Nothing screams “GRAD STUDENT!!!” louder than...

Here is a bit more on the LPM vs. nonlinear models issue.

My favorite quote: "Indeed, nothing screams “GRAD STUDENT!!!” louder than an obsession with fancy estimators — usually of the maximum likelihood variety, so probit, logit, tobit, etc., sometimes of the Bayesian variety — instead of with whether one has reasonably identified one’s parameter of interest (via a research design that relies on a plausibly exogenous source of variation), or with whether one’s findings have some reasonable claim at being externally valid (via the use of a representative sample)."

Also liked this: 

There is an unspoken ontological order of importance to things in applied work, which unfortunately goes unspoken in most econometrics classes. That order is roughly as follows:

1. Internal validity: Is your parameter of interest credibly identified? In other words, are you estimating a causal relationship, or are you merely dealing with a correlation? If the latter, how close can you get to estimating a causal relationship with the best available data and methods?
2. External validity: Are your findings applicable to observations outside of your sample? Why or why not?
3. Precision: Are your standard errors right? Have you accounted for things like heteroskedasticity? Did you cluster your standard errors at the right level?
4. Data-generating process: Did you properly model the DGP? For example, does your estimation procedure account for the fact that, say, your dependent variable is a positive integer, which would require a Poisson or negative binomial regression?