Introductory Econometrics with Applications by Ramu Ramanathan
My review at Goodreads
I've been realizing that most of the data I work with are observational as opposed to the experimental data that most statistics that I know are designed for. So I've been working through this econometrics text. By way of background, I've taken Statistics as undergrad using books and tables, as a master's, which was essentially using SAS, and as a grad student, in a very mathematical sense. Working through Intro. Econometrics with Applications was learning what all the math based stats in grad school, but actually understanding it. And the computer package Gretl makes the learning interactive.
Ramanathan's biggest strength is he does not stop at teaching definitions, formula and methods. And his example applications do not stop at working through methodology. He uses the computer output to build intuition, asking the question of why this variable is what it is, or why two models are different. The use of Gretl enhances this. The scripts and data sets make it easy to see the examples at work, but also make it very easy to explore. I found myself running the scripts, then exploring the data sets in Gretl to further analysis, and building my understanding. The mathematical derivations are there, but having both the theory and the computer outputs for discussion make for a good match.
The book datasets and examples are bundled into the econometrics package Gretl, which is an open source program (available for Windows, Mac, Linux). In addition to correctness (it passes completely the NIST datasets, which is something even SAS and SPSS don't do) the scripts let you see what is happening (as opposed to SAS which often seems like black boxes inside the PROC statement). And more flexible then the menu based packages like Minitab (or Excel based statistical packages). Compared to R/S-Plus there is a shorter learning curve, as Gretl displays more of the output immediately, while allowing various statistics to be exposed for later use if needed (the reverse of R, which exposes statistics, but makes you work to display them. Great for programming, but harder for exploring data to the neophyte.)
I think that texts like this that are integrated with a full fledged statistics package (as opposed to purely math or a demo version) make the learning and applying of statistics different. (other texts that have Gretl datasets available are listed at Data for gretl) The focus of the learning is not on the memorization of equations or definitions, but on the learning of methods and techniques. The chapter summaries become short forms of methodology (as opposed to formula) with the assumptions that the various methods repeated as well. I found this to be a particularly effective format for a first book on a subject (assumes a first sequence in undergrad statistics has been completed).
As of now, I've gone through 5 chapters of the book, working through the examples. As I do this, I am learning a lot about analysis, that is going to inform a project I am starting up. That is probably the best testimony for a book of this type.
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