nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒03‒22
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Short-term forecasting with mixed-frequency data: A MIDASSO approach By Boriss Siliverstovs
  2. Beyond location and dispersion models: The Generalized Structural Time Series Model with Applications By Djennad, Abdelmajid; Rigby, Robert; Stasinopoulos, Dimitrios; Voudouris, Vlasios; Eilers, Paul
  3. The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling By Joshua C.C. Chan

  1. By: Boriss Siliverstovs (KOF Swiss Economic Institute, ETH Zurich, Switzerland)
    Abstract: In this paper we extend the targeted-regressor approach suggested in Bai and Ng (2008) for variables sampled at the same frequency to mixed-frequency data. Our MIDASSO approach is a combination of the unrestricted MIxed-frequency DAta-Sampling approach (U-MIDAS) (see Foroni et al., 2015; Castle et al., 2009; Bec and Mogliani, 2013), and the LASSO-type penalised regression used in Bai and Ng (2008), called the elastic net (Zou and Hastie, 2005). We illustrate our approach by forecasting the quarterly real GDP growth rate in Switzerland.
    Keywords: LASSO, Switzerland, Forecasting, Real-time data, MIDAS
    JEL: C22 C53
    Date: 2015–03
  2. By: Djennad, Abdelmajid; Rigby, Robert; Stasinopoulos, Dimitrios; Voudouris, Vlasios; Eilers, Paul
    Abstract: In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of time-varying conditional skewness and kurtosis is a challenging task. We propose a class of parameter-driven time series models referred to as the generalized structural time series (GEST) model. The GEST model extends Gaussian structural time series models by a) allowing the distribution of the dependent variable to come from any parametric distribution, including highly skewed and kurtotic distributions (and mixed distributions) and b) expanding the systematic part of parameter-driven time series models to allow the joint and explicit modelling of all the distribution parameters as structural terms and (smoothed) functions of independent variables. The paper makes an applied contribution in the development of a fast local estimation algorithm for the evaluation of a penalised likelihood function to update the distribution parameters over time \textit{without} the need for evaluation of a high-dimensional integral based on simulation methods.
    Keywords: non-Gaussian parameter-driven time series, fast local estimation algorithm, time-varying skewness, time-varying kurtosis
    JEL: C14 C46 C53 C58 G17
    Date: 2015–03–12
  3. By: Joshua C.C. Chan
    Abstract: This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance, and its coefficient in the former is time-varying. We develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter to estimate this more general variant. We illustrate the methodology with an application that involves US, UK and Germany inflation. The estimation results show substantial time-variation in the coefficient associated with the volatility, high-lighting the empirical relevance of the proposed extension. Moreover, in a pseudo out-of-sample forecasting exercise, the proposed variant also forecasts better than various standard benchmarks.
    Keywords: nonlinear, state space, inflation forecasting, inflation uncertainty
    JEL: C11 C15 C53 C58 E31
    Date: 2015–03

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