This paper studies the properties of the quasi -maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances when a normal log likelihood is maximized but the assumption of normality is violated. Because the score of the normal log likelihood has the martingale difference property under fairly general r...

This paper studies properties of the likelihood ratio (LR) tests associated with the limited information maximum likelihood (LIML) estimators in a structural form estimation when the number of instrumental variables is large. Two types of asymptotic theories are developed to approximate the distribution of the likelihood ratio (LR) statistic under the null hypothesis H0 : β = β0: a (large sampl...

Since their introduction by Engle (1982) and Bollerslev (1986), respectively, autoregressive conditional heteroscedastic (ARCH) and generalized autoregressive conditional heteroscedastic (GARCH) models have found extraordinarily wide use. The survey article by Bollerslev, Chou, and Kroner (1982) cited more than 300 papers applying ARCH, GARCH, and other closely related models. As they showed, A...

We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the parameter likelihood gradient. We show, as opposed to [1][2], that these parameter learning methods can be improved and evaluate the resulting performance employing different inference techniques. We show that the approximat...

We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call Iterative Conditional Fitting, for computing the maximum likelihood estimator of the constrained covariance matrix, under the assumption of multivariate normality. In contrast to previous approaches, this algorithm h...

Most of the empirical applications of the stochatic volatility (SV) model are based on the assumption that the conditional distribution of returns given the latent volatility process is normal. In this paper the SV model based on a conditional normal distribution is compared with SV speciications using conditional heavy-tailed distributions, especially Student's t-distribution and the generaliz...

We propose a nonparametric imputation procedure for data with missing values and establish an empirical likelihood inference for parameters defined by general estimating equations. The imputation is carried out multiple times via a nonparametric estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a...

SINGLE VERSUS MULTIPLE MARKERS ........................................................................................................ 45 DETERMINING ASSOCIATIONS BETWEEN GENETIC MARKERS AND QTL WITH TWO MARKERS ................... 45 INTERVAL MAPPING .................................................................................................................................. 50 Maximum Li...