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 proposes a methodology for default probability estimation for low default portfolios, where the statistical inference may become troublesome. The author suggests using logistic regression models with the Bayesian estimation of parameters. The piecewise logistic regression model and...

Parameter shrinkage applied optimally can always reduce error and projection variances from those of maximum likelihood estimation. Many variables that actuaries use are on numerical scales, like age or year, which require parameters at each point. Rather than shrinking these towards zero,...

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric model. We establish theoretical arguments for why...

The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as “data” in a downstream econometric model. We establish theoretical arguments for...

In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the...

Chib and Hamilton (2000) discuss Bayesian estimation of treatment response models subject to the restriction that the cross-regime correlation parameter is zero. This note points out important consequences of that restriction, and argues that the range of applicability of such an approach is...

The logistic specification has been used extensively in non-Bayesian statistics to model the dependence of discrete outcomes on the values of specified covariates. Because the likelihood function is globally weakly concave estimation by maximum likelihood is generally straightforward even in...

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.