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A Bootstrap Procedure for Panel Datasets with Many Cross-Sectional Units

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    This paper considers the issue of bootstrap resampling in panel datasets. The availability of datasets with large temporal and cross sectional dimensions suggests the possibility of new resampling schemes. We suggest one possibility which has not been widely explored in the literature. It amounts to constructing bootstrap samples by resampling whole cross sectional units with replacement. In cases where the data do not exhibit cross sectional dependence but exhibit temporal dependence, such a resampling scheme is of great interest as it allows the application of i.i.d. bootstrap resampling rather than block bootstrap resampling. It is well known that the former enables superior approximation to distributions of statistics compared to the latter. We prove that the bootstrap based on cross sectional resampling provides asymptotic refinements. A Monte Carlo study illustrates the superior properties of the new resampling scheme compared to the block bootstrap.

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    File URL: http://www.econ.qmul.ac.uk/papers/doc/wp523.pdf
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    Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 523.

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    Date of creation: Oct 2004
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    Handle: RePEc:qmw:qmwecw:wp523
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    1. repec:cup:cbooks:9780521818551 is not listed on IDEAS
    2. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    3. repec:cup:cbooks:9780521522717 is not listed on IDEAS
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