Estimating Orthogonal Impulse Responses via Vector Autoregressive Models

Lütkepohl, Helmut; Poskitt, D. S.

doi:10.1017/s0266466600004722

Cited by 55 publications

(41 citation statements)

References 23 publications

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“…The proof is a direct result of proposition 15.4 in Lütkepohl (2005) and is mostly based on results by Lewis and Reinsel (1985) and Lütkepohl and Poskitt (1991).…”

Section: Propositionsmentioning

confidence: 99%

Simultaneous Confidence Regions for Impulse Responses

Jordà

2009

Review of Economics and Statistics

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Inference about an impulse response is a multiple testing problem with serially correlated coefficient estimates. This paper provides a method to construct simultaneous confidence regions for impulse responses to evaluate uncertainty about the shape of the impulse response; and conditional bands to examine individual significance levels of impulse response coefficients given propagation trajectories. The paper also shows how to constrain a subset of impulse response paths to anchor structural identification of the system; and how to formally test for the validity of such identifying constraints. Simulation and empirical evidence illustrate the new techniques. A broad summary of asymptotic results and simple formulas for a impulse response estimators based on VARs and local projections are provided to make the methods easily implementable with commonly available statistical software.

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“…The proof is a direct result of proposition 15.4 in Lütkepohl (2005) and is mostly based on results by Lewis and Reinsel (1985) and Lütkepohl and Poskitt (1991).…”

Section: Propositionsmentioning

confidence: 99%

Simultaneous Confidence Regions for Impulse Responses

Jordà

2009

Review of Economics and Statistics

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“…Then, a summary of results shown by Lewis and Reinsel (1985), Lütkepohl and Poskitt (1991) and Jordà and Kozicki (2007) are contained in the following corollary.…”

Section: Asymptotic Distribution Of the Forecast Pathmentioning

confidence: 99%

Path forecast evaluation

Jordà

Marcellino

2010

J of Applied Econometrics

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A forecast path refers to the vector of forecasts over the next 1 to h periods into the future. These forecasts are correlated across horizons so that to properly understand the uncertainty associated with the forecast path, one requires the joint predictive density of the path rather than the collection of marginal predictive densities for each horizon. This paper derives the joint predictive density for forecasts generated by VARs or from direct forecast methods (e.g. Marcellino, Stock and Watson, 2003). Given this density, we introduce the mean square forecast path metric to compare the predictive ability between competing models and appropriately modify Diebold-Mariano-West and GiacominiWhite predictive ability tests. We then use Scheffé's S-method to construct simultaneous confidence regions for the forecast path and show how to construct path forecasts conditional on assumed paths for a subset of the system's variables, along with their conditional predictive density and a test on the assumed path's likelihood.JEL Classification Codes: C32, C52, C53

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“…Of course the finite-sample performance of the CMD estimator will deteriorate. 24 The asymptotic normality of structural impulse responses estimators in Assumption 2 is a high-level assumption, and follows from arguments similar to those in Lewis and Reinsel (1985) and Lütkepohl and Poskitt (1991 …”

Section: Discussionmentioning

confidence: 98%

Information Criteria for Impulse Response Function Matching Estimation of DSGE Models

et al. 2008

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We propose a new Information Criterion for Impulse Response Function Matching estimators of the structural parameters of macroeconomic models. The main advantage of our procedure is that it allows the researcher to select the impulse responses that are most informative about the deep parameters, therefore reducing the bias and improving the efficiency of the estimates of the model's parameters. We show that our method substantially changes key parameter estimates of representative Dynamic Stochastic General Equilibrium models, thus reconciling their empirical results with the existing literature. Our criterion is general enough to apply to impulse responses estimated by VARs, local projections, as well as simulation methods.J.E.L. Codes: C32, E47, C52, C53.Acknowledgements. We thank Craig Burnside for sharing his codes and for many useful suggestions along the way. We also thank L. Christiano for making the Altig et al. (2004) codes available in his webpage. We gratefully acknowledge comments from

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Estimating Orthogonal Impulse Responses via Vector Autoregressive Models

Cited by 55 publications

References 23 publications

Simultaneous Confidence Regions for Impulse Responses

Simultaneous Confidence Regions for Impulse Responses

Path forecast evaluation

Information Criteria for Impulse Response Function Matching Estimation of DSGE Models

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