2.8 SUMMARY AND CONCLUSION ( Damodar N. Gujarati )

1. The key concept underlying regression analysis is the concept of the conditional expectation function (CEF), or population regression function (PRF). Our objective in regression analysis is to find out how the average value of the dependent variable ( or regressand) varies with the given value of the explanatory variable ( or regressor )

2. This book largely deals with linear PRFs, that is, regressions that are linear in they parameters. The may or may not be linear in the regressand or regressors.

3. For  empirical purposes, its the stochastic PRF that matters. The stochastic disturbance term ui plays a critical role in estimating the PRF.

4.The PRF is an idealized concept, since in practice one rarely has access to the entire population of interest. Usually, one has a sample of observation from the population .Therefore, one uses the stochastic sample regression function (SRF) to estimate the PRF. How this actually accomplished is discussed in Chapter3.

EXERCISES

Questions

2.1 What Is the conditional expectation function or they population regression function?

2.2 What is difference between the population and sample regression functions?.Is this a distinction without difference?

2.3 What  is the role of the stochastic error term ui in regresstion analysis? What in the difference between the stochastic error term and the residual ûi ?

2.4 Why do we need regression analysis ? Why not simply use the mean value of the regressand as its value?

2.5 What do we mean by a linear regression model?
2.6 Determine whether the the following models are linear in the parameters, or the variables, or both. Which of these models are linear regression model?

 2.8 What is meant by an intrinsically linear regression model? If β2 in exercise 2.7d were 0.8, would be a linear or nonlinear or non linear regression model?
*2.9 Consider the following nonstochastic model (i.e.,m models without the stochastic error term). Are they linear regression models? if no, is it posible, by suitable algebraic manipulation, to convert them into lineart modela?

 2.10 You are give the scattergram in Figure2.7 along with the regression line. What general conclusion do you draw from this diagram ? Is the regression line sketched in the diagram a population regression line or the sample regression line?

 FIGURE 2.8      Skill intensity of exports and human capital endowment. Data are for 126 industrial and developing countries in 1985. Values along the horizontal axis are logarithms of ratio of the country's average educational attainment to is land area: vertical axis value are logarithms of tario of manufacured to primary-products exports.

Source:world Bank,World Development Report 1995,P.59. Original sources: Export data from United Nation Statistical Office COMTRADE data base; education data from UNDP 1990;land data from the world bank. 

2.11.  From the scattergram given Figure 2.8, what general conclusions do you draw? What is the economic theory that underlies this scattergram? (Hint: Look up any international economics textbook and read up on the Heckscher-Ohlin model of trade) 

2.12. What does the scattergram Figure 2.9 reveal? On the basis of this diagram,Would you argue that minimum wage laws are good for economic well-being? 

2.13  Is the regression line shown in Figure I.3  Of the Introduction the PRF or SRF ? Why? How would you interpret the scatterpoint around the re  the regression line? Besides GDP, what other factors, or variables, might determine personal consumtion expenditure?
2.14 You are given the data  in Table 2.7 for the United States for years 1980-1996.

 

a. Plot the male civilian labor force participation rate against male civilian unemployment rate. Eyeball a regression line through the scatterpoints. A priori, waht is the expected relationship between the two and what is the underlying economic theory? Does the scattergram support the theory.

b.Repeat part a for females. 


c.Now Plot both the male and female labor participation rates against average hourly earnings ( in 1982 dollars). (you may use separate diagrams.) Now what do you find? And  how would you rationalize your finding?

d. Can you plot the labor force participation rate against the unemployment rate  and the average hourly earnings simultaneously? If not, how would you verbalize the relationship among the three variables?

2.7 AN ILLUSTRATIVE EXAMPLE (Damodar N. Gujarati)

We conclude this chapter with an example. Table 2.6 give data on the level of education ( measured by the number of years of schooling, the mean hourly wages earned by people at each level of education , and the number of the people at the stated level of education. Ernst Berndt originallly obtained the data presented in the table, and he derived these data from the current population survey conducted in May 1985.14 Wewil exsplore these data ( whith additional explanatory variables) in a later chapster.

Plotting the 9conditional) mean wage against education, we obtain the picture in Figure 2.6. Regression curve in the figure  shows how mean wages vary with the level of education; they generally increase  with the level of education, a finding one should not find surprising. We will study in a later chapter how variables besides education can also affect the mean wage.

2.6 THE SAMPLE REGRESSION FUNCTION ( SRF) (Danamor N. Gujarati)

By confining our discussion so far to the population of Y values corresponding to the fixed X's, we have deliberately avoided sampling considerations (note that the data of  Table 2.1 represent the population , not a sample). But it is  about time to face up to the sampling problems, for in most practical situations what we have is but a sample of Y values corresponding to some fixed X's. Therefore.our task now is to estimate the PRF on the basis of the sample information.

As an illustration, pretend that population of Table2.4 was not know ti us and the only information we had was a randomly selected sample of Y values for the fixed X's as given in Table 2.4. Unlike Table 2.1, we now have only one Y value corresponding to the given X's; each ( given Xi ) in Table 2.4 is chosen randomly from similar Y's corresponding to the same X from the population of Table 2.1.

