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The Classical Linear Regression Model (CLRM). The CLRM makes the following assumptions: A-1: The regression model is linear in the parameters as in.
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The CLRM makes the following assumptions:
A-1 : The regression model is linear in the parameters as in
it may or may not be linear in the variables Y and the X s.
A-2 : The regressors are assumed to be fixed or nonstochastic in the sense that their values are fixed in repeated sampling. This assumption may not be appropriate for all economic data, but as we will show later, if X and u are independently distributed the results based on the classical assumption discussed below hold true provided our analysis is conditional on the particular X values drawn in the sample. However, if X and u are uncorrelated , the classical results hold true asymptotically (i.e.in large samples.)^1
A-3 : Given the values of the X variables, the expected, or mean, value of the error term is zero. That is,^2
๐ธ๐ธ(๐ข๐ข (^) ๐๐ |X) = 0 (1.8)
where, for brevity of expression, X (the bold X ) stands for all X variables in the model. In words, the conditional expectation of the error term, given the values of the X variables, is zero. Since the error term represents the influence of factors that may be essentially random, it makes sense to assume that their mean or average value is zero. As a result of this critical assumption, we can write (1.2) as:
๐ธ๐ธ(๐๐๐๐ |X) = ๐ฉ๐ฉ๐ฉ๐ฉ + ๐ธ๐ธ(๐ข๐ข (^) ๐๐|X) = ๐ฉ๐ฉ๐ฉ๐ฉ (1.9)
which can be interpreted as the model for mean or average value of ๐๐๐๐ conditional on the X values. This is the population (mean) regression function ( PRF ) mentioned earlier. In regression analysis our main objective is to estimate this function. If there is only one X variable, you can visualize it as the (population) regression line. If there is more than one X variable, you will have to imagine it to be a curve in a multi-dimensional graph. The estimated PRF, the sample counterpart of Eq. (1.9), is denoted by ๐๐๏ฟฝ๐๐ = ๐๐๐๐. That is, ๐๐๏ฟฝ๐๐ = ๐๐๐๐ is an estimator of ๐ธ๐ธ(๐๐๐๐|๐๐).
(^1) Note that independence implies no correlation, but no correlation does not necessarily imply
independence. (^2) The vertical bar after ๐ข๐ข๐๐ is to remind that the analysis is conditional on the given values of X.
A-4 : The variance of each ๐ข๐ข (^) ๐๐ given the values of X , is constant, or homoscedastic ( homo means equal and scedastic means variance). That is,
๐ฃ๐ฃ๐ฃ๐ฃ๐ฃ๐ฃ(๐ข๐ข (^) ๐๐|๐๐) = ฯ^2 (1.10)
Note : There is no subscript on ฯ^2.
A-5 : There is no correlation between two error terms. That is, there is no autocorrelation. Symbolically,
Cov (๐ข๐ข (^) ๐๐ , ๐ข๐ข๐๐ | X ) =0 i โ j (1.11)
where Cov stands for covariance and i and j are two different error terms. Of course, if I = j , Eq. (1.11) will give the variance of ๐ข๐ข (^) ๐๐ given in Eq. (1.10).
A-6 : There are no perfect linear relationships among the X variables. This is the assumption of no multicollinearity. For example, relationships like ๐๐ 5 = 2๐๐ 3 + 4 ๐๐ 4 are ruled out.
A-7 : The regression model is correctly specified. Alternatively, there is no specification bias or specification error in the model used in empirical analysis. It is implicitly assumed that the number of observations, n , is greater than the number of parameters estimated. Although it is not a part of the CLRM, it is assumed that the error term follows the normal distribution with zero mean and (constant) variance ๐๐ 2. Symbolically,
A-8 : ๐ข๐ข (^) ๐๐ ~ N (0, ๐๐ 2 ) (1.12)
On the basis of Assumptions A-1 to A-7, it can be shown that the method of ordinary least squares ( OLS ), the method most popularly used in practice, provides estimators of the parameters of the PRF that have several desirable statistical properties , such as:
1- The estimators are linear, that is, they are linear functions of the dependent variable Y****. Linear estimators are easy to understand and deal with compared to nonlinear estimators.
2- The estimators are unbiased, that is, in repeated applications of the method, on average, the estimators are equal to their true values.
3- In the class of linear unbiased estimators, OLS estimators have minimum variance. As a result, the true parameter values can be estimated with least possible uncertainty; an unbiased estimator with the least variance is called an efficient estimator.
In short, under the assumed conditions, OLS estimators are BLUE : best linear unbiased estimators. This is the essence of the well-known GaussโMarkov theorem , which provides a theoretical justification for the method of least squares.