We provide numerically reliable analytical expressions for the score of conditionally heteroskedastic dynamic regression models when the conditional distribution is multivariate t. We also derive one-sided and two-sided LM tests for multivariate normality versus multivariate t based on the first...

In this paper we confront sensitivity analysis with diagnostic testing. Every model is misspecified, but a model is useful if the parameters of interest (the focus) are not sensitive to small perturbations in the underlying assumptions. The study of the e ect of these violations on the focus is...

In this paper we consider the problem of frequentist model averaging for quantile regression (QR) when all the M models under investigation are potentially misspecified and the number of parameters in some or all models is diverging with the sample size n. To allow for the dependence between the...

We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the...

This paper presents a CAPM-based threshold quantile regression model with GARCH specification to examine relations between stock excess returns and “abnormal trading volume.” By employing the Bayesian MCMC method with asymmetric Laplace distribution to six daily Dow Jones Industrial stocks,...

This paper analyzes the uniformity of the property appraisal outcome for multifamily apartment complexes in Phoenix, Arizona. Specifically, it investigates vertical and horizontal equity and equity across assessment methods. Apartment transactions and assessed valuations are examined over a...

In this poper we present a consistent spacification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation ant it is shown to have similar rates of convergence to the more commonly used kernel based tests.

In this poper we present a consistent spacification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation ant it is shown to have similar rates of convergence to the more commonly used kernel based tests.

We evaluate the relative performance of logistic credit risk models that were selected by means of standard stepwise model selection methods and average" models obtained by Bayesian model averaging (BMA). Our bootstrap analysis shows that BMA should be considered as an alternative to the...