I am a graduate student in economics who recently converted to R from other very well-known statistical packages (I was using SPSS mainly). My little problem at the moment is that I am the only R user in my class. My classmates use Stata and Gauss and one of my professors even said that R is perfect for engineering, but not for economics. He said that many packages are built by people who know a lot about programming, but not much about economics and therefore are not reliable. He also mentioned the fact that since no money is actually involved in building an R package, there is therefore no incentive to do it correctly (unlike in Stata for example) and that he used R for a time and got some "ridiculous" results in his attempts to estimate some stuff. Moreover, he complained about he random number generator in R which he said was "messy".

I've been using R for just a little more than a month and I must say I have fallen in love with it. All this stuff I am hearing from my professor is just discouraging me.

So my question is: "Is R reliable for the field of economics?".

Let me share a contrasting view point. I'm an economist. I was trained in econometrics using SAS. I work in financial services and just tonight I updated R based models which we will use tomorrow to put millions of dollars at risk.

Your professor is just plain wrong. But the mistake he's making is VERY common and is worth discussing. What your professor seems to be doing is commingling the idea of the R software (the GNU implementation of the S language) vs. packages (or other code) implemented in R. I can write crap implementations of a linear regression using SAS IML. As a matter of fact, I've done that very thing. Does that mean SAS is crap? Of course not. SAS is crap because their pricing is non-transparent, ridiculously expensive, and their in house consultants over promise, under deliver, and charge a premium for the pleasure. But I digress...

The openness of R is a double edged sword: Openness allows any Tom, Dick, or Harry to write a crap implementation of any algorithm they think up while smoking pot in the basement of the economics building. The same openness allows practicing economists to share code openly and improve on each other's code. The licensing rules with R mean that I can write parallelization code for running R in parallel on Amazon's cloud and not have to worry about licensing fees for a 30 node cluster. This is a HUGE win for simulation based analysis which is a big part of what I do. 

Your professor's comment that "many packages are built by people who know a lot about programming, but not much about economics" is, no doubt, correct. But there are 3716 packages on CRAN. You can be damn sure many of them were not written by economists. In the same way that you can be sure many of the 105,089 modules in CPAN were not written by economists. 

Choose your software carefully. Make sure you understand and have tested the tools you're using. Also make sure you understand the true economics behind  which ever implementation you chose. Getting locked into a closed software solution is more costly than just the licensing fees. 
