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Jackknife and analytical bias reduction for nonlinear panel models Author info | Abstract | Publisher info | Download info | Related research | Statistics Jinyong Hahn
Whitney Newey () (Institute for Fiscal Studies and Massachusetts Institute of Technology)
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Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed effects, as well as model parameters. We find large bias reductions from using these approaches in examples. We consider asymptotics where T grows with n, as an approximation to the properties of the estimators in econometric applications. We show that if T grows at the same rate as n the fixed effects estimator is asymptotically biased, so that asymptotic confidence intervals are incorrect, but that they are correct for the panel jackknife. We show T growing faster than n1/3 suffices for correctness of the analytic correction, a property we also conjecture for the jackknife.
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number
CWP17/03.
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Length: 20 pp.
Date of creation: Oct 2003Date of revision:
Handle: RePEc:ifs:cemmap:17/03Contact details of provider: Postal: The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE Phone: (+44) 020 7291 4800 Fax: (+44) 020 7323 4780 Email: Web page: http://cemmap.ifs.org.uk
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile , click on "citations" and make appropriate adjustments.: Jinyong Hahn & Guido Kuersteiner, 2002.
"Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large ,"
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