We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...

In this paper, we present a new time series model, whichdescribes self-exciting threshold autoregressive (SETAR) nonlinearityand seasonality simultaneously. The model is termed multiplicativeseasonal SETAR (SEASETAR). It can be viewed as a special case of ageneral non-multiplicativeSETAR model...

In finance, durations between successive transactions are usually modelled by the autoregressive conditional duration model based on a continuous distribution omitting frequent zero values. Zero durations can be caused by either split transactions or independent transactions. We propose a...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...