Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in GARCH and stochastic volatility models. Recent research has also found it useful to account for leverage in models of realized volatility, as well as in models of the continuous and jump components of ...

This article contains two novelties. First, a unified framework for testing and evaluating the adequacy of an estimated autoregressive conditional duration (ACD) model is presented. Second, two new classes of ACD models, the smooth transition ACD model and the time-varying ACD model, are introduced and their properties discussed. New misspecification tests for the ACD class of models are introd...

This paper is motivated by recent evidence that many univariate economic and …nancial time series have both nonlinear and long memory characteristics. Hence, this paper considers a general nonlinear, smooth transition regime autoregression which is embedded within a strongly dependent, long memory process. A time domainMLE with simultaneous estimation of the long memory, linear AR and nonlinear...

This paper investigates the linear and nonlinear effects of financial regulation policy uncertainty shocks on US macroeconomic aggregates within a Vector Autoregressive (VAR) framework. Financial regulation policy uncertainty (FRPU) is quantified with a news-based index developed by Baker et al. (2013). Particular attention is paid to the reaction of corporate credit spreads to FRPU shocks. The...

We present non-homogeneous Markov regression models of unknown order as a means to assess the duration of autoregressive dependence in longitudinal binary data. We describe a subject's transition probability evolving over time using logistic regression models for his or her past outcomes and covariates. When the initial values of the binary process are unknown, they are treated as latent variab...

We present two model-based methods for learning Granger causality networks for multivariate categorical time series. Our first proposal is based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of many local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The ne...

The Heston-STAR model is a new class of stochastic volatility models defined by generalizing the Heston model to allow the volatility of the volatility process as well as the correlation between asset log-returns and variance shocks to change across different regimes via smooth transition autoregressive (STAR) functions. The form of the STAR functions is very flexible, much more so than the fun...

A Bayesian model averaging procedure is presented that makes use of a …nite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the “Great Ratios” in U.S. macro-economic time series is investigated, together with the e¤ect of permanent shocks on business cycles. Second, the linear VAR model is ext...

This paper considers testing the unit root hypothesis against a smooth transition autoregressive model as the alternative. The model specification makes it possible to discriminate between nonstationary random walk and stationary nonlinear processes. Some new limit results are presented, extending earlier work, and two F type tests are proposed. Small sample simulations show some size distortio...

We describe a Bayesian projection model to produce country-specific projections of the total fertility rate (TFR) for all countries. The model decomposes the evolution of TFR into three phases: pre-transition high fertility, the fertility transition, and post-transition low fertility. The model for the fertility decline builds on the United Nations Population Division's current deterministic pr...