nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒12‒23
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. From general State-Space to VARMAX models By José Casals Carro; Alfredo García-Hiernaux; Miguel Jerez
  2. Dynamic Conditional Correlations for Asymmetric Processes By Manabu Asai; Michael McAleer
  3. VAR Forecasting Using Bayesian Variable Selection By Dimitris Korobilis

  1. By: José Casals Carro (Departamento de Fundamentos de Análisis Económico II, Universidad Complutense de Madrid.); Alfredo García-Hiernaux (Departamento de Fundamentos de Análisis Económico II, Universidad Complutense de Madrid.); Miguel Jerez (Departamento de Fundamentos de Análisis Económico II, Universidad Complutense de Madrid.)
    Abstract: Fixed coecients State-Space and VARMAX models are equivalent, meaning that they are able to represent the same linear dynamics, being indistinguishable in terms of overall fit. However, each representation can be specifically adequate for certain uses, so it is relevant to be able to choose between them. To this end, we propose two algorithms to go from general State-Space models to VARMAX forms. The first one computes the coeficients of a standard VARMAX model under some assumptions while the second, which is more general, returns the coeficients of a VARMAX echelon. These procedures supplement the results already available in the literature allowing one to obtain the State-Space model matrices corresponding to any VARMAX. The paper also discusses some applications of these procedures by solving several theoretical and practical problems.
    Keywords: State-Space, VARMAX models, Canonical forms, Echelon.
    Date: 2010
  2. 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
  3. 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

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