---
# System prepended metadata

title: 經濟計量與數值分析方法
tags: [Numerical Methods]

---

# 經濟計量與數值分析方法
{%hackmd @themes/orangeheart %}

<style>
.likecoin-button {
  position: relative;
  width: 100%;
  max-width: 485px;
  max-height: 240px;
  margin: 0 auto;
}
.likecoin-button > div {
  padding-top: 49.48454%;
}
.likecoin-button > iframe {
  position: absolute;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
}
</style>

###### tags: `Numerical Methods`

The course introduces basic numerical methods often used in econometrics, quantitative social science, and data science. Topics include random sampling, numerical integration, numerical differentiation, optimization, simulation, and maximum likelihood estimations. The theories are introduced at an accessible level, and the focus is on the application of the methods. 

Equally important in this course is introducing students to computer programming using `Julia` (primary), `R`, or `Matlab`. Students are asked to code functions to implement the numerical methods, and hands-on exercises are given to hone coding skills. Comprehension of the numerical methods and development of programming skills are mutually reinforcing and complementary to one another. [^1]

## Julia 與數據分析

`Julia`是一個由麻省理工學院(MIT)的資料科學家與資訊科學家開發出的程式語言。根據官網的說明：

> Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM.

可以看出，`Julia`的優點就是運算速度快，具有如`Matlab`一般的高速運算能力、像`Python`一樣通用、`R`$ 一樣的得心應手。在大數據的時代，對於許多資料科學的研究者與使用者而言，無疑是一大福音。因此，`Julia`與數據分析 這個專欄就是為了分享這個好用的語言而生。

<h3><a href='https://hackmd.io/@yueswater/Julia_Basic' target="_self">Julia 基礎教學</a></h3>

<h3><a href='https://hackmd.io/@yueswater/Julia_Plot' target="_self">在 Julia 中實現數據可視化</a></h3>

<h3><a href='https://hackmd.io/@yueswater/interactive_plot' target="_self">用 Julia 製作互動式圖形</a></h3>



## 筆記區

<h3><a href='https://hackmd.io/@yueswater/LinearAlgebra' target="_self">線性代數(Linear Algebra)</a></h3>

<h3><a href='https://hackmd.io/@yueswater/GibbsSampling' target="_self">吉布斯採樣(Gibbs Sampling)</a></h3>

<h3><a href='https://hackmd.io/@yueswater/numerical_integration' target="_self">數值積分</a></h3>



[^1]:節錄自[經濟計量數值方法導論課程大綱](https://nol.ntu.edu.tw/nol/coursesearch/print_table.php?course_id=323%20U1070&class=&dpt_code=3230&ser_no=36581&semester=110-2&lang=CH)。

<div class="likecoin-embed likecoin-button">
  <div></div>
  <iframe scrolling="no" frameborder="0" src="https://button.like.co/in/embed/xiaolong70701/button?referrer=hackmd.io"></iframe>
</div>