Replication data for: Estimation and Inference of Impulse Responses by Local Projections
Principal Investigator(s): View help for Principal Investigator(s) Òscar Jordà
Version: View help for Version V1
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EM_MC.doc | application/msword | 547.5 KB | 12/06/2019 10:27:AM |
GARCH_MC.doc | application/msword | 27 KB | 12/06/2019 10:27:AM |
LICENSE.txt | text/plain | 14.6 KB | 12/06/2019 10:27:AM |
evnew.csv | text/plain | 31.7 KB | 12/06/2019 10:27:AM |
irfs.g | text/x-c | 5.2 KB | 12/06/2019 10:27:AM |
irfs.src | text/plain | 7.2 KB | 12/06/2019 10:27:AM |
irfsmc1.g | text/plain | 9.1 KB | 12/06/2019 10:27:AM |
irfsmc1.src | text/plain | 22.2 KB | 12/06/2019 10:27:AM |
irfsmc2.g | text/x-fortran | 13.1 KB | 12/06/2019 10:27:AM |
irfsmc2.src | text/plain | 13.1 KB | 12/06/2019 10:27:AM |
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Project Citation:
Jordà, Òscar. Replication data for: Estimation and Inference of Impulse Responses by Local Projections. Nashville, TN: American Economic Association [publisher], 2005. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-06. https://doi.org/10.3886/E116037V1
Project Description
Summary:
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This paper introduces methods to compute impulse responses without specification and estimation of the underlying multivariate dynamic system. The central idea consists in estimating local projections at each period of interest rather than extrapolating into increasingly distant horizons from a given model, as it is done with vector autoregressions (VAR). The advantages of local projections are numerous: (1) they can be estimated by simple regression techniques with standard regression packages; (2) they are more robust to misspecification; (3) joint or point-wise analytic inference is simple; and (4) they easily accommodate experimentation with highly nonlinear and flexible specifications that may be impractical in a multivariate context. Therefore, these methods are a natural alternative to estimating impulse responses from VARs. Monte Carlo evidence and an application to a simple, closed-economy, new-Keynesian model clarify these numerous advantages.
Scope of Project
JEL Classification:
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C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
C53 Forecasting Models; Simulation Methods
E47 Money and Interest Rates: Forecasting and Simulation: Models and Applications
C32 Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
C53 Forecasting Models; Simulation Methods
E47 Money and Interest Rates: Forecasting and Simulation: Models and Applications
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