**1 Introduction**

As our statistical models become more complicated, ex pressing them in **matrix
algebra** (also

known as linear algebra ) becomes more important. This lets us present our models
in a simple and

uncluttered format. All of the estimation of our statistical models, from OLS
through the most

complicated models we will study, is based on matrices. It will be in our best
interests to have a

clear understanding of how matrix algebra works .

A **matrix** is a rectangular array of numbers. We usually use bold-face
letters to indicate a

matrix. For example:

Each element in the matrix is subscripted to denote its
place. The row is the first number, and

the column is the second number. That is, elements in a matrix are listed as
.

A **vector** is an ordered set of numbers arranged in either a row or a column. A
row vector has

one row :

while a column vector has one column:

We usually denote a vector with a bold-face lower-case
letter.

A matrix can also be viewed as a set of column vectors or row vectors. The**
dimension** of a

matrix is the number of rows and columns it contains. We say “**A** is a n × k
matrix, where n is

the number of rows and k is the number of columns.

• If n = k, **A** is a ** square matrix **.

• If , then **A** is a **
symmetric matrix**. For example:

• A** diagonal matrix** is a square matrix whose only
non- zero elements are on the main diagonal

(from upper left to lower right ):

• A** triangular matrix** is a square matrix that has
only zeros either above or below the main

diagonal. If the zeros are above the diagonal, the matrix is** lower triangular**:

If the zeros are below the diagonal, the matrix is **
upper triangular**:

• An** identity matrix** is all zeros off the main
diagonal, and ones down the main diagonal. It

is usually denoted as** I**. For example, a 3 × 3 identity matrix is:

Here are several matrix ope rations and relationships you
should know.

• Equality of matrices. Two matrices ** A** and **B** are equal if and only if they have
the same

dimensions and each element of **A** equals the corresponding element of **B**. That is,

for all i and k.

• Transposition. The **transpose** of a matrix **A** is denoted **A’**, and is
obtained by creating the

matrix whose k^{th} row is the k^{th} column of the original matrix. Thus, if
**A** is n ×
k, **A’** will

be k × n. For example:

Note the transpose of a row vector is a column vector, and
vice-versa.

• Matrix addition. Matrices cannot be added together
unless they have the same dimension, in

which case they are said to be **conformable for addition**. To add two matrices we
simply

add the corresponding elements in each matrix. That is, A+B =
for all i
and k. Of

course, addition is commutative (A+B = B +A), and subtraction works in the same
way as

addition. Matrix addition is also associative, meaning (A + B) + C = A + (B +
C).

• Matrix multiplication. Matrices are multiplied by using the **inner product**
(also known as

the **dot product**). The inner product of two vectors is a** scalar** (a constant
number). We

take the dot product by multiplying each element in the row matrix by the
corresponding

element in the column matrix. For example:

Note that a'b = b'a.

In order to multiply two matrices, the number of columns in the first must be
equal to the

number of rows in the second. If this is true the matrices are said to be
**conformable for**

multiplication. If we multiply a n × T matrix with a T × k matrix, the result is
a n × k

matrix. For example

Multiplication of matrices is generally not commutative
(AB ≠ BA generally). Multiplying

a matrix by a scalar just multiplies every element in the matrix by that scalar.
That is,

for all i and k.

• Inverse Matrices. The** inverse** of a matrix **A** is denoted A^{-1}. A^{-1} is constructed
such that

A^{-1}A = I = AA^{-1}. These are often difficult to compute (but that’s what computers
are for).