As my list indicates, some may be only loosely based on theory, others more explicitly so. This being said, the different classes of models have a lot to learn from each other, and would benefit from more interactions. But the goal of full integration has, I believe, proven counterproductive. They allow for a quick first pass at some question, and present the essence of the answer from a more complicated model or from a class of models. As a result, my contribution seems useful. I do not think so. Initiative for open bibliographies in EconomicsVarious rankings of research in Economics & related fieldsRePEc working paper series dedicated to the job marketTo make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. So, to me, the research priorities are clear:DSGE modellers should accept the fact that theoretical models cannot, and thus should not, fit reality closely. DSGE models have come to play a dominant role in macroeconomic research. Those must fit the data more closely, and this is likely to require in particular more flexible, less microfounded, lag structures (an example of such a model is the FRB/US model used by the Federal Reserve, which starts from microfoundations but allows the data to determine the dynamic structure of the various relations). This "Cited by" count includes citations to the following articles in Scholar.
I believe the answer to this is no. Semantic Scholar profile for Olivier Blanchard, with 465 highly influential citations and 106 scientific research papers. Does the model fit well, for example in the sense of being consistent with the dynamics of a VAR characterization? (For Meccano enthusiasts, the Meccano set number is 10. These are the discussions that must take place, not grand pronouncements on whether we should have DSGEs or not, or on the usefulness of macroeconomics in general.In some cases, maximum likelihood estimates of the parameters are well identified but highly implausible on theoretical grounds. Finally, come the models used for forecasting. \"Public Debt and Low Interest Rates,\"Working Paper Series WP19-4, Peterson Institute for International Economics. But it is not, and there are. The current core, roughly an RBC structure with one main distortion, nominal rigidities, seems too much at odds with reality to be the best starting point. Rather than looking for repairs, DSGE models should build on the large amount of work on consumer behaviour going on in the various fields of economics, from behavioural economics, to big data empirical work, to macro partial equilibrium estimation. To improve, however, they have to become less insular, by drawing on a much broader body of economic research. But, for the more casual reader, it is often extremely hard to understand what a particular distortion does on its own and then how it interacts with other distortions in the model. But art is of much value.I believe that there is wide agreement on the following three propositions; let us not discuss them further, and move on.For these models, capturing actual dynamics is clearly essential. Does it capture well the effects of past policies? Yet, I shall make him unhappy, and take the plunge.This set of remarks on the future of macroeconomic models was triggered by a project, led by David Vines, to assess how dynamic stochastic general equilibrium (DSGE) models had performed during the financial crisis (namely, badly) and to examine how these models might be improved. In many cases, the choice to rely on a ‘standard set of parameters’ is simply a way of shifting blame for the choice of parameters to previous researchers.These were the models David Vines (and many others) was criticizing when he started his project and, in their current incarnation, they raise two issues.Macroeconomics has been under scrutiny as a field since the financial crisis, which brought an abrupt end to the optimism of the Great Moderation. Given technological progress and the easy use of simulation programmes, such a model can and probably must be substantially larger than the IS–LM but still remain transparent. For conditional forecasting, i.e. We know, for example, that aggregation can make aggregate relations bear little resemblance to underlying individual behaviour.The purpose of these models is to help policy, to study the dynamic effects of specific shocks, to allow for the exploration of alternative policies. It may well be that, for these purposes, reduced form models will continue to beat structural models for some time; theoretical purity may be for the moment more of a hindrance than a strength.If, however, one does accept them (even if reluctantly), then wholesale dismissal of DSGEs is not an option. If their view of the world is correct, and network interactions are of the essence, they may well be right. They are art as much science.
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Olivier Blanchard google scholar