Qualitative versus quantitative external information for proxy vector autoregressive analysis

Boer, Lukas; Lütkepohl, Helmut

doi:10.1016/j.jedc.2021.104118

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…To the best of our knowledge, this is the first article to explore whether information about structural shocks should be used to impose NR in an otherwise set-identified SVAR or to construct a proxy for use in a proxy SVAR. Boer and Lütkepohl ( 2021 ) compare the efficiency of proxy SVAR estimators that use only the signs of structural shocks on particular dates against proxies that also use information about the size of the shock (i.e., standard proxies). Based on Monte Carlo simulations, they conclude that the estimator that uses information only about the sign of the shock is nearly as (or, in some cases, more) efficient than the estimator based on the quantitative information.…”

Section: Introductionmentioning

confidence: 99%

Narrative Restrictions and Proxies

Kitagawa

Read

2022

Journal of Business & Economic Statistics

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We compare two approaches to using information about the signs of structural shocks at specific dates within a structural vector autoregression (SVAR): imposing “narrative restrictions” (NR) on the shock signs in an otherwise set-identified SVAR; and casting the information about the shock signs as a discrete-valued “narrative proxy” (NP) to point-identify the impulse responses. The NP is likely to be “weak” given that the sign of the shock is typically known in a small number of periods, in which case the weak-proxy robust confidence intervals in Montiel Olea, Stock, and Watson are the natural approach to conducting inference. However, we show both theoretically and via Monte Carlo simulations that these confidence intervals have distorted coverage—which may be higher or lower than the nominal level—unless the sign of the shock is known in a large number of periods. Regarding the NR approach, we show that the prior-robust Bayesian credible intervals from Giacomini, Kitagawa, and Read deliver coverage exceeding the nominal level, but which converges toward the nominal level as the number of NR increases.

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Section: Introductionmentioning

confidence: 99%

Narrative Restrictions and Proxies

Kitagawa

Read

2022

Journal of Business & Economic Statistics

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Exogenous oil supply shocks and global agricultural commodity prices: The role of biofuels

Wei,

Qiu,

et al. 2024

International Review of Economics & Finance

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Identification of SVAR Models by Combining Sign Restrictions With External Instruments

Braun

Brüggemann

2022

Journal of Business & Economic Statistics

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We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further orthogonal shocks, or as an additional information on the shocks identified by the external instruments. In the latter case, the additional restrictions may be overidentifying and checked against the data. In a second setting, we assume that proxy variables are only 'plausibly exogenous'. In this case, various inequality restrictions based e.g. on correlations or variance contributions can be used for set-identification. This can be combined with conventional sign restrictions to further narrow down the set of admissible models. For our B-model type Proxy SVAR setup, we develop Bayesian inference and discuss the computation of Bayes factors to check overidentifying restrictions. We illustrate the usefulness of our methodology in estimating the effects of oil market and monetary policy shocks.

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Qualitative versus quantitative external information for proxy vector autoregressive analysis

Cited by 6 publications

References 27 publications

Narrative Restrictions and Proxies

Narrative Restrictions and Proxies

Exogenous oil supply shocks and global agricultural commodity prices: The role of biofuels

Identification of SVAR Models by Combining Sign Restrictions With External Instruments

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