What makes an econometric model robust? I was reading a paper on robustness (http://econ.ucsb.edu/~doug/245a/Papers/Robustness%20Checks.pdf) and they say:
"To determine whether one has estimated effects of interest, $\beta$; or only predictive coefficients, $\hat{\beta}$ one can check or test robustness by dropping or adding covariates."
What does a model being robust mean to you? Is this the only way to consider it in  an econometric sense?
 A: Take a look at this Hal Varian paper:

Many papers in applied econometrics present regression results in a
  table with several different specifications: which variables are
  included in the controls, which variables are used as instruments, and
  so on. The goal is usually to show that the estimate of some
  interesting parameter is not very sensitive to the exact specification
  used. One way to think about it is that these tables illustrate a
  simple form of model uncertainty: how an estimated parameter varies as
  different models are used. In these papers the authors tend to examine
  only a few representative specifications, but there is no reason why
  they couldn’t examine many more if the data were available.

I would also add that the effect may change when you alter the covariates or the sample, but it should do so in a predictable and theoretically consistent manner to be called robust. 
There are other sense of robust that are often used and are somewhat related: robust to heteroskedasticity or autocorrelation, outliers, and various assumption violations (like error distributions). 
