Quantitative Economics with Python by Thomas J. Sargent (.PDF)

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File Size: 11.9 MB

Quantitative Economics with Python by Thomas J. Sargent, John Stachurski
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Overview: This book presents a set of lectures on Python programming for economics and finance. The lecture describes important ideas in economics that use the mathematics of geometric series. Linear algebra is one of the most useful branches of applied mathematics for economists to invest in. For example, many applied problems in economics and finance require the solution of a linear system of equations. In this lecture we will cover the basics of linear and matrix algebra, treating both theory and computation. We admit some overlap with this lecture, where operations on NumPy arrays were first explained. Note that this lecture is more theoretical than most, and contains background material that will be used in applications as we go along. In an earlier lecture on Pandas, we looked at working with simple data sets. Econometricians often need to work with more complex data sets, such as panels. Common tasks include: – Importing data, cleaning it and reshaping it across several axes. – Selecting a time series or cross-section from a panel. – Grouping and summarizing data. Pandas (derived from ‘panel’ and ‘data’) contains powerful and easy-to-use tools for solving exactly these kinds of problems.
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