanded of each commodity depends on the price of the goods, the price of substitute and complementary goods, the consumer’s income, and the individ￾ual’s characteristics that affect taste. These equations can form the basis of an econo￾metric analysis of consumer demand. Economists have used basic economic tools, such as the utility maximization frame￾work, to explain behaviors that at first glance may appear to be noneconomic in nature. A classic example is Becker’s (1968) economic model of criminal behavior. Chapter 1 The Nature of Econometrics and Economic Data 2 14/99 4:34 PM Page 2

The Nature of econometrics and Economic data EXAMPLE1.1 (Economic Model of crime In a seminal article, Nobel prize winner Gary Becker postulated a utility maximization frame- work to describe an individuals participation in crime. Certain crimes have clear economic rewards, but most criminal behaviors have costs. The opportunity costs of crime prevent the criminal from participating in other activities such as legal employment. In addition, there are costs associated with the possibility of being caught and then, if convicted, the costs associated with incarceration. From Becker's perspective, the decision to undertake illegal activity is one of resource allocation, with the benefits and costs of competing activities aken into account Under general assumptions, we can derive an equation describing the amount of time spent in criminal activity as a function of various factors. We might represent such a fund =f(x1,x2x3,x4,xs,x6,x), y= hours spent in criminal activities x1="wage"for an hour spent in criminal activity in legal employ x3=income other than from crime or employment x4= probability of getting caught x6= expected sentence if convicted is representative of what might result from a formal economic analysis. As is common in economic theory, we have not been specific about the function f(- )in(1. 1). This function depends on an underlying utility function, which is rarely known. Nevertheless, we can use economic theory-or introspection -to predict the effect that each variable would have criminal activity. This is the basis for an econometric analysis of individual criminal activity Formal economic modeling is sometimes the starting point for empirical analysis, on to use economic theory less formally, or even to rely entirely intuition. You may agree that the determinants of criminal behavior appearing in equa tion(1. 1)are reasonable based on common sense; we might arrive at such an equation directly, without starting from utility maximization. This view has some merit, although there are cases where formal derivations provide insights that intuition can

EXAMPLE 1.1 (Economic Model of Crime) In a seminal article, Nobel prize winner Gary Becker postulated a utility maximization frame￾work to describe an individual’s participation in crime. Certain crimes have clear economic rewards, but most criminal behaviors have costs. The opportunity costs of crime prevent the criminal from participating in other activities such as legal employment. In addition, there are costs associated with the possibility of being caught and then, if convicted, the costs associated with incarceration. From Becker’s perspective, the decision to undertake illegal activity is one of resource allocation, with the benefits and costs of competing activities taken into account. Under general assumptions, we can derive an equation describing the amount of time spent in criminal activity as a function of various factors. We might represent such a func￾tion as y
f(x1,x2,x3,x4,x5,x6,x7), (1.1) where y
hours spent in criminal activities x1
“wage” for an hour spent in criminal activity x2
hourly wage in legal employment x3
income other than from crime or employment x4
probability of getting caught x5
probability of being convicted if caught x6
expected sentence if convicted x7
age Other factors generally affect a person’s decision to participate in crime, but the list above is representative of what might result from a formal economic analysis. As is common in economic theory, we have not been specific about the function f() in (1.1). This function depends on an underlying utility function, which is rarely known. Nevertheless, we can use economic theory—or introspection—to predict the effect that each variable would have on criminal activity. This is the basis for an econometric analysis of individual criminal activity. Formal economic modeling is sometimes the starting point for empirical analysis, but it is more common to use economic theory less formally, or even to rely entirely on intuition. You may agree that the determinants of criminal behavior appearing in equa￾tion (1.1) are reasonable based on common sense; we might arrive at such an equation directly, without starting from utility maximization. This view has some merit, although there are cases where formal derivations provide insights that intuition can overlook. Chapter 1 The Nature of Econometrics and Economic Data 3 d 7/14/99 4:34 PM Page 3

