842,185 research outputs found
Empirical Likelihood Block Bootstrapping
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.Econometric and statistical methods
Bayesian computation via empirical likelihood
Approximate Bayesian computation (ABC) has become an essential tool for the
analysis of complex stochastic models when the likelihood function is
numerically unavailable. However, the well-established statistical method of
empirical likelihood provides another route to such settings that bypasses
simulations from the model and the choices of the ABC parameters (summary
statistics, distance, tolerance), while being convergent in the number of
observations. Furthermore, bypassing model simulations may lead to significant
time savings in complex models, for instance those found in population
genetics. The BCel algorithm we develop in this paper also provides an
evaluation of its own performance through an associated effective sample size.
The method is illustrated using several examples, including estimation of
standard distributions, time series, and population genetics models.Comment: 21 pages, 12 figures, revised version of the previous version with a
new titl
Empirical Likelihood Block Bootstrapping
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.generalized methods of moments, empirical likelihood, block bootstrap
Empirical likelihood based testing for regression
Consider a random vector and let . We are interested
in testing for some known function , some compact set
IR and some function set of real valued
functions. Specific examples of this general hypothesis include testing for a
parametric regression model, a generalized linear model, a partial linear
model, a single index model, but also the selection of explanatory variables
can be considered as a special case of this hypothesis. To test this null
hypothesis, we make use of the so-called marked empirical process introduced by
\citeD and studied by \citeSt for the particular case of parametric regression,
in combination with the modern technique of empirical likelihood theory in
order to obtain a powerful testing procedure. The asymptotic validity of the
proposed test is established, and its finite sample performance is compared
with other existing tests by means of a simulation study.Comment: Published in at http://dx.doi.org/10.1214/07-EJS152 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Quasi Empirical Likelihood Estimation of Moment Condition Models
In this paper, I develop a quasi empirical likelihood estimator that has good finite-sample properties when there are many moment conditions. I show that the quasi empirical likelihood estimator, which uses semiparametric efficient estimation, is an approximation to the empirical likelihood estimator, which has been shown to have good statistical properties. The quasi empirical likelihood estimator is a consistent estimator and has a normal asymptotic distribution. As with the full-blown empirical likelihood estimator, the quasi empirical likelihood estimator reduces finite-sample bias, but is much simpler to compute than the empirical likelihood estimator. Monte Carlo experiments and a quick validation exercise confirm my theoretical resultsGMM, empirical likelihood, finite-sample bias, instrumental variables
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