Paul Krugman wrote an uncharacteristically positive post today about John Cochrane’s latest post in which Cochrane dialed it down a bit after writing two rather heated posts (here and here) attacking Alan Blinder for a recent piece he wrote in the New York Review of Books in which Blinder wrote dismissively quoted Cochrane’s dismissive remark about Keynesian economics being fairy tales that haven’t been taught to graduate students since the 1960s. I don’t want to get into that fracas, but I was amused to read the following paragraphs at the end of Cochrane’s second post in the current series.
Thus, if you read Krugman’s columns, you will see him occasionally crowing about how Keynesian economics won, and how the disciples of Stan Fisher at MIT have spread out to run the world. He’s right. Then you see him complaining about how nobody in academia understands Keynesian economics. He’s right again.
Perhaps academic research ran off the rails for 40 years producing nothing of value. Social sciences can do that. Perhaps our policy makers are stuck with simple stories they learned as undergraduates; and, as has happened countless times before, new ideas will percolate up when the generation trained in the 1980s makes their way to to top of policy circles.
I think we can agree on something. If one wants to write about “what’s wrong with economics,” such a huge divide between academic research ideas and the ideas running our policy establishment is not a good situation.
The right way to address this is with models — written down, objective models, not pundit prognostications — and data. What accounts, quantitatively, for our experience? I see old-fashioned Keynesianism losing because, having dramatically failed that test once, its advocates are unwilling to do so again, preferring a campaign of personal attack in the popular press. Models confront data in the pages of the AER, the JPE, the QJE, and Econometrica. If old-time Keynesianism really does account for the data, write it down and let’s see.
So Cochrane wants to take this bickering out of the realm of punditry and put the conflicting models to an objective test of how well they perform against the data. Sounds good to me, but I can’t help but wonder if Cochrane means to attribute the academic ascendancy of RBC/New Classical models to their having empirically outperformed competing models? If so, I am not aware that anyone else has made that claim, including Kartik Athreya who wrote the book on the subject. (Here’s my take on the book.) Again just wondering – I am not a macroeconometrician – but is there any study showing that RBC or DSGE models outperform old-fashioned Keynesian models in explaining macro-time-series data?
But I am aware of, and have previously written about, a paper by Kenneth Carlaw and Richard Lipsey (“Does History Matter?: Empirical Analysis of Evolutionary versus Stationary Equilibrium Views of the Economy”) in which they show that time-series data for six OECD countries provide no evidence of the stylized facts about inflation and unemployment implied by RBC and New Keynesian theory. Here is the abstract from the Carlaw-Lipsey paper.
The evolutionary vision in which history matters is of an evolving economy driven by bursts of technological change initiated by agents facing uncertainty and producing long term, path-dependent growth and shorter-term, non-random investment cycles. The alternative vision in which history does not matter is of a stationary, ergodic process driven by rational agents facing risk and producing stable trend growth and shorter term cycles caused by random disturbances. We use Carlaw and Lipsey’s simulation model of non-stationary, sustained growth driven by endogenous, path-dependent technological change under uncertainty to generate artificial macro data. We match these data to the New Classical stylized growth facts. The raw simulation data pass standard tests for trend and difference stationarity, exhibiting unit roots and cointegrating processes of order one. Thus, contrary to current belief, these tests do not establish that the real data are generated by a stationary process. Real data are then used to estimate time-varying NAIRU’s for six OECD countries. The estimates are shown to be highly sensitive to the time period over which they are made. They also fail to show any relation between the unemployment gap, actual unemployment minus estimated NAIRU and the acceleration of inflation. Thus there is no tendency for inflation to behave as required by the New Keynesian and earlier New Classical theory. We conclude by rejecting the existence of a well-defined a short-run, negatively sloped Philips curve, a NAIRU, a unique general equilibrium, short and long-run, a vertical long-run Phillips curve, and the long-run neutrality of money.
Cochrane, like other academic macroeconomists with a RBC/New Classical orientation seems inordinately self-satisfied with the current state of the modern macroeconomics, but curiously sensitive to, and defensive about, criticism from the unwashed masses. Rather than weigh in again with my own criticisms, let me close by quoting another abstract – this one from a paper (“Complexity Eonomics: A Different Framework for Economic Thought”) by Brian Arthur, certainly one of the smartest, and most technically capable, economists around.
This paper provides a logical framework for complexity economics. Complexity economics builds from the proposition that the economy is not necessarily in equilibrium: economic agents (firms, consumers, investors) constantly change their actions and strategies in response to the outcome they mutually create. This further changes the outcome, which requires them to adjust afresh. Agents thus live in a world where their beliefs and strategies are constantly being “tested” for survival within an outcome or “ecology” these beliefs and strategies together create. Economics has largely avoided this nonequilibrium view in the past, but if we allow it, we see patterns or phenomena not visible to equilibrium analysis. These emerge probabilistically, last for some time and dissipate, and they correspond to complex structures in other fields. We also see the economy not as something given and existing but forming from a constantly developing set of technological innovations, institutions, and arrangements that draw forth further innovations, institutions and arrangements.
Complexity economics sees the economy as in motion, perpetually “computing” itself — perpetually constructingitself anew. Where equilibrium economics emphasizes order, determinacy, deduction, and stasis, complexity economics emphasizes contingency, indeterminacy, sense-making, and openness to change. In this framework time, in the sense of real historical time, becomes important, and a solution is no longer necessarily a set of mathematical conditions but a pattern, a set of emergent phenomena, a set of changes that may induce further changes, a set of existing entities creating novel entities. Equilibrium economics is a special case of nonequilibrium and hence complexity economics, therefore complexity economics is economics done in a more general way. It shows us an economy perpetually inventing itself, creating novel structures and possibilities for exploitation, and perpetually open to response.
HT: Mike Norman