INTRODUCTION
I.1 WHAT IS ECONOMETRICS?
Literally interpreted, econometrics means “economic measurement.” Although
measurement is an important part of econometrics, the scope of
econometrics is much broader, as can be seen from the following quotations:
Econometrics, the result of a certain outlook on the role of economics, consists of
the application of mathematical statistics to economic data to lend empirical support
to the models constructed by mathematical economics and to obtain
numerical results.1
. . . econometrics may be defined as the quantitative analysis of actual economic
phenomena based on the concurrent development of theory and observation, related
by appropriate methods of inference.2
Econometrics may be defined as the social science in which the tools of economic
theory, mathematics, and statistical inference are applied to the analysis of economic
phenomena.3
Econometrics is concerned with the empirical determination of economic
laws.4
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 economic
theory and actual measurements, using the theory and technique of statistical
inference as a bridge pier.
I.2 WHY A SEPARATE DISCIPLINE?
As the preceding definitions suggest, econometrics is an amalgam of economic
theory, mathematical economics, economic statistics, and mathematical
statistics. Yet the subject deserves to be studied in its own right for
the following reasons.
Economic theory makes statements or hypotheses that are mostly qualitative
in nature. For example, microeconomic theory states that, other
things remaining the same, a reduction in the price of a commodity is expected
to increase the quantity demanded of that commodity. Thus, economic
theory postulates a negative or inverse relationship between the price
and quantity demanded of a commodity. But the theory itself does not provide
any numerical measure of the relationship between the two; that is, it
does not tell by how much the quantity will go up or down as a result of a
certain change in the price of the commodity. It is the job of the econometrician
to provide such numerical estimates. Stated differently, econometrics
gives empirical content to most economic theory.
The main concern of mathematical economics is to express economic
theory in mathematical form (equations) without regard to measurability or
empirical verification of the theory. Econometrics, as noted previously, is
mainly interested in the empirical verification of economic theory. As we
shall see, the econometrician often uses the mathematical equations proposed
by the mathematical economist but puts these equations in such a
form that they lend themselves to empirical testing. And this conversion of
mathematical into econometric equations requires a great deal of ingenuity
and practical skill.
Economic statistics is mainly concerned with collecting, processing, and
presenting economic data in the form of charts and tables. These are thejobs of the economic statistician. It is he or she who is primarily responsible
for collecting data on gross national product (GNP), employment, unemployment,
prices, etc. The data thus collected constitute the raw data for
econometric work. But the economic statistician does not go any further,
not being concerned with using the collected data to test economic theories.
Of course, one who does that becomes an econometrician.
Although mathematical statistics provides many tools used in the trade,
the econometrician often needs special methods in view of the unique nature
of most economic data, namely, that the data are not generated as the
result of a controlled experiment. The econometrician, like the meteorologist,
generally depends on data that cannot be controlled directly. As Spanos
correctly observes:
In econometrics the modeler is often faced with observational as opposed to
experimental data. This has two important implications for empirical modeling
in econometrics. First, the modeler is required to master very different skills
than those needed for analyzing experimental data. . . . Second, the separation
of the data collector and the data analyst requires the modeler to familiarize
himself/herself thoroughly with the nature and structure of data in question.8
I.3 METHODOLOGY OF ECONOMETRICS
How do econometricians proceed in their analysis of an economic problem?
That is, what is their methodology? Although there are several schools of
thought on econometric methodology, we present here the traditional or
classical methodology, which still dominates empirical research in economics
and other social and behavioral sciences.9
Broadly speaking, traditional econometric methodology proceeds along
the following lines:
1. Statement of theory or hypothesis.
2. Specification of the mathematical model of the theory
3. Specification of the statistical, or econometric, model
4. Obtaining the data
5. Estimation of the parameters of the econometric model
6. Hypothesis testing
7. Forecasting or prediction
8. Using the model for control or policy purposes.
To illustrate the preceding steps, let us consider the well-known Keynesian
theory of consumption.
1. Statement of Theory or Hypothesis
Keynes stated:
The fundamental psychological law . . . is that men [women] are disposed, as a
rule and on average, to increase their consumption as their income increases, but
not as much as the increase in their income.10
In short, Keynes postulated that the marginal propensity to consume
(MPC), the rate of change of consumption for a unit (say, a dollar) change
in income, is greater than zero but less than 1.
2. Specification of the Mathematical Model of Consumption
Although Keynes postulated a positive relationship between consumption
and income, he did not specify the precise form of the functional relationship
between the two. For simplicity, a mathematical economist might suggest
the following form of the Keynesian consumption function:
Y = β1 + β2X 0 < β2 < 1 (I.3.1)
where Y = consumption expenditure and X = income, and where β1 and β2,
known as the parameters of the model, are, respectively, the intercept and
slope coefficients.
The slope coefficient β2 measures the MPC. Geometrically, Eq. (I.3.1) is as
shown in Figure I.1. This equation, which states that consumption is linearly related to income, is an example of a mathematical model of the relationship
between consumption and income that is called the consumption
function in economics. A model is simply a set of mathematical equations.
If the model has only one equation, as in the preceding example, it is called
a single-equation model, whereas if it has more than one equation, it is
known as a multiple-equation model (the latter will be considered later in
the book).
In Eq. (I.3.1) the variable appearing on the left side of the equality sign
is called the dependent variable and the variable(s) on the right side are
called the independent, or explanatory, variable(s). Thus, in the Keynesian
consumption function, Eq. (I.3.1), consumption (expenditure) is the dependent
variable and income is the explanatory variable.