nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒04‒04
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Bootstrap prediction intervals in State Space models By Alejandro Rodriguez; Esther Ruiz
  2. A Review of Forecasting Techniques for Large Data Sets By Jana Eklund; George Kapetanios
  3. Volatility Threshold Dynamic Conditional Correlations: An International Analysis By Maria Kasch; Massimiliano Caporin
  4. Negative Volatility Spillovers in the Unrestricted ECCC-GARCH Model By Christian Conrad; Menelaos Karanasos

  1. By: Alejandro Rodriguez; Esther Ruiz
    Abstract: Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccurate. To overcome these drawbacks, Wall and Stoffer (2002) propose to obtain prediction intervals by using a bootstrap procedure that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. The bootstrap procedure proposed by Wall and Stoffer (2002) is further complicated by fact that the intervals are obtained for the prediction errors instead of for the observations. In this paper, we propose a bootstrap procedure for constructing prediction intervals in State Space models that does not need the backward representation of the model and is based on obtaining the intervals directly for the observations. Therefore, its application is much simpler, without loosing the good behavior of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer (2002) procedures for the Local Level Model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series.
    Keywords: Backward representation, Kalman filter, Local Level Model, Unobserved Components
    Date: 2008–03
  2. By: Jana Eklund (Bank of England); George Kapetanios (Queen Mary, University of London)
    Abstract: This paper provides a review which focuses on forecasting using statistical/econometric methods designed for dealing with large data sets.
    Keywords: Macroeconomic forecasting, Factor models, Forecast combination, Principal components
    JEL: C22 C53 E37 E47
    Date: 2008–03
  3. By: Maria Kasch (University of Bonn); Massimiliano Caporin (Università di Padova)
    Abstract: We extend the Dynamic Conditional Correlation multivariate GARCH specification to investigate the dynamic contemporaneous relationship between correlations and variances of the underlying assets. We present a generalization of the DCC model where the dynamic behavior depends on the assets variances through a threshold structure. Our purpose is to analyze the behavior of correlations in periods of high volatility. The application of the proposed specification to a sample of markets heterogeneous in the levels of their development allows the identification of market pairs whose correlations show low sensitivity to high underlying volatility.
    Keywords: dynamic correlations, thresholds, volatility thresholds, spillovers
    JEL: C50 F37 G11 G15
    Date: 2008
  4. By: Christian Conrad (KOF Swiss Economic Institute, ETH Zurich); Menelaos Karanasos (Economics and Finance, Brunel University, Uxbridge, West London)
    Abstract: This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model which allows for volatility feedback of either sign, i.e., positive or negative. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the coefficients of the model are non- negative, which is a su±cient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories.
    Keywords: Inequality constraints, multivariate GARCH processes, volatility feedback
    JEL: C32 C51 C52 C53
    Date: 2008–02

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