The Nature of Econometrics and Economic data Here is an example of an equation that was derived through reasoning. E M PLE.2 (Job Training and worker productivity) Consider the problem posed at the beginning of Section 1. 1. a labor economist would like to examine the effects of job training on worker productivity. In this case, there is little need for formal economic theory. Basic economic understanding is sufficient for realizing that factors such as education, experience, and training affect worker productivity. Also, econ omits are well aware that workers are paid commensurate with their productivity This sim- le reasoning leads to a model such as where wage is hourly wage, educ is years of formal education, exper is years of workforce experience, and training is weeks spent in job training. Again, other factors generally affect he wage rate, but (1. 2) captures the essence of the problem After we specify an economic model, we need to turn it into what we call an econo- metric model. Since we will deal with econometric models throughout this text, it is important to know how an econometric model relates to an economic model. Take equa- tion(1. 1)as an example. The form of the function f( )must be specified before we car ndertake an econometric analysis. A second issue concerning(1. 1)is how to deal with variables that cannot reasonably be observed. For example, consider the wage that a person can earn in criminal activity. In principle, such a quantity is well-defined, but it yould be difficult if not impossible to observe this wage for a given individual. Even ariables such as the probability of being arrested cannot realistically be obtained for iven individual. but at least we can observe relevant arrest statistics and derive a var- able that approximates the probability of arrest. Many other factors affect criminal behavior that we cannot even list let alone observe but we must somehow account for them The ambiguities inherent in the economic model of crime are resolved by specify g a particular econometric model: crime=Bo+ B, wage B2othinc B3freqarr Bafreqcomv 13) Bsavgsen bage +l where crime is some measure of the frequency of criminal activity, wage is the wage at can be earned in legal employment, othinc is the income from other sources(assets inheritance, etc. freqarr is the frequency of arrests for prior infractions(to approxi- mate the probability of arrest), fregcomm is the frequency of conviction, and avgsen is the average sentence length after conviction. The choice of these variables is deter mined by th theory as well as data considerations. The term u contains unob-

Here is an example of an equation that was derived through somewhat informal reasoning. EXAMPLE 1.2 (Job Training and Worker Productivity) Consider the problem posed at the beginning of Section 1.1. A labor economist would like to examine the effects of job training on worker productivity. In this case, there is little need for formal economic theory. Basic economic understanding is sufficient for realizing that factors such as education, experience, and training affect worker productivity. Also, econ￾omists are well aware that workers are paid commensurate with their productivity. This sim￾ple reasoning leads to a model such as wage
f(educ,exper,training) (1.2) where wage is hourly wage, educ is years of formal education, exper is years of workforce experience, and training is weeks spent in job training. Again, other factors generally affect the wage rate, but (1.2) captures the essence of the problem. After we specify an economic model, we need to turn it into what we call an econo￾metric model. Since we will deal with econometric models throughout this text, it is important to know how an econometric model relates to an economic model. Take equa￾tion (1.1) as an example. The form of the function f() must be specified before we can undertake an econometric analysis. A second issue concerning (1.1) is how to deal with variables that cannot reasonably be observed. For example, consider the wage that a person can earn in criminal activity. In principle, such a quantity is well-defined, but it would be difficult if not impossible to observe this wage for a given individual. Even variables such as the probability of being arrested cannot realistically be obtained for a given individual, but at least we can observe relevant arrest statistics and derive a vari￾able that approximates the probability of arrest. Many other factors affect criminal behavior that we cannot even list, let alone observe, but we must somehow account for them. The ambiguities inherent in the economic model of crime are resolved by specify￾ing a particular econometric model: crime
0 + 1wagem + 2othinc  3 freqarr  4 freqconv  5avgsen  6age  u, (1.3) where crime is some measure of the frequency of criminal activity, wagem is the wage that can be earned in legal employment, othinc is the income from other sources (assets, inheritance, etc.), freqarr is the frequency of arrests for prior infractions (to approxi￾mate the probability of arrest), freqconv is the frequency of conviction, and avgsen is the average sentence length after conviction. The choice of these variables is deter￾mined by the economic theory as well as data considerations. The term u contains unob￾Chapter 1 The Nature of Econometrics and Economic Data 4 14/99 4:34 PM Page 4

