A sample post with markdown.

Inline Highlighting

Sample of inline highlighting sum = parseInt(num1) + parseInt(num2)

Code Blocks

Some Javascript code

var num1, num2, sum
num1 = prompt('Enter first number')
num2 = prompt('Enter second number')
sum = parseInt(num1) + parseInt(num2) // "+" means "add"
alert('Sum = ' + sum) // "+" means combine into a string

Some Python code 🐍

def fib():
    a, b = 0, 1
    while True:            # First iteration:
        yield a            # yield 0 to start with and then
        a, b = b, a + b    # a will now be 1, and b will also be 1, (0 + 1)

for index, fibonacci_number in zip(range(10), fib()):
     print('{i:3}: {f:3}'.format(i=index, f=fibonacci_number))

Math Notation

Deriving the OLS Estimator

Using matrix notation, let $n$ denote the number of observations and $k$ denote the number of regressors.

The vector of outcome variables $\mathbf{Y}$ is a $n \times 1$ matrix,

\mathbf{Y} = \left[\begin{array}
  {c}
  y_1 \\
  . \\
  . \\
  . \\
  y_n
\end{array}\right]

\mathbf{Y} = \left[\begin{array} {c} y_1 \\ . \\ . \\ . \\ y_n \end{array}\right]

The matrix of regressors $\mathbf{X}$ is a $n \times k$ matrix (or each row is a $k \times 1$ vector),

\mathbf{X} = \left[\begin{array}
  {ccccc}
  x_{11} & . & . & . & x_{1k} \\
  . & . & . & . & .  \\
  . & . & . & . & .  \\
  . & . & . & . & .  \\
  x_{n1} & . & . & . & x_{nn}
\end{array}\right] =
\left[\begin{array}
  {c}
  \mathbf{x}'_1 \\
  . \\
  . \\
  . \\
  \mathbf{x}'_n
\end{array}\right]

\mathbf{X} = \left[\begin{array} {ccccc} x_{11} & . & . & . & x_{1k} \\ . & . & . & . & . \\ . & . & . & . & . \\ . & . & . & . & . \\ x_{n1} & . & . & . & x_{nn} \end{array}\right] = \left[\begin{array} {c} \mathbf{x}'_1 \\ . \\ . \\ . \\ \mathbf{x}'_n \end{array}\right]

The vector of error terms $\mathbf{U}$ is also a $n \times 1$ matrix.

At times it might be easier to use vector notation. For consistency, I will use the bold small x to denote a vector and capital letters to denote a matrix. Single observations are denoted by the subscript.

Least Squares

Start:
$y_i = \mathbf{x}'_i \beta + u_i$

Assumptions:

Linearity (given above)
$E(\mathbf{U}|\mathbf{X}) = 0$ (conditional independence)
rank( $\mathbf{X}$ ) = $k$ (no multi-collinearity i.e. full rank)
$Var(\mathbf{U}|\mathbf{X}) = \sigma^2 I_n$ (Homoskedascity)

Aim:
Find $\beta$ that minimises the sum of squared errors:

Q = \sum_{i=1}^{n}{u_i^2} = \sum_{i=1}^{n}{(y_i - \mathbf{x}'_i\beta)^2} = (Y-X\beta)'(Y-X\beta)

Solution:
Hints: $Q$ is a $1 \times 1$ scalar, by symmetry $\frac{\partial b'Ab}{\partial b} = 2Ab$ .

Take matrix derivative w.r.t $\beta$ :

\begin{aligned}
  \min Q           & = \min_{\beta} \mathbf{Y}'\mathbf{Y} - 2\beta'\mathbf{X}'\mathbf{Y} +
  \beta'\mathbf{X}'\mathbf{X}\beta \\
                   & = \min_{\beta} - 2\beta'\mathbf{X}'\mathbf{Y} + \beta'\mathbf{X}'\mathbf{X}\beta \\
  \text{[FOC]}~~~0 & =  - 2\mathbf{X}'\mathbf{Y} + 2\mathbf{X}'\mathbf{X}\hat{\beta}                  \\
  \hat{\beta}      & = (\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'\mathbf{Y}                              \\
                   & = (\sum^{n} \mathbf{x}_i \mathbf{x}'_i)^{-1} \sum^{n} \mathbf{x}_i y_i
\end{aligned}