nep-for New Economics Papers
on Forecasting
Issue of 2010‒12‒23
three papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches By de Silva, Ashton J
  2. VAR Forecasting Using Bayesian Variable Selection By Dimitris Korobilis
  3. Dynamic Conditional Correlations for Asymmetric Processes By Manabu Asai; Michael McAleer

  1. By: de Silva, Ashton J
    Abstract: Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data. In addition new multivariate specifications are outlined and demonstrated to be accurate.
    Keywords: exponential smoothing; state space models; multivariate time series; macroeconomic variables
    JEL: C32 C53 E17
    Date: 2010–12–13
  2. By: Dimitris Korobilis (Université Catholique de Louvain; The Rimini Centre for Economic Analysis (RCEA))
    Abstract: This paper develops methods for automatic selection of variables in forecasting Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally efficient algorithms for stochastic variable selection in generic (linear and nonlinear) VARs. The performance of the proposed variable selection method is assessed in a small Monte Carlo experiment, and in forecasting 4 macroeconomic series of the UK using time-varying parameters vector autoregressions (TVP-VARs). Restricted models consistently improve upon their unrestricted counterparts in forecasting, showing the merits of variable selection in selecting parsimonious models.
    Keywords: Forecasting; variable selection; time-varying parameters; Bayesian
    JEL: C11 C32 C52 C53 E37
    Date: 2010–01
  3. By: Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)
    Abstract: The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (WDCC) model and the Matrix-Exponential Conditional Correlation (MECC) model. The paper applies the WDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents an empirical example using the trivariate data of the Nikkei 225, Hang Seng and Straits Times Indices for estimating and forecasting the WDCC-EGARCH and WDCC-GJR models, and compares the performance with the asymmetric BEKK model. The empirical results show that AIC and BIC favour the WDCC-EGARCH model to the WDCC-GJR and asymmetric BEKK models. Moreover, the empirical results indicate that the WDCC-EGARCH-t model produces reasonable VaR threshold forecasts, which are very close to the nominal 1% to 3% values.
    Keywords: Dynamic conditional correlations, Matrix exponential model, Wishart process, EGARCH, GJR, asymmetric BEKK, heavy-tailed errors.
    Date: 2010–12

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