Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

Financial experts assume that measures the risk of financial asset returns generally have a normal distribution. Reality often shows asset returns are not normally distributed, so that the constraints and make it difficult to estimate the risk of taking the measurements. For it is necessary to...

This paper considers models with latent/discrete endogenous regressors and presents a simulation-based two-step (STS) estimator. The endogeneity is corrected by adopting a simulation-based control function approach. The first step consists of simulating of the residuals of the reduced-form...

This paper considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher-order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the...

This paper considers estimation methods and inference for linear dynamic panel data models with unit-specific heterogeneity and a short time dimension. In particular, we focus on the identification of the coefficients of time-invariant variables in a dynamic version of the Hausman and Taylor...

Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the...

In the context of an autoregressive panel data model with fixed effect, we examine the relationship between consistent parameter estimation and consistent model selection. Consistency in parameter estimation is achieved by using the tansformation of the fixed effect proposed by Lancaster (2002)....

An intensive and still growing body of research focuses on estimating a portfolio’s Value-at-Risk.Depending on both the degree of non-linearity of the instruments comprised in the portfolio and thewillingness to make restrictive assumptions on the underlying statistical distributions, a...

Exponential smooth transition autoregressive (ESTAR) models are widely used in the international finance literature, particularly for the modelling of real exchange rates. We show that the exponential function is ill-suited as a regime weighting function because of two undesirable properties....

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying,...