The Nature of econometrics and Economic data served factors, such as the wage for criminal activity, moral character, family back ground, and errors in measuring things like criminal activity and the probability of arrest. We could add family background variables to the model, such as number of sib- lings, parents'education, and so on, but we can never eliminate u entirely. In fact, deal- ng with this error term or disturbance term is perhaps the most important component of any econometric analysis. The constants Bo, Bl,..., B are the parameters of the econometric model, and they describe the directions and strengths of the relationship between crime and the factors used to determine crime in the model A complete econometric model for Example 1.2 might be wage=B.+ B,educ B2exper Straining u, 14 here the term u contains factors such as"innate ability, "quality of educ famil background, and the myriad other factors that can influence a persons wage. If we are specifically concerned about the effects of job training, then B, is the parameter of For the most part, econometric analysis begins by specifying an econometric model without consideration of the details of the models creation. We generally follow this approach, largely because careful derivation of something like the economic model of crime is time consuming and can take us into some specialized and often difficult areas of economic theory. Economic reasoning will play a role in our examples, and we will merge any underlying economic theory into the econometric model specification. In the economic model of crime example, we would start with an econometric model such as (1.3)and use economic reasoning and common sense as guides for choosing the vari ables. While this approach loses some of the richness of economic analysis, it is com- monly and effectively applied by careful researchers nce an econometric model such as(1.3)or (1. 4)has been specified, various hypotheses of interest can be stated in terms of the unknown parameters. For example, in equation(1.3)we might hypothesize that wage, the wage that can be earned in legal employment, has no effect on criminal behavior. In the context of this particular econo- metric model, the hypothesis is equivalent to P1=0. An empirical analysis, by definition, requires data. After data on the relevant vari ables have been collected, econometric methods are used to estimate the parameters in he econometric model and to formally test hypotheses of interest. In some cases, the econometric model is used to make predictions in either the testing of a theory or the study of a policys impact. Because data collection is so important in empirical work, Section 1.3 will describe he kinds of data that we are likely to encounter 13 THE STRUCTURE OF ECONOMIC DATA Economic data sets come in a variety of types. While some econometric methods can be applied with little or no modification to many different kinds of data sets, the spe- cial features of some data sets must be accounted for or should be exploited. We next describe the most important data structures encountered in applied work

