EconStor >
Bank of Canada, Ottawa >
Bank of Canada Working Papers >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/53924
  
Title:Empirical likelihood block bootstrapping PDF Logo
Authors:Allen, Jason
Gregory, Allan W.
Shimotsu, Katsumi
Issue Date:2008
Series/Report no.:Bank of Canada Working Paper 2008,18
Abstract:Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.
Subjects:Econometric and statistical methods
JEL:C14
C22
Document Type:Working Paper
Appears in Collections:Bank of Canada Working Papers

Files in This Item:
File Description SizeFormat
568420845.pdf240.9 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/53924

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.