Of these, it seems as if (i) and (iv) are unambiguously bad, (ii) is unambiguously good, and (iii) is good for workers but bad for employers (with a second-order net efficiency loss). As noted the size of (i) is the subject of the Rauchway vs Darby/Shlaes debate.

Is there any good evidence on the relative magnitudes of (ii), (iii) and (iv)?

Regarding comparing the work I did with Cole vs. Kydland and Prescott, as noted by Mr. Sadowski. The point I wish to make is that there are many models routinely used today that have as a foundation Kydland and Prescott, but have very different policy implications. It is hard to call these other models RBC models, which typically refers to a one-sector model with a representative agent (perfect risk-sharing) with random changes to productivity, and in which allocations are optimal. There really isnt a useful comparison between Kydland-Prescott, and what in the 1970s was called new classical macroeconomics, which had to do with whether anticipated monetary policy was neutral. If you add money to Kydland and Prescott, which many have done, anticipated monetary policy is indeed non-neutral.

Regarding comparisons between Kydland and Prescott and more recent work, my model with Cole has what some would call “structural unemployment” and is an optimizing insider-outsider model. About 20 years ago, Aiyagari developed a quantitative model with uninsurable risk, (a non-representative agent model), but that was clearly related to Kydland-Prescott. At about the same time, Danthine and Donaldson were working out models with imperfectly flexible prices and wages within the Kydland-Prescott model. Kocherlakota, Golosov, and Tsyvinksi and others have developed quantitative models with repeated principal-agent issues. All of these at some level have as antecedents Kydland-Prescott, but clearly differ along a number of dimensions.

And, as an outsider, I’m obviously interested in distinctions within the New Classical/RBC school.

http://www.forbes.com/2009/06/16/stimulus-arra-government-spending-krugman-prescott-opinions-contributors-ohanian.html

That being said I found his comment illuminating (in a Freudian sort of way). Evidently he wants to distance himself from “the type of RBC models that the threat is about.” (Yes, in fact, “t” is nowhere near “d” on the ASCII keyboard.)

Almost all of the increase in GDP between 1933-39 is from productivity, not recovery in hours worked, and GDP per person remains 27 percent below the normal 2 percent trend at the end of the decade. Cole and I attribute the failure of hours to recover from polices that promoted monpoly and high wages in some sectors. This is very far from the type of RBC models that the threat is about. In fact, the Cole-Ohanian model is in some ways closer to models with involuntary uenmployment than the original Kydland-Prescott model.

For those interested in more detailed discussion of this, see:

http://www.econ.ucla.edu/files/senate_testimony_april_04_2009_ohanian.pdf href=”http://www.princeton.edu/~rbenabou/groupthink%20iom%204l%20new2.pdf” rel=”nofollow”>http://www.princeton.edu/~rbenabou/groupthink%20iom%204l%20new2.pdf

What do we want? Realistic economics! When do we want it? In the short-to-medium term, subject to prevailing conditions!

“Hallucinogenic Business Cycle Theory (or HBC Theory) is a class of psychedelic macroeconomic models in which business cycle fluctuations to a large extent can be accounted for by imaginary (in contrast to reality based) shocks. (The four primary economic fluctuations are the gold rush, the bubble (deviation from trend), the counterintuitive movement, and mass hysteria (also known as “the panic” in classic terminology).) Unlike other leading theories of the business cycle, it sees recessions and periods of economic growth as an artificial response to illusory changes in the hallucinatory economic environment. That is, the level of national output necessarily minimizes the irrationally expected utility, and government should therefore concentrate on pretending to make short-run policy changes and intervene through random statements of make-believe fiscal or monetary policy designed to actively and whimsically whip the general public into a false sense of security.

According to HBC theory, business cycles are therefore “hallucinogenic” in that they are based on complete fantasy, and are the most inefficient possible operation of the economy, even given its seemingly perpetually unviable nature. It differs in this way from other theories of the business cycle, like Keynesian economics and Monetarism, which see asset bubbles as being untenable, and recessions as having tangible causes, which lead to what are known as “real-world repercussions.”