served factors, such as the wage for criminal activity, moral character, family back￾ground, and errors in measuring things like criminal activity and the probability of arrest. We could add family background variables to the model, such as number of sib￾lings, parents’ education, and so on, but we can never eliminate u entirely. In fact, deal￾ing with this error term or disturbance term is perhaps the most important component of any econometric analysis. The constants 0, 1, …, 6 are the parameters of the econometric model, and they describe the directions and strengths of the relationship between crime and the factors used to determine crime in the model. A complete econometric model for Example 1.2 might be wage
0  1educ  2exper  3training  u, (1.4) where the term u contains factors such as “innate ability,” quality of education, family background, and the myriad other factors that can influence a person’s wage. If we are specifically concerned about the effects of job training, then 3 is the parameter of interest. For the most part, econometric analysis begins by specifying an econometric model, without consideration of the details of the model’s creation. We generally follow this approach, largely because careful derivation of something like the economic model of crime is time consuming and can take us into some specialized and often difficult areas of economic theory. Economic reasoning will play a role in our examples, and we will merge any underlying economic theory into the econometric model specification. In the economic model of crime example, we would start with an econometric model such as (1.3) and use economic reasoning and common sense as guides for choosing the vari￾ables. While this approach loses some of the richness of economic analysis, it is com￾monly and effectively applied by careful researchers. Once an econometric model such as (1.3) or (1.4) has been specified, various hypotheses of interest can be stated in terms of the unknown parameters. For example, in equation (1.3) we might hypothesize that wagem, the wage that can be earned in legal employment, has no effect on criminal behavior. In the context of this particular econo￾metric model, the hypothesis is equivalent to 1
0. An empirical analysis, by definition, requires data. After data on the relevant vari￾ables have been collected, econometric methods are used to estimate the parameters in the econometric model and to formally test hypotheses of interest. In some cases, the econometric model is used to make predictions in either the testing of a theory or the study of a policy’s impact. Because data collection is so important in empirical work, Section 1.3 will describe the kinds of data that we are likely to encounter. 1.3 THE STRUCTURE OF ECONOMIC DATA Economic data sets come in a variety of types. While some econometric methods can be applied with little or no modification to many different kinds of data sets, the spe￾cial features of some data sets must be accounted for or should be exploited. We next describe the most important data structures encountered in applied work. Chapter 1 The Nature of Econometrics and Economic Data 5 d 7/14/99 4:34 PM Page 5

The Nature of econometrics and Economic data Cross-Sectional data A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes the data on all units do not correspond to precisely the same time period. For example several families may be surveyed during different weeks within a year. In a pure cross section analysis we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population. For exam- ple, if we obtain information on wages, education, experience, and other characteristics by randomly drawing 500 people from the working population, then we have a random sample from the population of all working people. Random sampling is the sampling scheme covered in introductory statistics courses, and it simplifies the analysis of cross sectional data. A review of random sampling is contained in Appendix C. Sometimes random sampling is not appropriate as an assumption for analyzing cross-sectional data. For example, suppose we are interested in studying factors that influence the accumulation of family wealth. We could survey a random sample of fam- ilies, but some families might refuse to report their wealth. If, for example, wealthier families are less likely to disclose their wealth, then the resulting sample on wealth is ot a random sample from the population of all families. This is an illustration of a sam- ple selection problem, an advanced topic that we will discuss in Chapter 17 Another violation of random sampling occurs when we sample from units that ar large relative to the population, particularly geographical units. The potential problem in such cases is that the population is not large enough to reasonably assume the obser vations are independent draws. For example, if we want to explain new business activ- ity across states as a function of wage rates, energy prices, corporate and property tax rates, services provided, quality of the workforce, and other state characteristics, it is unlikely that business activities in states near one another are independent. It turns that the econometric methods that we discuss do work in such situations, but they some- times need to be refined. For the most part, we will ignore the intricacies that arise in analyzing such situations and treat these problems in a random sampling framework, even when it is not technically correct to do so. Cross-sectional data are widely used in economics and other social sciences. In eco- omics, the analysis of cross-sectional data is closely aligned with the applied micro- economics fields, such as labor economics, state and local public finance, industrial organization, urban economics, demography, and health economics. Data on individu- als, households, firms, and cities at a given point in time are important for testing micro- economic hypotheses and evaluating economic policies The cross-sectional data used for econometric analysis can be represented and stored in computers. Table 1. 1 contains, in abbreviated form, a cross-sectional data set on 526 working individuals for the year 1976.(This is a subset of the data in the file WAGEl.RAW)The variables include wage(in dollars per hour), educ(years of educa- tion), exper (years of potential labor force experience), female(an indicator for gender), and married(marital status). These last two variables are binary(zero-one)in nature

