On the Lucas Critique:
But the term "Lucas critique" has survived, long after that original context has disappeared. It has a life of its own and means different things to different people. Sometimes it is used like a cross you are supposed to use to hold off vampires: Just waving it it an opponent defeats him. Too much of this, no matter what side you are on, becomes just name calling.
Business cycles are all alike?
As I have written elsewhere, I now believe that the evidence on post-war recessions (up to but not including the one we are now in) overwhelmingly supports the dominant importance of real shocks. But I remain convinced of the importance of financial shocks in the 1930s and the years after 2008. Of course, this means I have to renounce the view that business cycles are all alike!
ED: If the economy is currently in an unusual state, do micro-foundations still have a role to play?
RL: "Micro-foundations"? We know we can write down internally consistent equilibrium models where people have risk aversion parameters of 200 or where a 20% decrease in the monetary base results in a 20% decline in all prices and has no other effects. The "foundations" of these models don't guarantee empirical success or policy usefulness.
What is important---and this is straight out of Kydland and Prescott---is that if a model is formulated so that its parameters are economically-interpretable they will have implications for many different data sets. An aggregate theory of consumption and income movements over time should be consistent with cross-section and panel evidence (Friedman and Modigliani). An estimate of risk aversion should fit the wide variety of situations involving uncertainty that we can observe (Mehra and Prescott). Estimates of labor supply should be consistent aggregate employment movements over time as well as cross-section, panel, and lifecycle evidence (Rogerson). This kind of cross-validation (or invalidation!) is only possible with models that have clear underlying economics: micro-foundations, if you like.
This is bread-and-butter stuff in the hard sciences. You try to estimate a given parameter in as many ways as you can, consistent with the same theory. If you can reduce a 3 orders of magnitude discrepancy to 1 order of magnitude you are making progress. Real science is hard work and you take what you can get.
"Unusual state"? Is that what we call it when our favorite models don't deliver what we had hoped? I would call that our usual state.