BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Vector autoregression (VAR) models are widely used models for multivariate time series analysis, but often suffer from their dense parameterization. Bayesian methods are commonly employed as a remedy by imposing shrinkage on the model coefficients via informative priors, thereby reducing parameter uncertainty. The subjective choice of the informativeness of these priors is often criticized and can be alleviated via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models in a hierarchical fashion. It incorporates functionalities that permit addressing a wide range of research problems while retaining an easy-to-use and transparent interface. It features the most commonly used priors in the context of multivariate time series analysis as well as an extensive set of standard methods for analysis. Further functionalities include a framework for defining custom dummy-observation priors, the computation of impulse response functions, forecast error variance decompositions and forecasts.Series: Department of Economics Working Paper Serie

BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these priors is subject of much debate and can be approached via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models with hierarchical prior selection. It implements functionalities and options that permit addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. Features include structural analysis of impulse responses, forecasts, the most commonly used conjugate priors, as well as a framework for defining custom dummy-observation priors. BVAR makes Bayesian VAR models user-friendly and provides an accessible reference implementation

Unveiling Drivers of Deforestation: Evidence from the Brazilian Amazon

The drivers of deforestation are the subject of many spatially explicit studies with considerable policy impact, yet few studies account for spatial dependence, thus neglecting spillover effects. In this work, we use high-resolution remotely sensed land cover change maps, extended with socioeconomic panel data for 141 municipalities in the state of Mato Grosso, Brazil, to investigate the role of agriculture in deforestation from 2006 until 2016. Our econometric model specifically accounts for spatial indirect effects from the dependent and explanatory variables, thus avoiding biased and inconsistent estimates. We identify indirect spillover effects from croplands and direct effects from cattle as significant deforestation drivers. Neglecting to explicitly account for spatial dependence considerably underestimates deforestation pressure of soy production. We conclude that spatial dynamics play a crucial role in deforestation and need to be considered in econometric studies, in order to facilitate informed policy decisions

TT Arietis - Observations of a Cataclysmic Variable Star with the MOST Space Telescope

We measured the photometric flux of the cataclysmic variable TT Arietis (BD+14 341) using the MOST space telescope. Periodic oscillations of the flux reveal the orbital period as well as other features of this binary system. We applied a Discrete Fourier Transform (DFT) on a reduced dataset to retrieve the frequencies of TT Arietis. The analysis of the system revealed a photometric period of 3.19 hours. Though the MOST data has a high cadence of 52.8 seconds, a fine structure of the accretion disk is not obvious.Comment: 3 pages, 1 figure, JENAM 2008 proceeding

Kulturlandschaft gestalten: Ein Werkzeugkasten für die Ländliche Neuordnung

Flurbereinigungsverfahren bieten die Möglichkeit, ganzheitlich und zukunftsgerecht die ländlichen Räume zu entwickeln. Dabei sollen nicht nur wirtschaftliche Aspekte berücksichtigt sondern auch die regionaltypische Kulturlandschaft bewahrt und weiterentwickelt werden. Die Broschüre vermittelt Grundsätze zur erfolgreichen Landschaftsgestaltung. Redaktionsschluss: 02.11.202

Unveiling Drivers of Deforestation: Evidence from the Brazilian Amazon

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Indirect impacts and determinants that eventually differ across locations and over time are important factors in these processes. These are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we employ a flexible modelling framework to gain more accurate quantitative insights into the complexities of deforestation phenomena. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 and explicitly consider spatial spillovers and varying impacts over time and space. Spillover effects from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in later years. This suggests moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that neglect of spatial dynamics and the assumption of homogeneous impacts leads to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.Series: Ecological Economic Paper