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on Econometric Time Series |
| By: | Barbara Rossi; Tatevik Sekhposyan |
| Abstract: | We propose new methods for evaluating predictive densities that focus on the models’ actual predictive ability in finite samples. The tests offer a simple way of evaluating the correct specification of predictive densities, either parametric or non-parametric. The results indicate that our tests are well sized and have good power in detecting mis-specification in predictive densities. An empirical application to the Survey of Professional Forecasters and a baseline Dynamic Stochastic General Equilibrium model shows the usefulness of our methodology. |
| Keywords: | predictive density, dynamic mis-specification, forecast evaluation |
| JEL: | C22 C52 C53 |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:758&r=ets |
| By: | Christian Francq (CREST,University Lille 3); Jean-Michel Zakoian (CREST,University Lille 3) |
| Abstract: | This paper considers the statistical inference of the class of asymmetric power-transformed GARCH(1,1) models in presence of possible explosiveness. We study the explosive behavior of volatility when the strict stationarity condition is not met. This allows us to establish the asymptotic normality of the quasi-maximum likelihood estimator (QMLE) of the parameter, including the power but without the intercept, when strict stationarity does not hold. Two important issues can be tested in this framework: asymmetry and stationarity. The tests exploit the existence of a universal estimator of the asymptotic covariance matrix of the QMLE. By establishing the local asymptotic normality (LAN) property in this nonstationary framework, we can also study optimality issues |
| Keywords: | GARCH models, Inconsistency of estimators, Local power of tests, Non stationarity, Quasi Maximum Likelihood estimation |
| Date: | 2013–08 |
| URL: | http://d.repec.org/n?u=RePEc:crs:wpaper:2013-11&r=ets |
| By: | Tsunehiro Ishihara (Department of Economics, Hitotsubashi University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo); Manabu Asai (Faculty of Economics, Soka University,) |
| Abstract: | 　　 A multivariate stochastic volatility model with the dynamic correlation and the cross leverage effect is described and its efficient estimation method using Markov chain Monte Carlo is proposed. The time-varying covariance matrices are guaranteed to be positive definite by using a matrix exponential transformation. Of particular inter- est is our approach for sampling a set of latent matrix logarithm variables from their conditional posterior distribution, where we construct the proposal density based on an approximating linear Gaussian state space model. The proposed model and its ex- tended models with fat-tailed error distribution are applied to trivariate returns data (daily stocks, bonds, and exchange rates) of Japan. Further, a model comparison is conducted including constant correlation multivariate stochastic volatility models with leverage and diagonal multivariate GARCH models. |
| Date: | 2014–05 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2014cf932&r=ets |