Nobel Laureate Thomas J. Sargent can’t get enough of Julia.
Professor Sargent is the founder of QuantEcon,
a platform that advances pedagogy in quantitative economics using both
Julia and Python. His team at NYU uses Julia for macroeconomic modeling
and contributes to the Julia ecosystem.
Speaking at JuliaCon at MIT in June 2016, Professor Sargent explained
that the reason Julia is so important for his work is because the next
generation of macroeconomic models is very computationally intensive
with large datasets and large numbers of variables. These macroeconomic
models and their forecasts help solve large constrained optimization
problems using massive datasets to inform policy analysis.
The complexity stems from a large number of different economic actors -
including individuals, governments and businesses – each with a
different welfare maximization function, plus a number of different
resource and information constraints. Consider that each economic actor
makes decisions based on expectations of the future, which means that
each economic actor also has their own forecasting model.
It may come as no surprise that such models can become very complicated
mathematically. According to Professor Sargent, this is why he and his
team require Julia.
“Why are macroeconomists like myself so interested in and excited by
Julia? Because our models are complicated. It’s easy to write the
problem down, but it’s hard to solve it – especially if our model is
high dimension. That’s why we need Julia.”
These models tend to involve a number of discrete dynamic programs
(Discrete DPs), which are the workhorses of macroeconomics. One such
Discrete DP is the Bellman Equation, which is a functional equation and
is often used to solve discrete time optimization problems. Bellman
equations, though easily parallelizable, often run into higher
dimensions, which makes them relatively hard to solve. More elaborate
models that solve and predict financial crises involve an even more
complicated paradigm called dynamic programming squared (DP²).
DP² models involve Bellman equations within Bellman equations. The
inner Bellman equations describe responses of people whose incentives
are affected by government policies. Solving this DP² problem would
not only involve higher dimensions, but would also involve solving large
numbers of simultaneous inequalities in order to fit the data.
According to Professor
Sargent, “Julia is a great tool for doing this. This is a walking
advertisement for Julia.”
Thomas J. Sargent is among a new generation of professors using Julia
both for teaching, and to conduct cutting edge research. Professor
Sargent hinted that he would look to solve more important dynamic
programming problems in his research, such as Dynamic Programming Cubed
our enterprise products
Need help with Julia?
We also provide training and consulting services
and build open source or proprietary packages
for our customers on a consulting basis. Email us:
Julia Computing was founded by all the creators
of the language to provide commercial support
to Julia users. We are based in Boston, New York,
San Francisco, London and Bangalore with
customers across the world.
© 2016 - 2020 Julia Computing, Inc. All Rights Reserved.