A Bayesian non-parametric approach for efficient risk management is proposed. A dynamic model is considered where optimal portfolio weights and hedging ratios are adjusted at each period. The covariance matrix of the returns is described using an asymmetric MGARCH model. Restrictive parametric assumptions for the errors are avoided by relying on Bayesian nonparametric methods, which allow for a...

This paper studies transmission of international energy price shocks to various sectors in the Australian stock market. We take the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to modeling volatility and gather evidence that energy price shocks transmit to the price indices of various sectors classified by the global industry classification standard (...

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, t...

This paper studies a panel data regression setting, where a break occurs at a unknown common date. In this paper, we establish the consistency and rate of convergence of the change point estimator. The break date can be estimated consistently both in …xed time horizon and large panels, which indicates that the structural change can be well detected even in short panels. Furthermore, the limitin...

This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this exten...

T.W. Anderson and his collaborators have made seminal contributions to inference with instrumental variables and to dynamic panel data models. We review these contributions and the extensive economic and statistical literature that these contributions spawned. We describe our recent work in these two areas, presenting new approaches to (a) making valid inferences in the presence of weak instrum...

Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that a¤ect each individual unit di¤erently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coe¢ cient estimates if they are correlated with the explanatory variables. Moreover, if the proces...

splm is an R package for estimating and testing various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated by application to Munnel (1990) data on 48 US States...

Article history: Received 2 February 2012 Received in revised form 12 September 2012 Accepted 17 September 2012 Available online 24 September 2012 Dynamic panel models play a natural role in several important areas of corporate finance, but the combination of fixed effects and lagged dependent variables introduces serious econometric bias. Several methods of counteracting these biases are avail...