In this paper we provide tests for the unit root hypothesis against the occurence of an unspecified number of breaks which may be larger than 2 but smaller that the maximum allowed number of breaks, <i>m</i>, in univariate time series models. The advocated procedure is considerably less computationally...

This paper presents an invariance principle for highly nonstationary long memory processes, defined as processes with long memory parameter lying in (1, 1.5). This principle provides the tools for showing asymptotic validity of the bootstrap in the context of such processes.

Most work in the area of nonlinear econometric modelling is based on a single equation and assumes exogeneity of the explanatory variables. Recently, work by Caner and Hansen (2003) and Psaradakis, Sola, and Spagnolo (2004) has considered the possibility of estimating nonlinear models by methods...

This paper presents a new model of stochastic volatility which allows for infrequent shifts in the mean of volatility, known as structural breaks. These are endogenously driven from large innovations in stock returns arriving in the market. The model has a number of interesting properties. Among...

This paper introduces a new long memory volatility process, denoted by Adaptive <i>FIGARCH</i>, or <i>A-FIGARCH</i>, which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing the intercept to follow a slowly varying...

This paper introduces a new model of structural breaks which assumes that structural breaks are driven by large economic shocks. The model specifies that both the timing and size of breaks are stochastic and it can be used to investigate the impact of large economic shocks on the stability of...

The paper provides a proof of consistency of the ridge estimator for regressions where the number of regressors tends to infinity. Such result is obtained without assuming a factor structure. A Monte Carlo study suggests that shrinkage autoregressive models can lead to very substantial...