This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...

This paper introduces a new class of stochastic volatility models which allows for stochastic volatility of volatility (SVV): Volatility modulated non-Gaussian Ornstein-Uhlenbeck (VMOU) processes. Various probabilistic properties of (integrated) VMOU processes are presented. Further we study the...

This paper deals with the issues of identification and estimation in the canonical model of contagion advanced in Pesaran and Pick (2007). The model is a two-equation nonlinear simultaneous equations system with endogenous dummy variables; it also represents an extension of univariate threshold...

A factor-augmented vector auto-regressive (FAVAR) model is defined by a VAR equation that captures lead-lag correlations among a set of observed variables X and latent factors F, and a calibration equation that relates another set of observed variables Y with F and X. The latter equation is used...

This article introduces the R package ExtremeBounds to perform extreme bounds analysis (EBA), a sensitivity test that examines how robustly the dependent variable of a regression model is related to a variety of possible determinants. ExtremeBounds supports Leamer's EBA that focuses on the upper...

In this paper, we show that conditions derived under the CAPM ensure only weak exogeneity in a linear regression setting. Since strong exogeneity is not guaranteed, the OLS estimator of CAPM beta is only consistent but not necessarily unbiased. We provide empirical evidence that individual daily...

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter...

This chapter uses the marginal treatment effect (MTE) to unify and organize the econometric literature on the evaluation of social programs. The marginal treatment effect is a choice-theoretic parameter that can be interpreted as a willingness to pay parameter for persons at a margin of...

The family of Generalized Empirical Likelihood (GEL) estimators provide a number of potential advantages relative to Generalized Method of Moments (GMM) estimators. While it is well known these estimators share an asymptotic distribution, the GEL estimators may perform better in finite sample,...

We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves...