nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒06‒28
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models By Markku Lanne; Henri Nyberg
  2. Dynamic State-Space Models By Karapanagiotidis, Paul
  3. It's all about volatility of volatility: evidence from a two-factor stochastic volatility model By Stefano Grassi; Paolo Santucci de Magistris
  4. Forecasting with the Standardized Self-Perturbed Kalman Filter By Stefano Grassi; Nima Nonejad; Paolo Santucci de Magistris

  1. By: Markku Lanne (University of Helsinki and CREATES); Henri Nyberg (University of Helsinki)
    Abstract: We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of the new decomposition is illustrated with an empirical application to U.S. output growth and interest rate spread data.
    Keywords: Forecast error variance decomposition, generalized impulse response function, output growth, term spread
    JEL: C13 C32 C53
    Date: 2014–05–19
  2. By: Karapanagiotidis, Paul
    Abstract: A review of the general state-space modeling framework. The discussion focuses heavily on the three prediction problems of forecasting, filtering, and smoothing within the state- space context. Numerous examples are provided detailing special cases of the state-space model and its use in solving a number of modeling issues. Independent sections are also devoted to both the topics of Factor models and Harvey’s Unobserved Components framework.
    Keywords: state-space models, signal extraction, unobserved components
    JEL: C10 C32 C51 C53 C58
    Date: 2014–06–03
  3. By: Stefano Grassi; Paolo Santucci de Magistris
    Abstract: The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential matching procedure which adopts as auxiliary model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent financial crisis the relative weight of the daily component dominates over the monthly term. The estimates of the two factor stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial crisis is due to the increase in the volatility of the persistent volatility term. A set of Monte Carlo simulations highlights th correctness of the methodology adopted to extract the variability in the parameters.
    Keywords: Time-Varying Parameters; On-line Kalman Filter; Simulation-based inference; Predictive Likelihood; Volatility Factors
    JEL: G01 C00 C11 C58
    Date: 2013–11
  4. By: Stefano Grassi; Nima Nonejad; Paolo Santucci de Magistris
    Abstract: A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding the calibration of a design parameter. The standardization leads to a better tracking of the dynamics of the parameters compared to other on-line methods, especially as the level of noise increases. The proposed estimation method, coupled with dynamic model averaging and selection, is adopted to forecast S&P 500 realized volatility series with a time-varying parameters HAR model with exogenous variables.
    Keywords: TVP models; Self-Perturbed Kalman Filter; Dynamic Model Averaging; Dynamic Model Selection; Forecasting; Realized Variance
    JEL: C10 C11 C22 C80
    Date: 2014–02

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