nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒08‒27
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. A New Approach to Drawing States in State Space Models By William J. McCausland; Shirley Miller; Denis Pelletier
  2. Forecasting realized volatility models:the benefits of bagging and nonlinear specifications By Eric Hillebrand; Marcelo Cunha Medeiros

  1. By: William J. McCausland (Département de sciences économiques, Université de Montréal); Shirley Miller (Département de sciences économiques, Université de Montréal); Denis Pelletier (Department of Economics, North Carolina State University)
    Abstract: We introduce a new method for drawing state variables in Gaussian state space models from their conditional distribution given parameters and observations. Unlike standard methods, our method does not involve Kalman filtering. We show that for some important cases, our method is computationally more efficient than standard methods in the literature. We consider two applications of our method.
    Keywords: State space models, Stochastic volatility, Count data
    JEL: C11 C13 C15 C32 C63
    Date: 2007–08
  2. By: Eric Hillebrand (DEPARTMENT OF ECONOMICS, LOUISIANA STATE UNIVERSITY); Marcelo Cunha Medeiros (Department of Economics, PUC-Rio)
    Abstract: We forecast daily realized volatilities with linear and nonlinear models and evaluate the benefits of bootstrap aggregation (bagging) in producing more precise forecasts. We consider the linear autoregressive (AR) model, the Heterogeneous Autoregressive model (HAR), and a non-linear HAR model based on a neural network specification that allows for logistic transition effects (NNHAR). The models and the bagging schemes are applied to the realized volatility time series of the S&P500 index from 3-Jan-2000 through 30-Dec-2005. Our main findings are: (1) For the HAR model, bagging successfully averages over the randomness of variable selection; however, when the NN model is considered, there is no clear benefit from using bagging; (2) including past returns in the models improves the forecast precision; and (3) the NNHAR model outperforms the linear alternatives.
    Date: 2007–08

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