Cross-Sectional Data A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time. Sometimes the data on all units do not correspond to precisely the same time period. For example, several families may be surveyed during different weeks within a year. In a pure cross section analysis we would ignore any minor timing differences in collecting the data. If a set of families was surveyed during different weeks of the same year, we would still view this as a cross-sectional data set. An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population. For exam￾ple, if we obtain information on wages, education, experience, and other characteristics by randomly drawing 500 people from the working population, then we have a random sample from the population of all working people. Random sampling is the sampling scheme covered in introductory statistics courses, and it simplifies the analysis of cross￾sectional data. A review of random sampling is contained in Appendix C. Sometimes random sampling is not appropriate as an assumption for analyzing cross-sectional data. For example, suppose we are interested in studying factors that influence the accumulation of family wealth. We could survey a random sample of fam￾ilies, but some families might refuse to report their wealth. If, for example, wealthier families are less likely to disclose their wealth, then the resulting sample on wealth is not a random sample from the population of all families. This is an illustration of a sam￾ple selection problem, an advanced topic that we will discuss in Chapter 17. Another violation of random sampling occurs when we sample from units that are large relative to the population, particularly geographical units. The potential problem in such cases is that the population is not large enough to reasonably assume the obser￾vations are independent draws. For example, if we want to explain new business activ￾ity across states as a function of wage rates, energy prices, corporate and property tax rates, services provided, quality of the workforce, and other state characteristics, it is unlikely that business activities in states near one another are independent. It turns out that the econometric methods that we discuss do work in such situations, but they some￾times need to be refined. For the most part, we will ignore the intricacies that arise in analyzing such situations and treat these problems in a random sampling framework, even when it is not technically correct to do so. Cross-sectional data are widely used in economics and other social sciences. In eco￾nomics, the analysis of cross-sectional data is closely aligned with the applied micro￾economics fields, such as labor economics, state and local public finance, industrial organization, urban economics, demography, and health economics. Data on individu￾als, households, firms, and cities at a given point in time are important for testing micro￾economic hypotheses and evaluating economic policies. The cross-sectional data used for econometric analysis can be represented and stored in computers. Table 1.1 contains, in abbreviated form, a cross-sectional data set on 526 working individuals for the year 1976. (This is a subset of the data in the file WAGE1.RAW.) The variables include wage (in dollars per hour), educ (years of educa￾tion), exper (years of potential labor force experience), female (an indicator for gender), and married (marital status). These last two variables are binary (zero-one) in nature Chapter 1 The Nature of Econometrics and Economic Data 6 14/99 4:34 PM Page 6

The Nature of econometrics and Economic data Ttable 11 A Cross-Sectional Data Set on Wages and Other Individual Characteristics obsno educ exper emale married 3.10 2 0 2 3.24 12 1 5.30 12 525 11.56 526 50 14 and serve to indicate qualitative features of the individual. (The person is female or not; the person is married or not. We will have much to say about binary variables in Chapter 7 and beyond The variable obsno in Table 1. I is the observation number assigned to each person in the sample. Unlike the other variables, it is not a characteristic of the individual. All econometrics and statistics software packages assign an observation number to each data unit. Intuition should tell you that, for data such as that in Table 1. 1, it does not matter which person is labeled as observation one, which person is called Observation Two, and so on. The fact that the ordering of the data does not matter for econometric nalysis is a key feature of cross-sectional data sets obtained from random sampling Different variables sometimes correspond to different time periods in cross- sectional data sets. For example, in order to determine the effects of government poli cies on long-term economic growth, economists have studied the relationship between growth in real per capita gross domestic product(GDP)or ain period(say 19 to 1985)and variables determined in part by government policy in 1960(government consumption as a percentage of GDP and adult secondary education rates). Such a data set might be represented as in Table 1. 2, which constitutes part of the data set used in the study of cross-country growth rates by De Long and Summers(1991)

