
on Econometric Time Series 
By:  Nicola Cufaro Petroni; Piergiacomo Sabino 
Abstract:  Based on the concept of selfdecomposable random variables we discuss the application of a model for a pair of dependent Poisson processes to energy facilities. Due to the resulting structure of the jump events we can see the selfdecomposability as a form of cointegration among jumps. In the context of energy facilities, the application of our approach to model power or gas dynamics and to evaluate transportation assets seen as spread options is straightforward. We study the applicability of our methodology first assuming a Merton market model with two underlying assets; in a second step we consider price dynamics driven by an exponential meanreverting Geometric OrnsteinUhlenbeck plus compound Poisson that are commonly used in the energy field. In this specific case we propose a price spot dynamics for each underlying that has the advantage of being treatable to find nonarbitrage conditions. In particular we can find closeform formulas for vanilla options so that the price and the Greeks of spread options can be calculated in close form using the Margrabe formula (if the strike is zero) or some other well known approximation. 
Date:  2015–09 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1509.01144&r=all 
By:  Dimitrios P. Louzis (Bank of Greece) 
Abstract:  This study proposes methods for estimating Bayesian vector autoregressions (VARs) with an automatic variable selection and an informative prior on the unconditional mean or steadystate of the system. We show that extant Gibbs sampling methods for Bayesian variable selection can be efficiently extended to incorporate prior beliefs on the steadystate of the economy. Empirical analysis, based on three major US macroeconomic time series, indicates that the outofsample forecasting accuracy of a VAR model is considerably improved when it combines both variable selection and steadystate prior information. 
Keywords:  Bayesian VAR, Steady states, Variable selection, Macroeconomic forecasting 
JEL:  C32 
Date:  2015–07 
URL:  http://d.repec.org/n?u=RePEc:bog:wpaper:195&r=all 
By:  Carreiro, Andrea; Kapetanios, George; Marcellino, Massimiliano 
Abstract:  We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely related to that associated with dynamic factor models. Next, we describe classical and Bayesian estimation of large MAIs, and discuss methods for the rank determination. Then, the theoretical analysis is extended to the case of general rank restrictions on the VAR coefficients. Finally, the performance of the MAIs is compared with that of large Bayesian VARs in the context of Monte Carlo simulations and two empirical applications, on on the transmission mechanism of monetary policy and the propagation of demand, supply and financial shocks. 
Keywords:  Bayesian VARs; factor models; forecasting; large datasets; Multivariate Autoregressive Index models; Reduced Rank Regressions; structural analysis 
JEL:  C11 C13 C33 C53 
Date:  2015–09 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:10801&r=all 
By:  Tsyplakov, Alexander 
Abstract:  In the paper a method for constructing new varieties of timeseries models is proposed. The idea is to start from an unobserved components model in a statespace form and use it as an inspiration for development of another timeseries model, in which timevarying underlying variables are directly observed. The goal is to replace a statespace model with an intractable likelihood function by another model, for which the likelihood function can be written in a closed form. If state transition equation of the parent statespace model is linear Gaussian, then the resulting model would belong to the class of score driven model (aka GAS, DCS). 
Keywords:  timeseries model, statespace model, score driven model 
JEL:  C22 C51 
Date:  2015–07–10 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:66453&r=all 