nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒09‒11
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Cointegrating Jumps: an Application to Energy Facilities By Nicola Cufaro Petroni; Piergiacomo Sabino
  2. Steady-state priors and Bayesian variable selection in VAR forecasting By Dimitrios P. Louzis
  3. Structural Analysis with Multivariate Autoregressive Index Models By Carreiro, Andrea; Kapetanios, George; Marcellino, Massimiliano
  4. Quasifiltering for time-series modeling By Tsyplakov, Alexander

  1. By: Nicola Cufaro Petroni; Piergiacomo Sabino
    Abstract: Based on the concept of self-decomposable 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 self-decomposability 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 mean-reverting Geometric Ornstein-Uhlenbeck 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 non-arbitrage conditions. In particular we can find close-form 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
  2. 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 steady-state of the system. We show that extant Gibbs sampling methods for Bayesian variable selection can be efficiently extended to incorporate prior beliefs on the steady-state of the economy. Empirical analysis, based on three major US macroeconomic time series, indicates that the out-of-sample forecasting accuracy of a VAR model is considerably improved when it combines both variable selection and steady-state prior information.
    Keywords: Bayesian VAR, Steady states, Variable selection, Macroeconomic forecasting
    JEL: C32
    Date: 2015–07
  3. 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
  4. By: Tsyplakov, Alexander
    Abstract: In the paper a method for constructing new varieties of time-series models is proposed. The idea is to start from an unobserved components model in a state-space form and use it as an inspiration for development of another time-series model, in which time-varying underlying variables are directly observed. The goal is to replace a state-space 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 state-space model is linear Gaussian, then the resulting model would belong to the class of score driven model (aka GAS, DCS).
    Keywords: time-series model, state-space model, score driven model
    JEL: C22 C51
    Date: 2015–07–10

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