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Panels with Nonstationary Multifactor Error Structures

Author: George Kapetanios; M Hashem Pesaran; Takashi Yamagata
Publisher: München : CESifo, Center for Economic Studies & Ifo Institute for economic research, 2006.
Series: CESifo working paper series, 1788.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: George Kapetanios; M Hashem Pesaran; Takashi Yamagata
OCLC Number: 429414450
Description: 1 online resource (Text.)
Series Title: CESifo working paper series, 1788.
Responsibility: George Kapetanios, M. Hashem Pesaran, Takashi Yamagata.

Abstract:

The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.

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