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Mixed frequency structural vector auto-regressive models

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  • Claudia Foroni
  • Massimiliano Marcellino

Abstract

type="main" xml:id="rssa12120-abs-0001"> A mismatch between the timescale of a structural vector auto-regressive model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues and yield more reliable responses to shocks. The problems and possible remedy are illustrated analytically and with both simulated and actual data.

Suggested Citation

  • Claudia Foroni & Massimiliano Marcellino, 2016. "Mixed frequency structural vector auto-regressive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 403-425, February.
  • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:403-425
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
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