We conduct both an approximate Bayesian Model Averaging (BMA) and an exact Bayesian analysis to incorporate break date uncertainty of the mean growth rate into the trend-cycle decomposition of U.S. real GDP. Our results suggest a structural break in mean growth rate of U.S. real GDP in 1970s. Comparing to the models assuming fixed break date, we find higher uncertainty in the posterior density ...

I use parametric and semiparametric methods to test for the order of integration in stock market indexes. The results, which are based on the EOE (Amsterdam), DAX (Frankfurt), Hang Seng (Hong Kong), FTSE100 (London), S&P500 (New York), CAC40 (Paris), Singapore All Shares, and the Japanese Nikkei, show that in almost all of the series the unit root hypothesis cannot be rejected. The Hang Seng an...

This paper examines the hypothesis that both stock returns and volatility are asymmetrical functions of past information from the US market. By employing a double-threshold GARCH model to investigate six major index-return series, we find strong evidence supporting the asymmetrical hypothesis of stock returns. Specifically, negative news from the US market will cause a larger decline in a natio...

Consider a non-standard parametric estimation problem, such as the estimation of the AR(1) coefficient close to the unit root. We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We demonstrate the usefulness of our generic approach by also applying it ...

This paper provides a comprehensive analysis of the short-term interest-rate dynamics based on three different data sets and two flexible parametric specifications. The significance of nonlinearity in the short-rate drift declines with increasing maturity for the interest-rate series used in the study. Using a flexible diffusion specification and incorporating GARCH volatility and non-normal in...

Stable autoregressive models are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution...

Stable autoregressive models are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution...

The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is show...

We study the impact of nine categories of scheduled and unscheduled news announcements on the euro/ dollar return volatility. We highlight and analyze the pre-announcement, contemporaneous and postannouncement reactions. Using high-frequency intraday data and within the framework of ARCH-type models, we show that volatility increases in the pre-announcement periods, particularly before schedule...

This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, we construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2...