Tyler Brough writes:

I am currently a PhD student in Finance. I was explaining my research today to a very senior scholar at a well known eastern business school, who remarked that the econometric methods I am using are “way too complex, and that unless you just do OLS no one will believe it anyway.” What are your thoughts about that comment? If I know that the data generating process violates all of the assumptions underlying the classical linear regression model then I have to use more complex methods do I not?

My reply: I agree with the senior scholar that it’s important–even necessary–to do the simpler linear regression in addition to the more elaborate model. If a more elaborate model gives a different answer than the least-squares regression, this doesn’t necessarily mean that the more elaborate model is wrong, or even too complex–but it does mean that you need to understand what’s happening in the transition from the simple to the complex model.

Sometimes I prefer the simpler model and I think the more complex model is giving misleading results and implausible extrapolation.

Other times I like the complicated model, and I put in the effort to understand why it differs from the simple model. Ultimately you have to go to the data and to the underlying problem being studied.

Used to suggest people deconstruct complex models and reconstruct them as simple linear models calling these understudy models – models that stand in for those who find the complex models difficult to grasp (complexity being in the mind of the beholder) …

But understudy belies a preference for complexity and although Radford Neal will remind us that [certain] more complex models will be less wrong the "trendy" complex model is possibly more wrong while being harder to discern its wrongness BUT more likley fit for thesis work and academic publication [i.e. trendy].

Just the right amount of complexity for the problem at hand is arguably the better answer.

Keith