Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247560 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-26
Publisher: 
MDPI, Basel
Abstract: 
A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect Inference, in the context of Autoregressive Moving Average (ARMA) processes for finite samples. Resulting objective functions are treated as test statistics, which are inverted rather than optimized, via the Monte Carlo test method. Simulation studies illustrate accurate size and good power. Projected impulse-response confidence bands are simultaneous by construction and exhibit robustness to parameter identification problems. The persistence of shocks on oil prices and returns is analyzed via impulse-response confidence bands. Our findings support the usefulness of impulse-responses as an empirically relevant transformation of the confidence set.
Subjects: 
ARMA
Impulse-Response
Indirect Inference
Monte Carlo test
Root Cancelation
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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