Using density forecasts, we compare the predictive performance of dur ation models that have been developed fo modelling intra-day data on stock markets. Our model portfolio encompasses the auto regressive conditional duration (ACD) model, its logarithmic version (Log-ACD), the threshold...

This paper proposes a new approach to modeling volatility changes and clustering, we use a parsimonious high-order markov chain which allows for duration dependence.

In this paper the issue of detecting and handling outliers in the GARCH(1,1) model is addressed. Simulation evidence shows that neglecting even a single outlier has a dramatic on parameter estimates. To detect and correct for outliers, we propose an adaptation of the iterative in Chen and Liu...

This paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption series.

Based on Monte Carlo simulations using both stationary and nonstationary data, a model selection approach which uses the SIC to select a "best" group of forecasts in the context of forecast combination regressions dominates a number of other techniques, including the standard t-statistic...

First reported monthly and quarterly time series data on nine macroeconomic variables from 1960-1993 are given. Features of this so called "unrevised" or "first reported data" are discussed, and the data is compared with standard "fully revised" data using Granger causality tests.

Complicated (chaotic), global, expectations-driven business cycles in two-dimensional models have been shown to involve non-trivial intersections of stable and unstable manifolds of a (periodic) saddle steady state. Whether similar phenomena may occur in other two-dimensional dynamic economic...

Long-horizon regression tests are widely used in empirical finance, despite evidence of severe size distortions. I propose a new bootstrap method for small-sample inference in long-horizon regressions. A Monte Carlo study shows that this bootstrap test greatly reduces the size distortions of...

We develop regression-based tests of hypotheses about out of sample prediction errors. Representative tests include ones for zero mean and zero correlation between a prediction error and a vector of predictors. The relevant environments are ones in which predictions depend on estimated...

A Test of forecasts rationality based on the weak efficiency of fixed-event forecasts has recently been proposed by Nordhauss (1987). This paper considers the scope for pooling fixed-event forecasts across 'events' to deliver more powerful tests of the weak-efficiency hypothesis. In an empirial...