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Econometrics TWO-VARIABLE REGRESSION ANALYSIS:
SOME BASIC IDEAS
Domodar N. Gujarati
HamidUllah
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A HYPOTHETICAL EXAMPLE• Regression analysis is largely concerned with
estimating and/or predicting the (population) mean value of the dependent variable on the basis of the known or fixed values of the explanatory variable(s).
• Look at table 2.1 which refers to a total population of 60 families and their weekly income (X) and weekly consumption expenditure (Y). The 60 families are divided into 10 income groups.
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Weekly family income and consumption
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• There is considerable variation in weekly consumption expenditure in each income group. But the general picture that one gets is that, despite the variability of weekly consumption expenditure within each income bracket, on the average, weekly consumption expenditure increases as income increases.
• The dark circled points in Figure 2.1 show the conditional mean values of Y against the various X values. If we join these conditional mean values, we obtain what is known as the population regression line (PRL), or more generally, the population regression curve. More simply, it is the regression of Y on X. The adjective “population” comes from the fact that we are dealing in this example with the entire population of 60 families. Of course, in reality a population may have many families.
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THE CONCEPT OF POPULATION REGRESSIONFUNCTION (PRF)
• From the preceding discussion and Figures. 2.1 and 2.2, it is clear that
each conditional mean E(Y | Xi) is a function of Xi. Symbolically,
E(Y | Xi) = f (Xi) (2.2.1)
Equation (2.2.1) is known as the conditional expectation function (CEF)
or population regression function (PRF) or population regression (PR)
for short.
• The functional form of the PRF is an empirical question. For example,
we may assume that the PRF E(Y | Xi) is a linear function of Xi, say, of
the type E(Y | Xi) = β1 + β2Xi (2.2.2)7
THE MEANING OF THE TERM LINEAR
• Linearity in the Variables• The first meaning of linearity is that the conditional expectation of Y is a linear
function of Xi, the regression curve in this case is a straight line. But
• E(Y | Xi) = β1 + β2X2i is not a linear function
• Linearity in the Parameters• The second interpretation of linearity is that the conditional expectation of Y,
E(Y | Xi), is a linear function of the parameters, the β’s; it may or may not be linear in the variable X.
• E(Y | Xi) = β1 + β2X2i
• is a linear (in the parameter) regression model. All the models shown in Figure 2.3 are thus linear regression models, that is, models linear in the parameters.
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• Now consider the model: • E(Y | Xi) = β1 + β222 Xi .• The preceding model is an example of a nonlinear (in the
parameter) regression model. • From now on the term “linear” regression will always
mean a regression that is linear in the parameters; the β’s (that is, the parameters are raised to the first power only).
•
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STOCHASTIC SPECIFICATION OF PRF• We can express the deviation of an individual Yi around
its expected value as follows:• ui = Yi − E(Y | Xi)• or• Yi = E(Y | Xi) + ui (2.4.1)• Technically, ui is known as the stochastic disturbance or
stochastic error term.• How do we interpret (2.4.1) The expenditure of an
individual family, given its income level, can be expressed as the sum of two components: – (1) E(Y | Xi), the mean consumption of all families with the
same level of income. This component is known as the systematic, or deterministic, component,
– (2) ui, which is the random, or nonsystematic, component.
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THE SIGNIFICANCE OF THE STOCHASTICDISTURBANCE TERM
• The disturbance term ui is a surrogate for all those variables that are omitted from the model but that collectively affect Y. Why don’t we introduce them into the model explicitly? The reasons are many:
• 1. Vagueness of theory: The theory, if any, determining the behavior of Y may be, and often is, incomplete. We might be ignorant or unsure about the other variables affecting Y.
• 2. Unavailability of data: Lack of quantitative information about these variables, e.g., information on family wealth generally is not available.
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• 3. Core variables versus peripheral variables: Assume that besides income X1, the number of children per family X2, sex X3, religion X4, education X5, and geographical region X6 also affect consumption expenditure. But the joint influence of all or some of these variables may be so small and it does not pay to introduce them into the model explicitly. One hopes that their combined effect can be treated as a random variable ui.
• 4. Intrinsic randomness in human behavior: Even if we succeed in introducing all the relevant variables into
the model, there is bound to be some “intrinsic” randomness in individual Y’s that cannot be explained no matter how hard we try. The disturbances, the u’s, may very well reflect this intrinsic randomness.
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• 5. Poor proxy variables: for example, Friedman regards permanent
consumption (Yp) as a function of permanent income (Xp). But since data on these variables are not directly observable, in practice we use proxy variables, such as current consumption (Y) and current income (X), there is the problem of errors of measurement, u may in this case then also represent the errors of measurement.
• 6. Principle of parsimony: we would like to keep our regression model as simple
as possible. If we can explain the behavior of Y “substantially” with two or three explanatory variables and if our theory is not strong enough to suggest what other variables might be included, why introduce more variables? Let ui represent all other variables.
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• 7. Wrong functional form: • Often we do not know the form of the functional
relationship between the regressend (dependent) and the repressors. Is consumption expenditure a linear (in variable) function of income or a nonlinear (invariable) function? If it is the former,
• Yi = β1 + B2Xi + ui is the proper functional relationship between Y and X, but if it is the latter,
• Yi = β1 + β2Xi + β3X2i + ui may be the correct functional
form. • In two-variable models the functional form of the
relationship can often be judged from the scattergram. But in a multiple regression model, it is not easy to determine the appropriate functional form, for graphically we cannot visualize scattergrams in multiple dimensions.
