Historically, economics has fallen into the bad habit of thinking the fancier estimation method is closer to being "right" -- based on one sample of data. We used to think this of GLS vs OLS until we paid careful attention to exogeneity assumptions.
#metricstotheface
#metricstotheface
We used to think this of 2SLS vs OLS until problems with weak instruments were revealed.
We still seem to think matching methods are somehow superior to regression adjustment if they give somewhat different estimates.
We still seem to think matching methods are somehow superior to regression adjustment if they give somewhat different estimates.
And we now seem to think ML methods are to be preferred over basic methods. Perhaps a reckoning is on its way.
And when we simulate, it's common to rig the simulation methods in favor of the fancier method. Such as imposing strict exogeneity when comparing GLS when correcting for serial correlation to OLS, and usually assuming the serial correlation model is correct.
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