It is a well-known property that standard GMM estimators for dynamic panel data might perform poorly in small samples. Several papers have noted this to be especially true for the estimated standard errors, which are normally biased downwards. The aim of the present paper is to compare how two recently suggested bootstrap procedures can assist inference in dynamic panel data models, when the mentioned small-sample bias is a potential problem. We do this by means of Monte Carlo experiments, forming tests using both standard errors estimated by asymptotic approximations, as well as by bootstrap procedures. The results give a fairly clear support for using bootstrap inference. Whereas the tests based on asymptotics have empirical levels that may deviate substantially from their nominal ones, the bootstrap procedures appear to perform quite well in the context of dynamic panel data estimation.
Download Info
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page
whether it is in fact available.
3. Perform a search for a similarly titled item that would be
available.
Publisher Info
Paper provided by Uppsala University, Department of Economics in its series Working Paper Series with number
1997:23.
Length: 17 pages Date of creation: 24 Aug 1998 Date of revision: Handle: RePEc:hhs:uunewp:1997_023
Contact details of provider: Postal: Department of Economics, Uppsala University, P. O. Box 513, SE-751 20 Uppsala, Sweden Phone: + 46 18 471 25 00 Fax: + 46 18 471 14 78 Email: Web page: http://www.nek.uu.se/ More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Katarina Grönvall).
Find related papers by JEL classification: C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data