Giter Club home page Giter Club logo

macro-318's Introduction

Macroeconomics 318 Tutorials

Notebooks | Schedule | Exercises | Resources

These turorials provide an introduction to computational methods for macroeconomics.

Most of the material will also be made available via SUNLearn. However, for the most up to date version of the work you can refer here.

It is not required that you know how Git and Github works in order to work through the tutorials. I will give some basic instructions in the first tutorial, so those that want to delve more into version control are able to get started.

Below are my details. You can contact me via email.

Dawie van Lill
Email [email protected]
Office Schumann 511
GitHub DawievLill

Notebooks

You can view the notebooks in their HTML form by clicking on the link below.

For those of you who want to run the notebooks locally (on your own computer), find the installation instructions at section 1.3 of the QuantEcon site here

Schedule

This course schedule will be updated frequently, so please check before the tutorial for the video recording and notes. The links to the notebooks can also be found below. However, these are not interactive, so you can not run them unless you have Python, Julia and Jupyter installed on your computer. You will only be able to view their content. I will speak about this some more in the lecture.

Compulsory content: The slides contain all the compulsory content and are a shortened version of the full notebooks.

If you want a deeper understanding of the material you can access the notebooks for more in depth explanations. However, if you are only interested in completing the problem sets then the information in the slides will be enough.

If you are considering postgraduate studies in Economics I recommend that you read all the content in the notebooks. This will greatly help with your preparation.

There are also optional sections in the notebooks that are meant for students who have an interest in computational methods. These sections are generally a bit more difficult.

Tutorial Topic Longer notes Shorter notes (compulsory) Slides Problem Set
#1 Introduction to Julia [.ipynb / .html] [.ipynb / .html] .html .html
#2 Data, statistics and basic math [.ipynb / .html] [.ipynb / .html] .html .pdf
#3 Optimisation and the consumer problem [.ipynb / .html] [.ipynb / .html] .html .pdf
#4 The Solow-Swan model [.ipynb / .html] [.ipynb / .html]

Exercises

In this class we will be doing several tutorial exercises. Some of them will be done by hand and others on computer. The exercises for each week will be posted on this Github page as soon as possible. I will also post them on SUNLearn if that is easier to access for students.

Resources

For a good introduction to Julia I would recommend,

Good introductory notes for computational economics that use Python are the following

I have used these resources to compile these notes and tried to give the proper acknowledgment throughout.

Textbook: Williamson, S. Macroeconomics. 4th edition.

macro-318's People

Contributors

dawievlill avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.