Reddit mentions of Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences)

John Fox's book is great. It's mostly linear regression models for continuous variables, but the GLM section is very helpful. If I remember correctly, the second edition is way more helpful with GLM than the first.

For categorical variables Scott Long's book is wonderfully helpful.

Unfortunately both are expensive. Hopefully your library has them.

Any more specificity in what types of variables you might be working with or what your data is like? Knowing what type of link function you're looking for my give you better results from some of the uber statisticians here.

Beyond intermediate texts, my classes ended up just reading papers from econ journals. You may want to pick up an econometrics text, get familiar with the methods, then read papers (here is a list of the 100 most cited).

I wrote my opinions on econometric textbooks I've used for another reddit comment, so I just pasted it in below. If you get into it, I'd recommend reading a less rigorous book straight through, then using a more rigorous text as reference or to do the practice stuff.

Less Mathematically Rigorous

• Kennedy - survey of modeling issues without the math. More about how to think about modeling rather than how do it. Easy to read, I liked it
  
  
• Angrist - similar to Kennedy, covers the why & how econometrics answers questions, very little math. Each chapter starts with a hitchhikers guide to the galaxy quote, which is fun. Just as good as Kennedy
  
  
• Long - this book is more about just "doing stuff" and presenting results, absolutely non-technical, but also dodges the heavy thinking in Angrist & Kennedy so I wasn't a big fan
  
  
• King - covers the thinking of Angrist & content of Maddala. It is more accessible but wordier, so give it a go if Kennedy or Angrist are too much. It is aimed at Poli Sci rather than econ.
  
  Middle of the Road
  
  
• Gujarati - I used this for a class. It wasn't hard to follow, but it mostly taught methodology and the how/why/when/what, and I didn't like that - a little too "push button" and slow moving.
  
  
• Woodlridge - a bit more rigorous than Gujarati, but it was more interesting and was clearer about motivations from the standpoint of interesting problems
  
  
• Cameron & Trivedi - I liked the few chapters I read, the math is there, but the methodology isn't driven by the math. I ddin't get too far into it
  
  More Mathematically Rigorous
  
  
• Greene - lots of math, so much it was distracting for me, but probably good for people who really want to learn the methodology
  
  
• Wooldridge - similar to Greene, you need a solid understanding before diving into this book. Some of the chapters are impenetrable
  
  
• Maddala - this book is best for probit/logit/tobit models and is somewhat technical but dated. My best econometrics teacher loved it

http://www.amazon.com/Applied-Regression-Analysis-Generalized-Linear/dp/0761930426

http://www.amazon.com/Regression-Categorical-Dependent-Quantitative-Techniques/dp/0803973748

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

http://www-bcf.usc.edu/~gareth/ISL/

http://www.amazon.com/Extending-Linear-Model-Generalized-Nonparametric/dp/158488424X/ref=sr_1_2?s=books&ie=UTF8&qid=1380716057&sr=1-2

http://www.amazon.com/Generalized-Edition-Monographs-Statistics-Probability/dp/0412317605

hope it helps man. good luck. im learning this stuff for my project too