collageWhen you ask a macro economist and a micro economist about the economy, you might get two different answers. Macroeconomists look at how trends and events on a national or global level affect the economy as a whole. Microeconomic analyses on the other hand focus on specific areas within economics such as consumer decision making, competition between businesses and industries, and pricing strategies of firms.
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You will also discover recent, related posts on python for economics pdf, python for economists online course, python for economics book, python programming for economics and finance, python economics projects, quantitative economics with python pdf, quantecon python package, python economics library on collegelearners.
The following topics also appear at Collegelearners.
Python Macroeconomics
Macro with Python is a set of introductory examples that apply Python to typical topics covered in an Intermediate (or advanced) macroeconomics course. The discussion assumes an intro/basic knowledge of Python and same familiarity with intermediate macroeconomic models.
The discussions in this project do not provide a full explanation of macroeconomic models nor intends to show coding best practices. The intention of the macro examples is to get started with how to use Python in macroeconomics. QuantEcon offers more advanced an detailed documentation.
The macro examples below are written in JupyterLab notebook and rendered with Jupyter’s nbviewer. Juyter notebooks are divided in cells, where each cell can be text or code. Since the purpose of this examples is pedagogical, each cell is written as a stand-alone piece of code (no need to execute the codes in previous cells).
Macroeconomic Models in Python
- The Labor Market
- The IS-LM Model
- The AD-AS Model
- The Solow Model
- The R&D Growth Model
- A Simple Ramsey Model
advanced quantitative economics
Advanced Lectures in Quantitative Economics summarizes some of the efforts of a second-phase program for first-rate candidates with a Master’s degree in economics who wish to continue with a doctoral degree in quantitative economics. This book is organized into three main topics—macroeconomics, microeconomics, and econometrics. This text specifically discusses the Neo-Keynesian macroeconomics in an open economy, international coordination of monetary policies under alternative exchange-rate regimes, and prospects for global trade imbalances. The post-war developments in labor economics, introduction to overlapping generation models, and measurement of expectations and direct tests of the REH are also elaborated. This monograph likewise covers the dynamic econometric modeling of decisions under uncertainty and fundamental bordered matrix of linear estimation.
Online Text and Notes in Advanced Econometrics and Quantitative Techniques
Phil Haynes, University of Brighton
Dynamic Pattern Synthesis is “a new mixed method that uses Cluster Analysis, Qualitative Comparative Analysis (QCA), and small-n time series data, to examine longitudinal change.” This site makes available an e-book about this technique, along with supporting data files, for educational purposes. To get the files, users have to enter a name and email address and say why they want access.
Published or updated: 2021
Licence: All Rights Reserved
Graduate econometrics lecture notes
Michael Creel, Universitat Autònoma de Barcelona
“Econometrics lecture notes with examples using the Julia language” The PDF includes more than 1,000 pages of lecture notes and the Julia code itself can be installed as a repository from GitHub
Published or updated: 2021
Licence: GPL / LGPL / MIT / Other free software licence
Quantitative Economics with Julia
Thomas J. Sargent, New York University; John Stachurski, Australian National University
A set of course materials that can be configured as undergraduate- or graduate-level, based around Jupyter notebooks. It discusses getting started with the Julia language, including setting up a Julia environment. It then works through many examples in economics. Each section of the material can be downloaded as a PDF using the buttons near the top of the text.
Published or updated: 202
0Licence: Creative Commons Attribution NoDerivatives (CC-BY-ND)
Quantitative Economics with Python
Thomas J. Sargent, New York University; John Stachurski, Australian National University
A set of course materials that can be configured as undergraduate- or graduate-level, based around Jupyter notebooks. It discusses setting up your own python programming environment, relevant software libraries and techniques, then works through many examples in economics. Each section of the material can be downloaded as a PDF using the buttons near the top of the text.
Published or updated: 2020
Licence: Creative Commons Attribution NoDerivatives (CC-BY-ND)
Methods of Empirical Research in Economics
Paul Schrimpf, University of British Columbia, Canada
Slides and lecture notes from a 2019 course. TeX code for all documents is available from a git repository.
Published or updated: 2019
Licence: Creative Commons Attribution ShareAlike (CC-BY-SA)
Econ 722 – Advanced Econometrics IV
Francis J. DiTraglia, University of Pennsylvania
Slides (as one large PDF), lecture notes (as one large PDF), problem sets and a couple of RStudio interactive apps from a course run in 2018.
Published or updated: 2018
Licence: Not known: assume All Rights Reserved
Statistical Method in Economics
Anna Mikusheva, MIT
Archived from a graduate-level course in Fall 2013, this Open CourseWare site has detailed notes from all 12 lectures, as well as past problem sets. The main course text is Casella and Berger, “Statistical Inference”.
Published or updated: 2013
Licence: Creative Commons Attribution NonCommercial ShareAlike (CC-BY-NC-SA)
Anna Mikusheva, MIT
Archived site for a graduate-level course that ran in Autumn 2013, with reading lists and detailed PDF notes from 26 lectures. The main text used is James D. Hamilton’s “Time Series Analysis” and the top-level sections of the course are: Stationary Time Series, Multivariate Stationary Analysis, Univariate Non-Stationary Processes, Multivariate Non-Stationary, GMM and Related Issues, Likelihood Methods, and Bayesian Methods.
Published or updated: 2013
Licence: Creative Commons Attribution NonCommercial ShareAlike (CC-BY-NC-SA)
Introduction to calculus for business and economics
Stephen J Silver, The Citadel (Military College of South Carolina)
This is a refresher PDF document summarising differentiation (including maxima and minima, partial differentiation and the Lagrangean multiplier) and integration with examples from economics. There are eight pages of content apart from the title page and an appendix summarising differentials and integrals of common functions.
Published or updated: 2012
Licence: Not known: assume All Rights Reserved
D. Stephen G. Pollock, University of Leicester
Materials from a 2011 course for postgraduates, with topics including Filtering economic data, Fourier analysis of time series data, and Spatial analysis of a stationary stochastic process.
Published or updated: 2011
Licence: Not known: assume All Rights Reserved
python for economics book
What do we mean when we talk about ‘big data’, and how can be become better critical consumers of it? Data scientist Vicki Boykis recommends the best books for learning Python—a language, she says, as versatile as a Swiss Army knife—and shows that it’s possible to teach yourself coding and data science.
Learn Python the Hard Way
by Zed A. Shaw
Coders at Work: Reflections on the Craft of Programming
by Peter Seibel
Big Data: Principles and Best Practices of Scalable Realtime Data Systems
Nathan Marz (with James Warren)
How To Lie With Statistics
by Darrell Huff
Computer Organization and Design MIPS Edition: The Hardware/Software Interface
by David A. Patterson & John L. Hennessy
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