Bayesian analysis of endogenous delay threshold models

We develop Bayesian methods of analysis for a new class of threshold autoregressive models: endogenous delay threshold. We apply our methods to the commonly used sunspot data set and find strong evidence in favor of the Endogenous Delay Threshold Autoregressive (EDTAR) model over linear and traditional threshold autoregressions

Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression

Theories often make predictions about the signs of the effects of economic shocks on observable variables, thus implying inequality constraints on the parameters of a structural vector autoregression (SVAR). We introduce a new Bayesian procedure to evaluate the probabilities of such constraints, and, hence, to validate the theoretically implied economic shocks. We first estimate a SVAR, where the shocks are identified by statistical properties of the data, and subsequently label these statistically identified shocks by the Bayes factors calculated from their probabilities of satisfying given inequality constraints. In contrast to the related sign restriction approach that also makes use of theoretically implied inequality constraints, no restrictions are imposed. Hence, it is possible that only a subset or none of the theoretically implied shocks can be labelled. In the latter case, we conclude that the data do not lend support to the theory implying the signs of the effects in question. We illustrate the method by empirical applications to the crude oil market, and U.S. monetary policy.Peer reviewe

Forest dynamics, SILVI-STAR : a comprehensive monitoring system

To learn about the interactions between individual trees and between trees and other forest organisms, long-term monitoring of spontaneous forest development is necessary. A complete monitoring system has been developed including a computer package for analysis of long-term forest dynamics observations. A method of nested plot data collection on forest architecture and plant species composition has been worked out for monitoring purposes. The spatial and temporal relations between data are numerically expressed. Therefore a three-dimensional single-tree architectural model has been worked out to describe asymmetric tree shapes with a minimum of measured data points. Time series of forest development at different sites are built up on the basis of a digital descriptive model of the complex reality of forest structure and species composition.To guarantee continuity in data storage and data query a commercially available database and a geographical information system were used in the design of the information system. A visual interpretation of data is enabled by graphical system outputs such as profiles and ground plans of tree crown projections, providing substitutes for traditional profile drawings and maps. Application programs were developed to solve specific problems, as a step towards predictive models. In an application program, for integration with remote sensing studies, an aerial view of the forest canopy is simulated on the basis of measured plot data. This view provides a ground-truth reference for the training and interpretation of remote sensing images. To explain the growth of individual trees and the distribution patterns of herbs and tree regeneration on the forest floor, another application was developed, simulating the penetration of direct and of diffuse light. For the reconstruction of forest growth with tree ring data, a technique of animation was elaborated facilitating a visual interpretation of the forest development. The system is applied to demonstrate forest development in some European forest reserves using forest architectural descriptions and vegetation releves, tree ring data and historical sources.</p