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Koenker, Roger W. Galvao, Antonio Fialho, Jr 2015-09-25T22:47:32Z 2015-09-25T22:47:32Z 10000-01-01 2009 2009 The third chapter develops penalized quantile regression methods for dynamic panel data with fixed effects. We consider a penalized strategy designed to improve the properties of the dynamic panel data quantile regression instrumental variables estimator. The penalty involves l1 shrinkage of the fixed effects. We discuss a tuning parameter selector based on the Schwartz information criterion, and propose a bootstrap resampling procedure for constructing confidence intervals for the parameters of interest. Monte Carlo simulations illustrate the dramatic improvement in the performance of the proposed estimator compared with the fixed effects quantile regression instrumental variables estimator. Finally, we provide an application to the partial adjustment toward target capital structures. The results show evidence that there is substantial heterogeneity in the speed of adjustment among firms. Made available in DSpace on 2015-09-25T22:47:32Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3392019.pdf: 1455395 bytes, checksum: b101c7083672a7b251334c142aeba28f (MD5) Previous issue date: 2009 Embargo set by: Seth Robbins for item 86879 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 81 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009. http://hdl.handle.net/2142/85598 (MiAaPQ)AAI3392019 eng Economics, Theory Essays on Quantile Regression for Dynamic Panel Data Models text Economics Economics University of Illinois at Urbana-Champaign Dissertation Ph.D.