142 lines
4.1 KiB
Plaintext
142 lines
4.1 KiB
Plaintext
---
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title: Deriving the OLS Estimator
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date: '2019-11-16'
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tags: ['next js', 'math', 'ols']
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draft: false
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summary: 'How to derive the OLS Estimator with matrix notation and a tour of math typesetting using markdown with the help of KaTeX.'
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---
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# Introduction
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Parsing and display of math equations is included in this blog template. Parsing of math is enabled by `remark-math` and `rehype-katex`.
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KaTeX and its associated font is included in `_document.js` so feel free to use it in any pages.
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^[For the full list of supported TeX functions, check out the [KaTeX documentation](https://katex.org/docs/supported.html)]
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Inline math symbols can be included by enclosing the term between the `$` symbol.
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Math code blocks is denoted by `$$`.
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The dollar signal displays without issue since only text without space and between two `$` signs are considered as math symbols.[^2]
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Inline or manually enumerated footnotes are also supported. Click on the links above to see them in action.
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[^2]: Here's $10 and $20.
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# Deriving the OLS Estimator
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Using matrix notation, let $n$ denote the number of observations and $k$ denote the number of regressors.
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The vector of outcome variables $\mathbf{Y}$ is a $n \times 1$ matrix,
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```tex
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\mathbf{Y} = \left[\begin{array}
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{c}
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y_1 \\
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. \\
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. \\
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. \\
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y_n
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\end{array}\right]
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```
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$$
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\mathbf{Y} = \left[\begin{array}
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{c}
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y_1 \\
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. \\
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. \\
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. \\
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y_n
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\end{array}\right]
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$$
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The matrix of regressors $\mathbf{X}$ is a $n \times k$ matrix (or each row is a $k \times 1$ vector),
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```latex
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\mathbf{X} = \left[\begin{array}
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{ccccc}
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x_{11} & . & . & . & x_{1k} \\
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. & . & . & . & . \\
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. & . & . & . & . \\
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. & . & . & . & . \\
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x_{n1} & . & . & . & x_{nn}
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\end{array}\right] =
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\left[\begin{array}
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{c}
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\mathbf{x}'_1 \\
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. \\
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. \\
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. \\
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\mathbf{x}'_n
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\end{array}\right]
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```
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$$
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\mathbf{X} = \left[\begin{array}
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{ccccc}
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x_{11} & . & . & . & x_{1k} \\
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. & . & . & . & . \\
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. & . & . & . & . \\
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. & . & . & . & . \\
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x_{n1} & . & . & . & x_{nn}
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\end{array}\right] =
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\left[\begin{array}
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{c}
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\mathbf{x}'_1 \\
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. \\
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. \\
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. \\
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\mathbf{x}'_n
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\end{array}\right]
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$$
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The vector of error terms $\mathbf{U}$ is also a $n \times 1$ matrix.
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At times it might be easier to use vector notation. For consistency I will use the bold small x to denote a vector and capital letters to denote a matrix. Single observations are denoted by the subscript.
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## Least Squares
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**Start**:
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$$y_i = \mathbf{x}'_i \beta + u_i$$
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**Assumptions**:
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1. Linearity (given above)
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2. $E(\mathbf{U}|\mathbf{X}) = 0$ (conditional independence)
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3. rank($\mathbf{X}$) = $k$ (no multi-collinearity i.e. full rank)
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4. $Var(\mathbf{U}|\mathbf{X}) = \sigma^2 I_n$ (Homoskedascity)
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**Aim**:
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Find $\beta$ that minimises sum of squared errors:
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$$
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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)
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$$
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**Solution**:
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Hints: $Q$ is a $1 \times 1$ scalar, by symmetry $\frac{\partial b'Ab}{\partial b} = 2Ab$.
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Take matrix derivative w.r.t $\beta$:
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```tex
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\begin{aligned}
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\min Q & = \min_{\beta} \mathbf{Y}'\mathbf{Y} - 2\beta'\mathbf{X}'\mathbf{Y} +
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\beta'\mathbf{X}'\mathbf{X}\beta \\
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& = \min_{\beta} - 2\beta'\mathbf{X}'\mathbf{Y} + \beta'\mathbf{X}'\mathbf{X}\beta \\
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\text{[FOC]}~~~0 & = - 2\mathbf{X}'\mathbf{Y} + 2\mathbf{X}'\mathbf{X}\hat{\beta} \\
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\hat{\beta} & = (\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'\mathbf{Y} \\
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& = (\sum^{n} \mathbf{x}_i \mathbf{x}'_i)^{-1} \sum^{n} \mathbf{x}_i y_i
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\end{aligned}
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```
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$$
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\begin{aligned}
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\min Q & = \min_{\beta} \mathbf{Y}'\mathbf{Y} - 2\beta'\mathbf{X}'\mathbf{Y} +
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\beta'\mathbf{X}'\mathbf{X}\beta \\
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& = \min_{\beta} - 2\beta'\mathbf{X}'\mathbf{Y} + \beta'\mathbf{X}'\mathbf{X}\beta \\
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\text{[FOC]}~~~0 & = - 2\mathbf{X}'\mathbf{Y} + 2\mathbf{X}'\mathbf{X}\hat{\beta} \\
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\hat{\beta} & = (\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'\mathbf{Y} \\
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& = (\sum^{n} \mathbf{x}_i \mathbf{x}'_i)^{-1} \sum^{n} \mathbf{x}_i y_i
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\end{aligned}
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$$
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