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Activity Number:
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360
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #307680 |
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Title:
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Optimal Impulse Response Function Matching Estimation
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Author(s):
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Barbara Rossi*+ and Atsushi Inoue and Alastair R. Hall and James Nason
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Companies:
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Duke University and University of British Columbia and University of Manchester and Federal Reserve Bank of Atlanta
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Address:
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213 Social Science Building, Durham, NC, 27708,
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Keywords:
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impulse response ; information criterion ; DSGE ; model estimation ; classical minimum distance estimators
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Abstract:
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We propose a new Impulse Response Function Matching estimator for the parameter of a structural model based on classical Minimum Distance estimation, where the number of impulse responses is selected according to an Information Criterion. The advantages of our procedure are that: (i) it improves the efficiency of the estimates of the model's deep parameters; (ii) it allows the researcher to select the impulse responses that are more informative about the deep parameters. An empirical application to the estimation of representative Dynamic Stochastic General Equilibrium models show that our method can substantially improve inference.
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