An important principle underlying HBC Theory is the principle of irrational expectations. Irrational expectations theory defines this kind of expectations as being identical to a wild guess about the future (a preposterous forecast) that systematically ignores, or thoroughly misinterprets, all of the available information. However, even with an unlimited number of additional assumptions, this theory of expectations indetermination still makes the prediction that human behavior will still be completely capricious and herd like. Thus, it is assumed that outcomes that are being forecast differ arbitrarily or unpredictably from the market disequilibrium results. As a result, irrational expectations differ substantially from disequilibrium results. That is, it assumes that people systematically make errors when predicting the future, and deviations from common sense happen consistently. In an economic model, this is typically modeled by assuming that the expected value of a variable is equal to a spontaneous error term representing the role of ignorance and mistakes plus the value of some completely irrelevant piece of information (such as the price of tea in China).”

P.S. Is it really safe to say that the theoretical program of RBC is dead? People are still quoting Robert Lucas as though he ever really knew what he was talking about. (Like perfectly vertical LM curves. Hahahahahahaha!)

On economic history, Lee and Ohanian used the once standard calculations of Stanley Lebergott (sp?). They were the official numbers in the Statistical Abstract of the United States until economic historians convinced the editors that they were silly.
The exclusion of WPA workers (but not other public sector employees) from the employed was certainly based on ideoological hostility to the new deal. Lebergott’s methodological note bases the choice on the assertion that the WPA did not differ in any relevant regard from Buchenwald and the Gulag (i am not exagerating). I’d guess you know this as I got if from Rauchway, so your description of who lost the millions of workers leaves out Lebergott for brevity.

However, his numbers were standard. I’ve used them myself. It’s not totally fair to Lee and Ohanian to give them all of the blame for the definitional choice.

The blaming WWII on Yalta analogy is, however, perfect.

A Potted History of Macroeconomics:

Before, during and shortly after WW2:
1) Keynes’s model of the macro-economy, as interpreted by Hicks, Hansen and Modigliani

2) The Cowles Commission’s work on statistical problems related to understanding the business cycle, esp. estimation of systems of simultaneous equation

3) Cowles Commission looks for models to estimate, including input-output models (Marschak and Andrews, Econometrica 1944), the consumption function (Haavelmo, JASA 1947; and Girshick and Haavelmo, Econometrica 1947) and early (traditional, i.e., Keynesian) macroeconomic models (Klein, “Economic Fluctuations in the US”, 1950).

1950s-1960s
4) Following this period, we get much theoretical and econometric work; the focus of the theoretical work is to improve understanding of the various components of the NIPA that had become central to macro models. The purpose of the work in econometrics was to improve the models’ fit of the data, and out of sample forecasting, and to develop better statistical estimators.

4a) The econometric work culminates in the macro models identified with Ray Fair, DRI and the Fed-MIT-Penn. (1950s to the present). The most important analysis that did not itself present a new extension to a model or a new estimator was Adelman*2 (Economica, 1959), entitled, “Dynamic Properties of the Klein-Goldberger Model”. I believe this is the first use of a macroeconometric model for simulations.

4b) The pertinent theoretical work is, I think, Modigliani’s (1954) and Friedman’s(1957) models and studies of consumption. I think (not sure) that these were the first attempts to analyze aggregates as the result of a single agent’s solution to an optimization problem, i.e., the Representative Agent (RA)

5) Lucas and Rapping (1969) extend the RA to labor supply.

1980s-2000s
6) Kydland & Prescott (1982) combine the RA with the Adelmans’ work in a simple GE model of the macro-economy, the first RBC model. They did not estimate this model because, in Sargent’s words,

“Calibration is less optimistic [than MLE] about what your theory can accomplish because you’d only use it if you didn’t fully trust your entire model, meaning that you think your model is partly misspecified or incompletely specified, or if you trusted someone else’s model and data set more than your own. My recollection is that Bob Lucas and Ed Prescott were initially very enthusiastic about rational expectations econometrics. After all, it simply involved imposing on ourselves the same high standards we had criticized the Keynesians for failing to live up to. But after about five years of doing likelihood ratio tests on rational expectations models,I recall Bob Lucas andEd Prescott both telling me that those tests were rejecting too many good models. The idea of calibration is to ignore some of the probabilistic implications of your model, but to retain others. Somehow, calibration was intended as a balanced response to professing that your model, though not correct, is still worthy as a vehicle for quantitative policy analysis.”
(Macroeconomic Dynamics, 2005).

