Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors...

We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modelled as a sequence...

This paper considers the most important aspects of model uncertainty for spatial regression models, namely the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models...

We introduce the class of FIR-GARCH models in this paper. FIR-GARCH models provide a parsimonious joint model for low-frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of...

In the present paper we consider the Quasi Maximum Likelihood (QML) procedure for the estimation of stationary Stochastic Volatility models. We prove the consistency of the QML estimators and compute explicitly their asymptotic variances. This allows us to obtain also consistent estimators of...