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Quantitative Economics with Python
Quantitative Economics with Python ¶. Quantitative Economics with Python. This website presents a set of lectures on quantitative economic modeling, designed and written by Thomas J. Sargent and John Stachurski. For an overview of the series, see this page.
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Quantitative Economics with Python – QuantEcon
Quantitative Economics with Python. This project provides a series of online textbooks on Python programming and quantitative economic modeling, designed and written by Thomas J. Sargent and John Stachurski. Read more... Python Programming for Economics and Finance. Quantitative Economics with Python. Advanced Quantitative Economics with Python.
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QuantEcon
QuantEcon is a NumFOCUS fiscally sponsored project dedicated to development and documentation of modern open source computational tools for economics, econometrics, and decision making. We welcome contributions and collaboration from the economics community and other partner organizations.
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About Python Lectures – QuantEcon
The lecture series treats. algorithms and numerical methods for quantitative economic problems, related mathematical and statistical concepts, and. basics of coding skills and software engineering. As stated above, the coding language for this lecture series is Python. Note that there’s also a related set of Julia lectures.
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Advanced Quantitative Economics with Python
Advanced Quantitative Economics with Python ¶. Advanced Quantitative Economics with Python. This website presents a set of advanced lectures on quantitative economic modeling, designed and written by Thomas J. Sargent and John …
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Contribute to QuantEcon.py – QuantEcon
The current structure of QuantEcon.py is relatively flat. The majority of methods are available at the top level namespace for easy access. The Python API is defined by the init.py files within the quantecon package. The QuantEcon.py API. As QuantEcon.py evolves, the structure will naturally move towards having more sub-packages. Every effort ...
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python.quantecon.org
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Quantitative Economics with Python — Quantitative Economics …
Quantitative Economics with Python — Quantitative Economics with Python
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26. Robustness - Advanced Quantitative Economics with Python
26.1. Overview ¶. This lecture modifies a Bellman equation to express a decision-maker’s doubts about transition dynamics. His specification doubts make the decision-maker want a robust decision rule.. Robust means insensitive to misspecification of transition dynamics.. The decision-maker has a single approximating model.. He calls it approximating to acknowledge that he …
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Basics — QuantEcon DataScience
Objects and Types¶. Everything in Python is an object.. Objects are “things” that contain 1) data and 2) functions that can operate on the data. Sometimes we refer to the functions inside an object as methods.. We can investigate what data is inside an object and which methods it supports by typing . after that particular variable, then hitting TAB.. It should then list data and …
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MATLAB–Python–Julia cheatsheet — Cheatsheets by QuantEcon …
MATLAB–Python–Julia cheatsheet. Dependencies and Setup. Creating Vectors. Creating Matrices. Manipulating Vectors and Matrices. Accessing Vector/Matrix Elements. Mathematical Operations. Sum / max / min. Programming.
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GitHub - QuantEcon/QuantEcon.py: A community based Python …
Before installing quantecon we recommend you install the Anaconda Python distribution, which includes a full suite of scientific python tools. Note: quantecon is …
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Getting Started — QuantEcon DataScience
Welcome¶. Welcome to the start of your path to learning how to work with data in the Python programming language! A programming language is, loosely speaking, a structured subset of natural language (words) and special characters (e.g. , or {) that allow humans to describe operations they would like their computer to perform on their behalf. The programming …
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QuantEcon · GitHub
Notebooks for https://python.quantecon.org. Jupyter Notebook 191 120 0 0 Updated Jul 22, 2022. lecture-python.myst Public Quantitative Economics with Python Jupyter Notebook 27 16 37 6 Updated Jul 22, 2022. Expectations.jl Public Expectation operators for Distributions.jl objects Julia 49 MIT 16 5 1 Updated Jul 19, 2022. QuantEcon.notebooks Public Jupyter notebooks …
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GitHub - JAT005/JQuantEcon.py: A community based Python …
QuantEcon.py is supported financially by the Alfred P. Sloan Foundation and is part of the QuantEcon organization. Downloading the quantecon Repository. An alternative is to download the sourcecode of the quantecon package and install it manually from the github repository. For example, if you have git installed type
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Two Modifications of Mean-Variance Portfolio Theory
36.4. Black-Litterman Starting Point ¶. When estimates of \(\mu\) and \(\Sigma\) from historical sample means and covariances have been combined with plausible values of the risk-aversion parameter \(\delta\) to compute an optimal portfolio from formula , a typical outcome has been \(w\) ’s with extreme long and short positions.. A common reaction to these outcomes is that …
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QuantEcon · GitHub
Python library to support the QuantEcon Data Science lecture series. Python 3 4 3 (1 issue needs help) 1 Updated Jul 6, 2021. lecture-python-advanced Public Source files for https://python-advanced.quantecon.org. Python 37 BSD-3-Clause 21 15 0 Updated Jun 22, 2021. MatchingMarkets.py Public Python toolbox for simulation of matching markets in …
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QuantEcon documentation — QuantEcon 0.5.3 documentation
QuantEcon documentation ¶. QuantEcon documentation. The quantecon python library consists of a number of modules which includes game theory (game_theory), markov chains (markov), random generation utilities (random), a collection of tools (tools), and other utilities (util) which are mainly used by developers internal to the package. Game theory.
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GitHub - QuantEcon/lecture-python: Source files for …
Source files for https://python.quantecon.org. Contribute to QuantEcon/lecture-python development by creating an account on GitHub.
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Python cheatsheet — Cheatsheets by QuantEcon documentation
Description. plt.plot(x, y, s =None) The plot command is included in matplotlib.pyplot . The plot command is used to plot x versus y where x and y are iterables of the same length. By default the plot command draws a line, using the s argument you can specify type of line and color. For example ‘-‘, ‘- -‘, ‘:’, ‘o’, ‘x’, and ...
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