7) Eventually, 3 things happen to RBC models:

7a) As they are elaborated (1980s-1990s), New Keynesians begin to play with these models, giving them features and dynamic behavior that they believe appropriate;

7b) The theoretical models are used to generate econometric models, complete with equations that can be estimated;

7c) People stop referring to RBC models and start referring to Dynamic Stochastic GE models (DSGE).

Conclusion
What macroeconomics is left with, after roughly 40 years of the rational expectations hypothesis is:

a) a new method for generating models for policy experiments, and perhaps for forecasting
b) new econometric techniques for estimating these models, and new algorithms for solving them.
c) much stronger reliance on the RA than ever before, as a way to enforce a strong link between theory – optimizing behavior – and analysis.

The parallels between the Keynesian revolution and the RE revolution are striking. Both start with a basic model of the economy. Eventually, technology comes along to estimate the model. Either before or after this point, people use the model not just for understanding how different pieces of the economy fit together, but to analyze the dynamic structure of the macroeconomy, especially in support of their preferred policies. Both have critics all along the way, and (at least for American economists) whether you are a critic or a supporter maps pretty well to your political views (less true once New Keynesians make DSGE models their own). After about 30-40 years, both come acropper, the Keynesian due to the shifting Phillips Curve in the late 1960s & 1970s, and the associated stagflation; the RE due to the widespread belief (and its eventual obvious falsity) that markets are self-regulating and self-correcting, and that a financial crisis on the order of 1929-1933 is no longer possible.

Hence, as I like to call it, the need for realistic economics. Look, there’s a whole looming alp of memetic high ground here, and someone better get up here and claim it before someone else does…

Meaning… If you want to make whatever model you decide to support look like the real world, you just need to throw a random variable in front of some actually endogenous variable and then fit it’s distribution with something that is estimated from economic history. These models only mimic the real world (and mimic it only in terms of similar means and variances – not actual behavior) because they are rigged to do just that, with all of the dynamics being imported exogenously. In general, these models seem to require continuous shocks to maintain dynamic movement…otherwise, an initial impulse fades away quite rapidly and things return to steady state very quickly. Essentially, to get any of the DSGE (dynamic stochastic gen-eq) models to give you those neat, RBC looking squiggly graphs, they need to be hit by randomly generated shocks every period. Clearly, every period, everyone just decides that they will aggregately be more productive, or like leisure more (complicated here, especially since these leisure choices really look more like unemployment…how many of us can actually say I’m going to work only 6.5 hours/day this quarter), or whatever each and every quarter.
The DSGE project started, from what I gather, as a response to a very smart epistemological argument. It argued that traditional econometric predictions could never be useful when something fundamentally changed…they could only predict based on the past and assume the present was the same. The critique is right, but the solution has been to put all the emphasis on the exogenous and, being economists, we don’t set about understanding them. (Note from quick edit: the emphasis in thinking about the models is on the endogenous, but the work is all being done by the exogenous RVs). We just call everything we don’t understand random and then backwardly engineer distributions so that, if they were actually completely random, our models could generate data similar to what we see. But this poses a whole different epistemological problem, namely, our models have nothing really interesting to say about why these fluctuations occur as they do…the response to impulses is understood from the micro groundings, but the impulses themselves (the really interesting economic question for macro) are completely disregarded. It’s not like anyone would actually believe that productivity is stochastically determined in any given period…what could that possibly mean? A machine rolls a die to decide how many ball bearings it will produce today (and even here, shouldn’t some LLN and CLT hold, smoothing things on aggregate)? It’s just that people seem to think….let’s develop these models, and then if someone actually ever figures out why productivity changes from period to period, maybe we can incorporate that, but that’s not really our problem. Our problem is finding a model that can make squiggly lines that look like business cycles.