The Question is: From the sample of Table 2.4 can we predict the average weekly consumption expenditure Y in the population as a whole corresponding to the chosen X's?. In other words, can we estimate the PRF from the sample data? As the reader surely suspects, we may not be ale to estimate the PRF "accurately" because of sampling fluctuations. To see this suppose we draw another random sample from the population of Table 2.1. as presented in Table 2.5.

Plotting the data of Tables 2.4 and 2.5 we obtain the scattegram given in Figure 2.4. In the scattergram two sample regression lines are drawn so as to 'fit' the scatter reasonably well: SRF1 is based on the first sample, and SRF2 is based on the second sample.

Which of the two regression lines represent s the "true" population regression line? If we avoid the temptation of looking at figure2.1, which purportedly represents the PR, there is no way we can be absolutely  sure that either of the regression lines shown in Figure 2.4represent the true population regression line (or curve). The regression lines in Figure 2.4 are known as the sample regression lines.Supposedly the present the population regression line, but because of sampling fluctuations they are not best an approximation of the true PR.. In general, we would get N different SRFs for N different samples, and these SRFs are not likely to be the same.

Now, analogously to the PRF that underlies the population  regression line, we can develop the concept of the sample regression function (SRF) to represent the sample regression line .The sample counterpart of (2.2.2) may be written as

FIGURE 2.5 Sample and population regression lines

the PRF based on the SRF is at best an approximate one. This approximation is shown diagrammatically in Figure2.5

For X = Xi, we have one (sample) observation Y = Y i  . In terms of the SRF, the observed Y i can be expressed as.

Y i  = ^Y i + ^ui                              (2.6.3)

an in term of the PRF, it can be expressed as

Y i  = E(Y | Xi) + ui                         (2.6.4)

 

Now obviously in Figure 2.5 ^Y i overestimates the true E( Y | Xi ) for the Xi shown therein. Be the same token, for any Xi to the left of  the poin A,the SRF will underestimate the true PRF. But the reader can readily see that such over-and underestimateis inevitable because of sampling fluctuations.

 

The critical question now: Granted that the SRF is but an approximation on the PRF, can we devises a rule or a method that will make this approximation as "close"as Possible? in other words, how should the SRTF be constructed so ^B1 is as "close" as possible to the true B1 and ^B2 is as "close" as possible to the true B2 even through we will never know the true B1 and B2 ?

The answer to this question will occupy much of our attention in Chapter 3. We note  here that we can develop procedures that tell us now to construct the SRF to mirror the PRF as faithfully as possible. it is fascinating to consider that this can be done even though we never actually determine the PRF itself.

2.5 THE SIGNIFICANCE OF THE STOCHASTIC ( Damodar N. Gujarati )

DISTURBANCE TERM

As noted in Section 2.4, the disturbance term ui is a surrogate for all those variable that are omitted from the model but that collectively affect Y. The obvious question  is: Why not introduce these variables into  the model explicitly? Stated otherwise, why not develop a multiple regression model whit as many variables as possible? The reason are many.

1. Vagueness of theory : The theory, if any, determining the behavior of Y may be, and often is, incomplete. We might know for certain that weekly income X influence weekly consumption expenditure Y, but we might be ignorant or unsure about the other variables affecting Y. Therefore, ui may be used as asubstitute for all the excluded or omitted variables from the model.

2. Unavailability of data: Even if we know what some of the excluded variables are and therefore consider a multiple regression rather than a simple regression, we may not have quantitative information about these variables. It is a common experiences in empirical analysis that the data we would ideally like to have often are not available. For Example, in principle we could introduce family wealth as an explanatory variable in addition to the income variable to explain family consumption expenditure. But unfortunately, information on family wealth generally is not available. therefore, we may be forced to omit the wealth variable from our model despite its great theoretical relevance in explaining consumption expenditure.

3. Core variables versus peripheral variables: Assume in our consumption income example  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 it is quite possible that the joint influence of all or some of these variables may be so small and best non systematic or random that as a practical matter and for cost considerations 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 .10

4. Intrinsic randomness in human behavior: Even if we succeed in introducing all the relevant variables in to 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.

5. Poor proxy variables: Although the classical regression model ( to be developed in Chapter.3 ) assumes that the variables Y and X are measured accurately, in practice the data may be plagued by errors of measurement. Consider, for example Milton Friedman's well-known theory of the consumption function.11

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10.A further difficulty is that variables such as sex, education , and religion are difficult to quantify.
11Milton Friedman, A Theory of the Consumption Function, Princeton University Press, Princeton, N.J., 1957
12"That descriptions be kept as simple as possible until proved inadequate"The World of mathematics, vol.2,J.R. Newman  ( ed.), Simon & Schuster, New York, 1956,p.1247,or"Entities should no be multiplied beyond necessity,"Donald F. Morrison, Applied linear Statistical Method, Prentice Hall, Englewood Cliffs,N.J.,1983, p.58.