Arbeitspapier

Empirical Likelihood Block Bootstrapping

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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.

Language: Englisch

Bibliographic citation: Series: Queen's Economics Department Working Paper ; No. 1156

Classification: Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

Subject: generalized methods of moments
empirical likelihood
block-bootstrap
Momentenmethode
Bootstrap-Verfahren
Monte-Carlo-Simulation

Event: Geistige Schöpfung

(who): Allen, Jason
Gregory, Allan W.
Shimotsu, Katsumi

Event: Veröffentlichung

(who): Queen's University, Department of Economics

(where): Kingston (Ontario)

(when): 2008

Handle: http://hdl.handle.net/10419/189432

Last update: 10.03.2025, 11:42 AM CET

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Object type

Arbeitspapier

Associated

Allen, Jason
Gregory, Allan W.
Shimotsu, Katsumi
Queen's University, Department of Economics

Time of origin

2008

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