by The blatant absence of empirical fit of macroeconomic models Some months ago sorta-kinda ‘New Keynesian’ Paul Krugman argued on his blog that the problem with the academic profession is that some macroeconomists aren’t “bothered to actually figure out” how the ‘New Keynesian’ model with its Euler conditions — “based on the assumption that people have perfect access to capital markets, so that they can borrow and lend at the same rate” — really works. According to Krugman, this shouldn’t be hard at all — “at least it shouldn’t be for anyone with a graduate training in economics.” But if people — not the representative agent — at least sometimes can’t help being off their labour supply curve — as in the real world — then what are these hordes of Euler equations that you find ad nauseam in these ‘New Keynesian’ macro models going to help us? Yours truly’s doubts regarding the ‘New Keynesian’ modelers’ obsession with Euler equations is basically that, as with so many other assumptions in ‘modern’ macroeconomics, the Euler equations don’t fit reality.
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Lars Pålsson Syll considers the following as important: Economics
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The blatant absence of empirical fit of macroeconomic models
Some months ago sorta-kinda ‘New Keynesian’ Paul Krugman argued on his blog that the problem with the academic profession is that some macroeconomists aren’t “bothered to actually figure out” how the ‘New Keynesian’ model with its Euler conditions — “based on the assumption that people have perfect access to capital markets, so that they can borrow and lend at the same rate” — really works. According to Krugman, this shouldn’t be hard at all — “at least it shouldn’t be for anyone with a graduate training in economics.”
But if people — not the representative agent — at least sometimes can’t help being off their labour supply curve — as in the real world — then what are these hordes of Euler equations that you find ad nauseam in these ‘New Keynesian’ macro models going to help us?
Yours truly’s doubts regarding the ‘New Keynesian’ modelers’ obsession with Euler equations is basically that, as with so many other assumptions in ‘modern’ macroeconomics, the Euler equations don’t fit reality.
In a classic paper by Hansen and Singleton (1982) only very little support for the Euler equations was found, and in later paper by Canzoneri, Cumby, and Diba (2006) it was confirmed that there is vanishing little support for real people acting according to the Euler equatons
In the standard neoclassical consumption model — underpinning ‘New Keynesian’ microfounded macroeconomic modeling — people are basically portrayed as treating time as a dichotomous phenomenon – today and the future — when contemplating making decisions and acting. How much should one consume today and how much in the future?
The Euler equation implies that the representative agent (consumer) is indifferent between consuming one more unit today or instead consuming it tomorrow. This importantly implies that according to the neoclassical consumption model that changes in the (real) interest rate and the ratio between future and present consumption move in the same direction.
So good, so far. But how about the real world? Is the neoclassical consumption as described in this kind of models in tune with the empirical facts? Not at all — the data and models are as a rule insconsistent!
In the Euler equation we only have one interest rate, equated to the money market rate as set by the central bank. The crux is that — given almost any specification of the utility function – the two rates are actually often found to be strongly negatively correlated in the empirical literature.
Theories are difficult to directly confront with reality. Economists therefore build models of their theories. Those models are representations that are directly examined and manipulated to indirectly say something about the target systems.
But being able to model a ‘credible world,’ a world that somehow could be considered real or similar to the real world, is not the same as investigating the real world. Even though all theories are false, since they simplify, they may still possibly serve our pursuit of truth. But then they cannot be unrealistic or false in any way. The falsehood or unrealisticness has to be qualified.
If we cannot show that the mechanisms or causes we isolate and handle in our models are stable, in the sense that what when we export them from are models to our target systems they do not change from one situation to another, then they only hold under ceteris paribus conditions and a fortiori are of limited value for our understanding, explanation and prediction of our real world target system.
But how do mainstream economists react when confronted with the monumental absence of empirical fit of their macroeconomic models? Well, they do as they always have done — they use one of their four pet strategies for immunizing their models to the facts:
(1) Treat the model as an axiomatic system, making all its claims into tautologies — ‘true’ by the meaning of propositional connectives.
(2) Use unspecified auxiliary ceteris paribus assumptions, giving all claims put forward in the model unlimited ‘alibis.’
(3) Limit the application of the model to restricted areas where the assumptions/hypotheses/axioms are met.
(4) Leave the application of the model open, making it impossible to falsify/refute the model by facts.
Sounds great doesn’t it?
Well, the problem is, of course, that ‘saving’ theories and models by these kind of immunizing strategies are totally unacceptable from a scientific point of view.
If macroeconomics has nothing to say about the real world and the economic problems out there, why should we care about it? As long as no convincing justification is put forward for how the inferential bridging between model and reality de facto is made, macroeconomic modelbuilding is little more than hand waving.
The real macroeconomic challenge is to face reality and still try to explain why economic transactions take place – instead of simply conjuring the problem away by assuming rational expectations, or treating uncertainty as if it was possible to reduce it to stochastic risk, or by immunizing models by treating them as purely deductive-axiomic systems. That is scientific cheating. And it has been going on for too long now.