A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. The method conveniently deals with the path dependence problem that arises in these types of models. The performance of the method is shown to work well us...

The theme of unit roots in macroeconomic time series have received a great amount of attention in terms of theoretical and applied research over the last three decades. Since the seminal work by Nelson and Plosser (1982), testing for the presence of a unit root in the time series data has become a topic of great concern. This issue gained further momentum with Perron’s 1989 paper which emphasiz...

This paper uses newly available long-span data on real per capita incomes from 1900-2001 to test for stochastic convergence in a diverse group of 29 countries. To perform our tests, we utilize the two-break LM unit root test of Lee and Strazicich (2003) and endogenously determine two distinct structural breaks in level and trend for each country. Despite including both OECD and non-OECD countri...

We present the results of numerical simulations of the modal properties of Photonic Band Gap Fibers (PBGFs) in which a structural distortion of the silica ring surrounding the air core is gradually introduced. We demonstrate that surface modes supported within such fibers are very sensitive to structural distortions, and that any asymmetric change in the structure can break their degeneracy res...

This paper extends the distributional theory for the problem of testing for structural change in the linear model when the timing of the change is unknown, and proposes a simple method of obtaining approximate critical values for the mean-Wald test. The results apply for a very wide range of regressor types, including integrated and trending regressors, and regressors that exhibit their own str...

We propose a flexible model that is able to simultaneously approximate long memory behavior as well as incorporate structural breaks in the model parameters. Our model is an extension of the heterogeneous autoregressive (HAR) model, which is designed to model and forecast volatility of financial time series. In an extensive empirical evaluation involving several volatility series, we demonstrat...