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on Econometric Time Series |
| By: | Mark J Jensen; John M Maheu |
| Abstract: | This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. The new model is assessed based on simulation evidence, an empirical example, and comparison to parametric models. |
| Keywords: | Dirichlet process mixture, MCMC, block sampler |
| JEL: | C22 C11 |
| Date: | 2008–04–25 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-314&r=ets |
| By: | Yamin Ahmad (Department of Economics, University of Wisconsin - Whitewater) |
| Abstract: | This paper investigates the properties of a class of models which incorporate nonlinear dynamics, known as Threshold Autoregressive (TAR) models. Simulations show that within the context of the real exchange rate literature, a threshold model of exchange rates exhibits significant small sample bias even with long time series data. The results of this paper has severe implications for the properties of estimated coefficients within TAR models. |
| Keywords: | Threshold Autoregressive Models, Nonlinear Models, Small Sample Bias, Real Exchange Rates, Simulation |
| JEL: | F47 C15 C32 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:uww:wpaper:07-01&r=ets |