Objective
DSGE models are the standard tool of quantitative macroeconomics. We use them to measure economics phenomena and to provide policy advice. However, since Kydland and Prescott s 1982, the profession has fought about how to take these models to the data. Kydland and Prescott proposed to calibrate their model. Why? Macroeconomists could not compute their models efficiently. Moreover, the techniques required for estimating DSGE models using the likelihood did not exist. Finally, models were ranked very badly by likelihood ratio tests. Calibration offered a temporary solution. By focusing only on a very limited set of moments of the model, researchers could claim partial success and keep developing their theory. The landscape changed in the 1990s. There were developments along three fronts. First, macroeconomists learned how to efficiently compute equilibrium models with rich dynamics. Second, statisticians developed simulation techniques like Markov chain Monte Carlo (MCMC), which we require to estimate DSGE models. Third, and perhaps most important, computer power has become so cheap that we can now do things that were unthinkable 20 years ago. This proposal tries to estimate non-linear and/or non-normal DSGE models using a likelihood approach. Why non-linear models? Previous research has proved that second order approximation errors in the policy function have first order effects on the likelihood function. Why non-normal models? Time-varying volatility is key to understanding the Great Moderation. Kim and Nelson (1999), McConnell and Pérez-Quirós (2000), and Stock and Watson (2002) have documented a decline in the variance of output growth since the mid 1980s. Only DSGE models with richer structure than normal innovations can account for this.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
ERC-2009-StG
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Host institution
28014 Madrid
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.