Filtering with Limited Information

Thorsten Drautzburg, Fernández-Villaverde, Jesús, Pablo Guerron and Dick Oosthuizen
Authors registered in the RePEc Author Service: Jesus Fernandez-Villaverde

No 19270, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We propose a new tool to filter non-linear dynamic models that does not require the researcher to specify the model fully and can be implemented without solving the model. If two conditions are satisfied, we can use a flexible statistical model and a known measurement equation to back out the hidden states of the dynamic model. The first condition is that the state is sufficiently volatile or persistent to be recoverable. The second condition requires the possibly non-linear measurement to be sufficiently smooth and to map uniquely to the state absent measurement error. We illustrate the method through various simulation studies and an empirical application to a sudden stops model applied to Mexican data.

Date: 2024-07
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP19270 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Working Paper: Filtering with Limited Information (2024)
Working Paper: Filtering with Limited Information (2024)
Working Paper: Filtering with Limited Information (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:19270

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP19270

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().