Monte-carlo evidence suggesting a no moment problem of the continuous updating estimator

Monte Carlo evidence is provided that suggests that the continuous updating estimator might have a moment problem. In the linear simultaneous equation model, its performance in terms of sample median and standard deviation is virtually identical to the one of the limited information maximum likelihood estimator.

Generalized empirical likelihood tests in time series models with potential identification failure

We introduce test statistics based on generalized empirical likelihood methods that can be used to test simple hypotheses involving the unknown parameter vector in moment condition time series models. The test statistics generalize those in Guggenberger and Smith (2005) from the i.i.d. to the time series context and are alternatives to those in Kleibergen (2001) and Otsu (2003). The main feature of these tests is that their empirical null rejection probabilities are not affected much by the strength or weakness of identification. More precisely, we show that the statistics are asymptotically distributed as chi—square under both classical asymptotic theory and weak instrument asymptotics of Stock and Wright (2000). A Monte Carlo study reveals that the finite—sample performance of the suggested tests is very competitive.Generalized Empirical Likelihood, Nonlinear Moment Conditions, Similar Tests, Size Distortion, Weak Identification

The Limit of Finite-Sample Size and a Problem with Subsampling

This paper considers inference based on a test statistic that has a limit distribution that is discontinuous in a nuisance parameter or the parameter of interest. The paper shows that subsample, b_nAsymptotic size, b