•
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THE METHOD OF ORDINARY LEAST SQUARES. (OLS)
• To understand this method, we first explain the least squares principle.
• Recall the two-variable PRF:Yi = β1 + β2Xi + ui (2.4.2)• The PRF is not directly observable. We estimate it from the
SRF:Yi = βˆ1 + βˆ2Xi +uˆi (2.6.2) = Yˆi +uˆi (2.6.3)• where Yˆi is the estimated (conditional mean) value of Yi .• But how is the SRF itself determined? First, express
(2.6.3) as uˆi = Yi − Yˆi
= Yi − βˆ1 − βˆ2Xi (3.1.1)16
• Now given n pairs of observations on Y and X, we would like to determine the SRF in such a manner that it is as close as possible to the actual Y. To this end, we may adopt the following criterion:
• Choose the SRF in such a way that the sum of the residuals ˆui = (Yi − Yˆi) is as small as possible.• But this is not a very good criterion. If we adopt the criterion
of minimizing ˆui , Figure 3.1 shows that the residuals ˆu2 and ˆu3 as well as the residuals ˆu1 and ˆu4 receive the same weight in the sum (ˆu1 + ˆu2 + ˆu3 + ˆu4).
• A consequence of this is that it is quite possible that the algebraic sum of the ˆui is small (even zero) although the ˆui are widely scattered about the SRF.
• To see this, let ˆu1, ˆu2, ˆu3, and ˆu4 in Figure 3.1 take the values of 10, −2, +2, and −10, respectively. The algebraic sum of these residuals is zero although ˆu1 and ˆu4 are scattered more widely around the SRF than ˆu2 and ˆu3.
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• We can avoid this problem if we adopt the least-squares criterion, which states that the SRF can be fixed in such a way that
ˆu2i = (Yi − Yˆi)2
= (Yi − βˆ1 − βˆ2Xi)2 (3.1.2)• is as small as possible, where ˆu2
i are the squared residuals.
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• By squaring ˆui , this method gives more weight to residuals such as ˆu1 and ˆu4 in Figure 3.1 than the residuals ˆu2 and ˆu3.
• It is obvious from (3.1.2) that: ˆu2
i = f (βˆ1, βˆ2) (3.1.3)• that is, the sum of the squared residuals is some
function of the estimators βˆ1 and βˆ2. To see this, consider Table 3.1 and conduct two experiments.
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Example
• Since the βˆ values in the two experiments are different, we get different values for the estimated residuals.
• Now which sets of βˆ values should we choose? Obviously the βˆ’s of the first experiment are the “best” values. But we can make endless experiments
• and then choosing that set of βˆ values that gives us the least possible value of ˆu2
i • But since time, and patience, are generally in short
supply, we need to consider some shortcuts to this trial-and-error process. Fortunately, the method of least squares provides us with unique estimates of β1 and β2 that give the smallest possible value of ˆu2
i.
• ˆu2i = (Yi − βˆ1 − βˆ2Xi)2 (3.1.2)
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• The process of differentiation yields the following equations for estimating β1 and β2:
Yi Xi = βˆ1Xi + βˆ2X2i (3.1.4)
Yi = nβˆ1 + βˆ2Xi
(3.1.5)• where n is the sample size. These simultaneous
equations are known as the normal equations. Solving the normal equations simultaneously, we obtain
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• where X¯ and Y¯ are the sample means of X and Y and where we define xi = (Xi − X¯ ) and yi = (Yi − Y¯). Henceforth we adopt the convention of letting the lowercase letters denote deviations from mean values.
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• We now consider the goodness of fit of the fitted regression line to a set of data; that is, we shall find out how “well” the sample regression line fits the data. The coefficient of determination r2 (two-variable case) or R2 (multiple regression) is a summary measure that tells how well the sample regression line fits the data.
• Consider a heuristic explanation of r2 in terms of a graphical device, known as the Venn diagram shown in Figure 3.9.
• In this figure the circle Y represents variation in the dependent variable Y and the circle X represents variation in the explanatory variable X. The overlap of the two circles indicates the extent to which the variation in Y is explained by the variation in X.
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THE COEFFICIENT OF DETERMINATION r 2:A MEASURE OF “GOODNESS OF FIT”
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THE COEFFICIENT OF DETERMINATION r 2:A MEASURE OF “GOODNESS OF FIT”
Derivation of R2• TSS= ESS+RSS (3.5.3)
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• The quantity r2 thus defined is known as the (sample) coefficient of
determination and is the most commonly used measure of the
goodness of fit of a regression line.• Verbally, r2 measures the proportion or percentage of the
total variation in Y explained by the regression model.
• Two properties of r2 may be noted:
• 1. It is a nonnegative quantity. • 2. Its limits are 0 ≤ r2 ≤ 1.
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Coefficient Correlation• In correlation analysis, the primary objective is to
measure the strength or degree of linear association between two variables. The coefficient, measures this strength of (linear) association.
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