Overview of Computational Economics and Finance Certificate Program
Advances in computational power over the last 30 years have led to significant steps forward in how we do economics and finance. These advances have created an entirely new class of computational economists. Computational economists and financial analysts work in universities, other research institutions, business entities, and even tech companies like Alibaba, Tencent, and PingAn Bank.
A computational economist must understand a number of tools beyond those that a typical undergraduate program in the economics and finances has time to deliver. Understanding must include mastery of topics from many distinct disciplines including computer science, data science, mathematics, and statistics. It is virtually impossible to provide such a broad training at an undergraduate program so many schools have started to offer such training at the graduate level. Programs such as the University of Chicago’s Masters in Computational economics and finance, Quantitative Methods in the economics and finances at Columbia, and the Computational economics and finance Emphasis at UC Davis are leading the way. We aim to develop a similar graduate certificate program here at PHBS.
The PHBS certificate program aims to prepare students for ambitious careers as technically knowledgeable data scientists with special skills useful for computational economics and finance.
The PHBS certificate program will also provide invaluable technical skills for those of its graduates who might want to pursue a PhD program in economics, political science, or sociology after they will also have completed MA or MS programs in economics and/or finance.
The certificate program consists of four one-semester long classes together with an online preparatory “bootcamp’’ class to be completed during the summer preceding the certificate program sequence.
Absolute prerequisites for entering the PHBS certificate program are an undergraduate degree and mastery of undergraduate mathematics and statistics. It will also be useful for students to have taken some classes in economics, and finance. Students who enter the program will be expected to know computer science at a level typically taught in a good undergraduate class. If not, they should be prepared to learn at that level during a “bootcamp” that will be offered as part of a pre-certificate summer class.
Online bootcamp preparatory class
Basic Tools for Computational economics and finance: The introductory course in the sequence will be an online, self-paced course over 6 weeks covering skills that will be used for the remainder of the year. The class introduces students to the Python programming language and various software engineering tools that will be applied in subsequent courses.
Four Core Courses
The courses come in pairs, with two courses to be taken each semester.
Mathematical Foundations for Computational economics and finance: After quickly reviewing some key results from calculus and linear algebra, the class discusses random variables, model building, and model estimation (both frequentist and Bayesian). The course does two things: (1) It empowers students to build state of the art models; (2) It builds students’ mathematical maturity so that they are able to teach themselves mathematically rigorous material.
Data Tools for Computational economics and finance: This course arms students with cutting-edge data manipulation and management tools. The class uses the Python pandas package and emphasizes that “real-world” data are messy; the course presents essential tools for thriving in such an environment.
Dynamic Models for Computational economics and finance: This course covers models with dynamic components and procedures for decision making. Topics included are dynamic programming, time-series analysis (both Bayesian and frequentist), and Markov models (such as those used for text analysis). These topics are a focus of cutting-edge research. The course empowers students to apply these tools in research projects.
Machine Learning for Computational economics and finance: When applied correctly, powerful tools from “machine learning” allow individuals to approximate complex outcomes in the real world. However, when these tools are applied carelessly, results can be misleading. This course will cover supervised learning (both regression and classification) , reinforcement learning, and model selection via validation procedures. The course prepares students to apply a variety of classical and cutting-edge machine learning techniques to problems in the economics and finances. Students will be taught principled approaches that adhere to best practices and promote understanding and transparency.
|Semester||Course 1||Course 2|
|Summer:||Basic Tools for Computational economics and finance|
|Fall:||Mathematical Foundations for Computational economics and finance||Data Tools for Computational economics and finance|
|Spring:||Dynamic Models for Computational economics and finance||Machine Learning for Computational economics and finance|