2011 | OriginalPaper | Chapter
The General Linear Model: The Basics
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Consider the following regression equation
7.1
$$y = X\beta + u$$
where
$$y = \left[\begin{array}{c}Y_1\\ Y_2\\ \atop^{.}_{.}\\ Y_n\end{array} \right]; X = \left[ \begin{array}{cccc}X_{11} & X_{12} & \ldots & X_{1k}\\ X_{21} & X_{22} & \ldots & X_{2k}\\ \atop^{.}_{.} & \atop^{.}_{.} & \atop^{.}_{.} & \atop^{.}_{.}\\ X_{n1} & X_{n2} & \ldots & X_{nk} \end{array} \right]; \beta = \left[ \begin{array}{c}\beta_1\\ \beta_2\\ \atop^{.}_{.}\\ \beta_k \end{array} \right]; u = \left[\begin{array}{c}u_1\\ u_2\\ \atop^{.}_{.}\\ u_n \end{array}\right]$$
with
n
denoting the number of observations and
k
the number of variables in the regression, with
n
>
k
. In this case,
y
is a column vector of dimension (
n
×1) and
X
is a matrix of dimension (
n
×
k
). Each column of
X
denotes a variable and each row of
X
denotes an observation on these variables. If
y
is log(wage) as in the empirical example in Chapter 4, see Table 4.1 then the columns of
X
contain a column of ones for the constant (usually the first column), weeks worked, years of full time experience, years of education, sex, race, marital status, etc.