and serve to indicate qualitative features of the individual. (The person is female or not; the person is married or not.) We will have much to say about binary variables in Chapter 7 and beyond. The variable obsno in Table 1.1 is the observation number assigned to each person in the sample. Unlike the other variables, it is not a characteristic of the individual. All econometrics and statistics software packages assign an observation number to each data unit. Intuition should tell you that, for data such as that in Table 1.1, it does not matter which person is labeled as observation one, which person is called Observation Two, and so on. The fact that the ordering of the data does not matter for econometric analysis is a key feature of cross-sectional data sets obtained from random sampling. Different variables sometimes correspond to different time periods in cross￾sectional data sets. For example, in order to determine the effects of government poli￾cies on long-term economic growth, economists have studied the relationship between growth in real per capita gross domestic product (GDP) over a certain period (say 1960 to 1985) and variables determined in part by government policy in 1960 (government consumption as a percentage of GDP and adult secondary education rates). Such a data set might be represented as in Table 1.2, which constitutes part of the data set used in the study of cross-country growth rates by De Long and Summers (1991). Chapter 1 The Nature of Econometrics and Economic Data 7 Table 1.1 A Cross-Sectional Data Set on Wages and Other Individual Characteristics obsno wage educ exper female married 1 3.10 11 2 1 0 2 3.24 12 22 1 1 3 3.00 11 2 0 0 4 6.00 8 44 0 1 5 5.30 12 7 0 1 525 11.56 16 5 0 1 526 3.50 14 5 1 0 d 7/14/99 4:34 PM Page 7

The Nature of econometrics and Economic data ttable 1.2 A Data Set on Economic Growth Rates and Country Characteristics obsno country gpcrgap ovcon seconds Argentina 0.89 32 Austria 3.32 16 3 2.56 Bolivia 1.24 Zimbabwe 2.30 The variable gpcrgdp represents average growth in real per capita GDP over the period 1960 to 1985. The fact that govcons60(government consumption as a percentage of GDP) and second60(percent of adult population with a secondary education) corre spond to the year 1960, while gpcrgdp is the average growth over the period from 1960 to 1985, does not lead to any special problems in treating this information as a cross- sectional data set. The order of the observations is listed alphabetically by country, but there is nothing about this ordering that affects any subsequent analysis Time series data a time series data set consists of observations on a variable or several variables over time. Examples of time series data include stock prices, money supply, consumer price index, gross domestic product, annual homicide rates, and automobile sales figure Because past events can influence future events and lags in behavior are prevalent in the social sciences, time is an important dimension in a time series data set. Unlike the arrangement of cross-sectional data, the chronological ordering of observations in a time series conveys potentially important information a key feature of time series data that makes it more difficult to analyze than cross- sectional data is the fact that economic observations can rarely, if ever, be assumed to be independent across time. Most economic and other time series are related, often strongly related, to their recent histories. For example, knowing something about the ross domestic product from last quarter tells us quite a bit about the likely range GDP during this quarter, since GDP tends to remain fairly stable from one quarter to

The variable gpcrgdp represents average growth in real per capita GDP over the period 1960 to 1985. The fact that govcons60 (government consumption as a percentage of GDP) and second60 (percent of adult population with a secondary education) corre￾spond to the year 1960, while gpcrgdp is the average growth over the period from 1960 to 1985, does not lead to any special problems in treating this information as a cross￾sectional data set. The order of the observations is listed alphabetically by country, but there is nothing about this ordering that affects any subsequent analysis. Time Series Data A time series data set consists of observations on a variable or several variables over time. Examples of time series data include stock prices, money supply, consumer price index, gross domestic product, annual homicide rates, and automobile sales figures. Because past events can influence future events and lags in behavior are prevalent in the social sciences, time is an important dimension in a time series data set. Unlike the arrangement of cross-sectional data, the chronological ordering of observations in a time series conveys potentially important information. A key feature of time series data that makes it more difficult to analyze than cross￾sectional data is the fact that economic observations can rarely, if ever, be assumed to be independent across time. Most economic and other time series are related, often strongly related, to their recent histories. For example, knowing something about the gross domestic product from last quarter tells us quite a bit about the likely range of the GDP during this quarter, since GDP tends to remain fairly stable from one quarter to Chapter 1 The Nature of Econometrics and Economic Data 8 Table 1.2 A Data Set on Economic Growth Rates and Country Characteristics obsno country gpcrgdp govcons60 second60 1 Argentina 0.89 9 32 2 Austria 3.32 16 50 3 Belgium 2.56 13 69 4 Bolivia 1.24 18 12 61 Zimbabwe 2.30 17 6 14/99 4:34 PM Page 8

