Introduction to LaTeX

Learning LaTeX can seem challenging at first, but with just a little practice it can become an easy and very powerful way to compose professional scientific documents. To help with the first steps, I have here provided a few screencast tutorials, latex source templates, and other resources. Hope this helps!


These screencasts are meant to be used alongside the appropriate .tex source files. All of the source files are compressed and available here. The first tutorial introduces the basic elements of LaTeX, explaining how the system works in general. The second tutorial goes over how to use the LaTeX homework template.

LaTeX Source Templates

These source templates can be used to get a running start to LaTeX. Each one features different elements of LaTeX or are useful in different situations. Two are meant to accompany the screencast tutorials mentioned above. One of them was a useful document I found online (citation included in the source document). You can view the compiled, pdf documents by clicking on of the links below. The corresponding .tex files can be downloaded here.

Other Resources

I have found these resources useful in the past.

  • LaTeX Wikibook: This online textbook is a great reference on using Latex. 
  • LaTeX Cheatsheet: This “cheat sheet” gets passed along a lot. It is a useful, quick reference.
  • Online LaTeX Equation Editor: This is useful for practicing or for when you want to quickly write an equation and save it as an image.
  • Detexify Latex Handwriting Tool: This is a great tool. If you ever can’t find the right symbol, just draw it and this site will figure it out for you!
  • Latex Flash Cards: If you are familiar with the flash card program Anki, I have created a shared deck here (and there’s another good LaTeX one here).

BYU Computational Public Economics Conference 2012

Last December was my first real conference experience. I was also excited to have some of research presented there. The BYU Computational Public Economics Conference 2012 was held in Park City, UT in late December. Park City is a fun city with great scenery and lots of things to do. And the conference was really just great. We had an amazing group of people show up, including Nobel-Prize-winning economist Tom Sargent, and there was a lot of interesting discussion to be had. Due to the good turnout, from what I can tell, the conference will be held again next winter (2013). Until then, be sure to check out the website, recording of the lectures, or some of the slides from this previous year!

The video below was made available by the BYU Macroeconomics and Computational Laboratory in the Department of Economics at Brigham Young University.

It’s Alive! New Workstation with Tesla C2075 GPU


This gallery contains 4 photos.

Recently, the BYU Macroeconomics and Computational Laboratory (the lab that I work in as a research assistant) was able to purchase a new workstation. Named Samuel, the workstation contains 2 Quad-core Xeon 2.26 GHz processors, 20 GB memory, and a Tesla … Continue reading

Parallel Programming Video Lectures Posted!

The BYU Computational Economics course has begun its unit on parallel computing. The lectures on parallel computing are being recorded and the screencasts are now available on the the course unit page,, along with all other course material (or view them directly on my Youtube Channel,

The course covers parallel programming with MPI, teaching it using Python and mpi4py. It also touches on a few parallel libraries and other useful tools in high performance computing.

Click image to link to ‘Video Lectures’ page.

Is Economics Ignoring Parallel Computing?

Parallel computing has taken a central role in efforts such as climate modeling, protein folding, drug discovery, energy research, and data analysis. But what sort of role has it taken in economics?

Over the past few years, as the development of ever faster monolithic processors has slowed, parallel computing has become more and more visibly important (see graph, taken from The Free Lunch is Over, or the article Parallel Bars, published 2011 in The Economist).

Intel CPU Introductions (graph updated August 2009; article text original from December 2004)

This is due to the fact that in many applications, the need for greater computational power is the most conspicuous of the several constraints holding researchers back from a solution. Seemingly, High Performance Computing (HPC) offers the most readily available solution. But, due to the nature of modern HPC architectures, the software requires special parallel design,  Accordingly, parallel computing has taken a central role in various efforts such as climate modeling, protein folding, drug discovery, energy research, and data analysis. But what sort of role has it taken in economics?

In a recent visit to my home institution, Kenneth Judd, the Paul H. Bauer Senior Fellow at the Hoover Institution at Stanford, expressed his concern that economics has not yet begun to take full advantage of the resources available and that a stigma against modern computational methods exists within a discipline that allegedly denounces them as “black box” methods. According to Judd, “economics is ignoring the potential of modern computational technologies and related mathematical methods” (click here for the full discussion).

Keeping in mind that parallel computing is a developing method and still far from widespread use in many disciplines (not just economics), I was curious to see how far it had made it into the economics community. A quick Google search for “Parallel Computing in Economics” led me to the following results:

Articles and Book Chapters

Conferences and Other Initiatives


  • Parallel Dynare (Dynare is a user-friendly software platform designed to solve a wide class of economic models, in particular, dynamic stochastic general equilibrium (DSGE) and overlapping generations (OLG) models.)
  • PETSc (Portable, Extensible Toolkit for Scientific Computation. They claim to have been used in application to economics)

As a disclaimer, I have included only a fraction of the of the results that I found. This quick search shows that certainly there is considerable work being done to take advantage of modern technology in economics. So perhaps a better question to ask would be, is economics lagging behind other disciplines in adopting these new methods?

While I am of the opinion that economics as well as most other disciplines (and I think most would agree with this) has yet to discover or fully apply the resources available in modern computational technology and, in particular, the increases in computational power afforded by parallel computing, I am unable to make answer the question about the progress the economics discipline has made relative to others. All I can say is that these resources will in time prove invaluable as the data we collect grows and the models we write become more complex. Indeed, as our computational power increases, the scope of problems that we can even begin to consider solving increases. Therefore, while I can’t answer the question about economics and parallel computing, I can certainly say that it’s one worth asking.