Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta (1998), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well-suited to rational expectations applications (and pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finitesample maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen (1992) procedure. Furthermore, we construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate yield is presented and discussed.Smooth Transition Threshold Autoregressive, Forecasting, Nonlinear Models

Contemporaneous threshold autoregressive models: estimation, testing and forecasting

This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta (1998), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. We argue that the contemporaneous model is well-suited to rational expectations applications (and pricing exercises), in that it does not require the initial regimes to be predetermined. We investigate the properties of the model and evaluate its finite-sample maximum likelihood performance. We also propose a method to determine the number of regimes based on a modified Hansen (1992) procedure. Furthermore, we construct multiple-step ahead forecasts and evaluate the forecasting performance of the model. Finally, an empirical application of the short term interest rate yield is presented and discussed. ; Earlier title: Contemporaneous threshold autoregressive models: estimation, forecasting and rational expectations applicationsRational expectations (Economic theory) ; Forecasting

State-Dependent Threshold STAR Models

In this paper we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a time-varying function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State-dependent logistic STAR and contemporaneous-threshold STAR models are introduced and discussed. These models are also used to investigate the dynamics of U.S. short-term interest rates, where the threshold is allowed to be a function of past output growth and inflation.Nonlinear autoregressive models; Smooth transition; Threshold; Interest rates.

Multivariate contemporaneous threshold autoregressive models

In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. The model is a multivariate generalization of the contemporaneous threshold autoregressive model introduced by Dueker et al. (2007). A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. The stability and distributional properties of the proposed model are investigated. The C-MSTAR model is also used to examine the relationship between US stock prices and interest rates.Time-series analysis ; Capital assets pricing model

Multivariate Contemporaneous-Threshold Autoregressive Models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific innovation covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.Nonlinear autoregressive model; Smooth transition; Stability; Threshold.

A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m^−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management

Density-Based Individual Tree Detection from Three-Dimensional Point Clouds

The use of three-dimensional point clouds in forestry is steadily increasing. Numerous algorithms to detect individual trees from point clouds and derive some fundamental inventory parameters have been proposed so far, but they usually provide higher accuracy in coniferous stands than in deciduous one. In the latter kind of stands, indeed, the tree identification is hampered by the geometrical round shape of the crowns, the interlacing branches of adjacent trees and the usual presence of understory vegetation. In an attempt to overcome these limitations, we developed an algorithm that is innovatively based on the areal point density of the three-dimensional cloud and that provides the height and coordinates of all the trees within a region of interest. In this work, we apply the algorithm to different situations, ranging from the regularly-arranged plantations to the very interlaced crowns of the naturally established stands, demonstrating how it is able to correctly detect most of the trees and recreate a map of their spatial distribution. We also test its capability to deal with relatively low point density and explore the possibility to use it to recreate time series of vegetation biomass. Finally, we discuss the algorithm’s limitations and potentialities, particularly focusing on its coupling to other existing tools to deal with a wider range of applications in forestry and land management

BRCA mutation carriers' perceptions on postmenopausal hormone therapy: An Italian study

Objective To evaluate the actual perceptions of postmenopausal hormone therapy (HT) in BRCA mutation carriers (BRCAmc) in comparison with women from the general population.Methods Questionnaire-based study of 83 BRCAmc and a control group of 89 women without a genetic mutation. Perceptions were evaluated by specific questions and Likert scales (-5-+5).Results Present and past users of HT were more frequent in the control group (p = 0.01), with a longer time of use (p = 0.03). The preferred route of administration of HT was 'oral' (54.6%). The most frequently reported adverse effect of HT was venous thrombosis (0.8), while a protective effect on bone health was reported. No noticeable beneficial effects of HT have been recognised for hot flushes (0.2) and vaginal dryness (0.1). The most frequently perceived beneficial and adverse effects of HT were not significantly different between BRCA mutation carriers and controls. The greatest oncological fear was breast cancer (1.0). The protective role of HT on colorectal cancer was not known (0.1). These oncological impacts were mostly overestimated in BRCAmc, however this was not significant. Few BRCAmc would think of taking HT after risk-reducing surgeries.Conclusions Knowledge of the effects of HT on BRCAmc is relatively poor and they are likely to overstate its negative effects and underestimate its health benefits; however, this is not significant in comparison to the general population. More and better information should be given to BRCAmc to allow them to make informed decisions about the use of HT, especially before undergoing risk-reducing surgeries