The Nature of Econometrics and Economic data the next. While most econometric procedures can be used with both cross-sectional and time series data, more needs to be done in specifying econometric models for time series data before standard econometric methods can be justified. In addition, modifi- cations and embellishments to standard econometric techniques have been developed account for and exploit the dependent nature of economic time series and to address other issues, such as the fact that some economic variables tend to display clear trends over time Another feature of time series data that can require special attention is the data fre quency at which the data are collected In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually. Stock prices are recorded at daily intervals(excluding Saturday and Sunday). The money supply in the U.S. economy reported weekly. Many macroeconomic series are tabulated monthly, including infla tion and employment rates. Other macro series are recorded less frequently, such as every three months(every quarter). Gross domestic product is an important example of a quarterly series. Other time series, such as infant mortality rates for states in the United States, are available only on an annual basis Many weekly, monthly, and quarterly economic time series display a strong seasonal pattern, which can be an important factor in a time series analysis. For ex- ample, monthly data on housing starts differs across the months simply due to changin eather conditions. We will learn how to deal with seasonal time series in Chapter 10 Table 1.3 ins a time series data set obtained from an article by Castillo- Freeman and Freeman(1992) on minimum wage effects in Puerto Rico. The earliest year in the data set is the first observation, and the most recent year available is the last Ttable 1.3 Minimum Wage, Unemployment, and Related Data for Puerto Rico obsno year agcol unemp 1950 0.20 20.1 154 878.7 5 1952 0.23 22.6 4.8 0159 1986 281.6 1987 3.35 58.2 16.8 4496.7

the next. While most econometric procedures can be used with both cross-sectional and time series data, more needs to be done in specifying econometric models for time series data before standard econometric methods can be justified. In addition, modifi￾cations and embellishments to standard econometric techniques have been developed to account for and exploit the dependent nature of economic time series and to address other issues, such as the fact that some economic variables tend to display clear trends over time. Another feature of time series data that can require special attention is the data fre￾quency at which the data are collected. In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually. Stock prices are recorded at daily intervals (excluding Saturday and Sunday). The money supply in the U.S. economy is reported weekly. Many macroeconomic series are tabulated monthly, including infla￾tion and employment rates. Other macro series are recorded less frequently, such as every three months (every quarter). Gross domestic product is an important example of a quarterly series. Other time series, such as infant mortality rates for states in the United States, are available only on an annual basis. Many weekly, monthly, and quarterly economic time series display a strong seasonal pattern, which can be an important factor in a time series analysis. For ex￾ample, monthly data on housing starts differs across the months simply due to changing weather conditions. We will learn how to deal with seasonal time series in Chapter 10. Table 1.3 contains a time series data set obtained from an article by Castillo￾Freeman and Freeman (1992) on minimum wage effects in Puerto Rico. The earliest year in the data set is the first observation, and the most recent year available is the last Chapter 1 The Nature of Econometrics and Economic Data 9 Table 1.3 Minimum Wage, Unemployment, and Related Data for Puerto Rico obsno year avgmin avgcov unemp gnp 1 1950 0.20 20.1 15.4 878.7 2 1951 0.21 20.7 16.0 925.0 3 1952 0.23 22.6 14.8 1015.9 37 1986 3.35 58.1 18.9 4281.6 38 1987 3.35 58.2 16.8 4496.7 d 7/14/99 4:34 PM Page 9