For those who may not know, Prof. Sargent was awarded the Nobel Prize in Economics in 2011. Also, kudos to the Sloan Foundation for funding this work. I wish there were more high quality textbooks of this kind.
A better name would have been Quantitative Macroeconomics, because that is clearly the target market. Yes, I know there are some generic topics in there like Linear Algebra that both micro and macro economists could use, but it's not like there is a lack of numpy and scipy examples on the Internet.
The treatment in the book we are discussing is decidedly macro, and looks generally similar to the treatment in a previous Sargent book titled "Recursive Macroeconomic Theory."
Disregarding the fact that job search is a subtopic of unemployment, one of the main concepts in macro, you'll notice their model parameters are human capital, investment, and wage. The only quasi micro flavor of that model is search effort as these variables will almost always be in equilibrium based on some kind of an optimal stopping rule. Whether that rule has to do with finding a new job or discovering information about, say, the prices in the market dictates whether it would be a micro or macro use case. Again, if you actually looked at the model, their optimal stopping rule is when the person finds another job. And once again, the idea of jobs are the main ingredient in the concept of unemployment.... a macro topic. Not to mention, whether or not I'm searching for a job dictates whether I'm factored into the unemployment rate or not.
So..... yeah, job search is definitely not micro.
Does anyone have anything to say about Julia's benefits vs R or even Python (SciPy, Numpy, etc)? I'm in a machine learning course this semester and we have a choice of language and I'm wondering if its worth it to try and use Julia rather than Python since its so hip. (Just kidding about it being hip, but it would be interesting to learn something with increasing developer support).
Python with Scikit-learn could be a good choice too from everything I hear (possibly even better, by some accounts).
To be clear, Julia is more than capable of doing ML, but I'd say that interface-wise its not quite there yet. Most of the pieces are there, everything from DataFrames to wrappers for GLMNet to random forests, and even the deep learning library Mocha.jl (check it out, its fantastic!). If you were to implement a new ML algorithm, I'd want to be doing it in Julia - it'll perform great without having to get in a multi-language scenario (like R+Rcpp or Python+???[numba?]).
And then 2 weeks later, it wouldn't compile. Ah, the joys of the cutting edge.
I'd say give it a shot. Julia is a really impressive language. I find it as easy and expressive as Python, but it's blazingly fast, offering near-native performance.
You can access the Python lectures from: quant-econ.net
It even has a comparison: http://quant-econ.net/python_or_julia.html
I ended up using Weka for parts of the class, and python using scikit-learn for the other parts.
However, I would say I am regretting not trying R. The other students in my course really liked it.
> Note: You are currently viewing an automatically generated PDF version of our on- line lectures, which are located at http://quant-econ.net Please visit the website for more information on the aims and scope of the lectures and the two language options (Julia or Python). This PDF is generated from a set of source files that are orientated towards the website and to HTML output. As a result, the presentation quality can be less consistent than the website.
And, indeed, the website is very nice. It'd be nice to change the link to the website.
I feel really sorry for physics, chemistry and other researchers who must be really worth something to get this prize just to have a moron believing that printing money solves economic issues sitting next to him.
