What is your name?

Dr. Paul Beaumont

What kind of work do you do in the college?

I am a Professor in the Department of Economics at Florida State University. I teach, do research and engage in service in the department, profession and community.

Why did you decide to become an academic?

Honest answer? My Ph.D. advisor told me that I would never survive outside of academics. Presumably, academicians have some sort of genetic defect.  Actually, I did try a brief hiatus from academics to develop algorithms to price complex financial derivatives.  I quickly learned about the 80/20 rule in industry: 80% of the profits come from the first 20% of the research. To me that means, just when a project gets interesting you move on to something else. That is just not fun. So, back to academics where we get to spend 80% of our time on the last 20% of a problem—the interesting bits. 

What do you find most fulfilling about your job?

I have really enjoyed my academic career. I love teaching and watching my students develop and working with my graduate students on research projects. In this career you are constantly learning. Every semester I have a new group of students asking “have you read this?” or “how would I do that?”. My answers are usually “no” and “I’m not sure” so there is never a shortage of new things for me to explore. 

What are you working on or teaching right now that has you excited professionally?

Right now I am working on several projects with former students who are now professors at other universities. In one project we develop an algorithm to do multiple imputation on very large-scale data sets with missing or suppressed data subject to complex constraints. Imagine a multilayered spreadsheet with one billion numbers in it and 1 million of those numbers have gone missing due to some nonrandom but unknown process. What’s your best guess of the joint distribution of those missing numbers? The original motivation for this was a  “how would I do that?” question from a former student working at a government agency.  Another current project was motivated during my time in industry when we noticed that many financial time series followed very complex stochastic processes involving multiple long-memory cycles. The underlying models involve parameters that converge at different rates so the question was, “how do you estimate that thing?” This is one of those problems where the last 20% is really interesting. A third project is also motivated by a question from a government agency: “How can we measure the spillover impacts of simultaneous stimulus shocks to several sectors in the economy?” So, if I provide economic stimuli to several tech sectors, how many new jobs will be created in, say, the financial services sector? This was trickier than we thought so we had to develop a new type of vector autoregression impulse response function to get at the answer. 

I am very excited about my courses in the Masters in Applied Economics Program. This is one of the top programs of its type in the country and it has a very impressive history of student placement. I teach a data analysis course where students use data science tools to develop models and produce interactive reports that can be presented online and updated in real time. This is a fast moving field so it is always exciting to teach this class. I am currently developing a new course for the MS program that uses some of these same data science tools to explore modern financial portfolio management and how government policies impact financial markets. 

What are you reading and writing about right now?

An interesting read is: “God’s Bankers: A History of Money and Power at the Vatican” by Gerald Posner — depressing but fascinating.

Right now I am reading “Stories of Your Life and Others” by Ted Chiang — one of the best collections of short stories that I have read in several years. A blog that I find interesting is by Bloomberg opinion columnist <a href=”http://Matt Levine — it is finance with as sense of humor.

A couple of my recent publications are:

Wiesen, Thomas F. P., Paul M. Beaumont, Stefan C. Norrbin, and Anuj Srivastava. 2018.  Are generalized spillover indices overstating connectedness? Economics Letters, 173: 131–134.

Tzeng, Yu-Ying, Paul M. Beaumont and Giray Okten . 2018. Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall. <a href=”http://Computational Economics,  52, 1: 55–77.