Challenges for land system science

While considerable progress has been made in understanding land use change, land system science continues to face a number of grand challenges. This paper discusses these challenges with a focus on empirical land system studies, land system modelling and the analysis of future visions of land system change. Contemporary landscapes are contingent outcomes of past and present patterns, processes and decisions. Thus, empirical analysis of past and present land-use change has an important role in providing insights into the socio-economic and ecological processes that shape land use transitions. This is especially important with respect to gradual versus rapid land system dynamics and in understanding changes in land use intensity. Combining the strengths of empirical analysis with multi-scale modelling will lead to new insights into the processes driving land system change. New modelling methods that combine complex systems thinking at a local level with macro-level economic analysis of the land system would reconcile the multi-scale dynamics currently encapsulated in bottom-up and top-down modelling approaches. Developments in land use futures analysis could focus on integrating explorative scenarios that reflect possible outcomes with normative visions that identify desired outcomes. Such an approach would benefit from the broad and in-depth involvement of stakeholders in order to link scientific findings to political and societal decision-making culminating in a set of key choices and consequences. Land system models have an important role in supporting future land use policy, but model outputs require scientific interpretation rather than being presented as predictions. The future of land system science is strongly dependent on the research community's capacity to bring together the elements of research discussed in the paper, via empirical data collection and analysis of observed processes, computer simulation across scale levels and futures analysis of alternative, normative visions through stakeholder engagement.     

Determinants of Current Account Imbalances in 16 OECD Countries: An Out-Of-Sample Perspective

We employ panel data as well as country-specific models to uncover empirically the determinants of current account imbalances. We find evidence of slope heterogeneity likely rendering the fixed-effect estimator inconsistent. Mean group estimation is followed to overcome the latter difficulty. Both estimation techniques are evaluated by means of in- and out-of-sample criteria. The mean group estimator outperforms the fixed-effect approach and, moreover, only three economic variables are found to impact on the current account balance significantly. These are the government budget balance, the domestic output gap and the changes of the terms of trade. We finally propose a parsimonious dynamic model of the current account, including only these variables and illustrate that it has similar predictive accuracy as country-specific benchmark models. JEL no. F32, C23, C53     

Münzunion oder Währungsunion?

Ohne Zusammenfassung     

Zum Problem optimaler Wachstumspfade in mehrsektoralen Modellen

Zusammenfassung Ausgangspunkt bildet ein Produktionsmodell, das durch eine Kombination von zwischensektoraler Prozeßbeschreibung und allgemeinen sektoralen Produktionsfunktionen gewissen empirischen Befunden über input-output-Struktur und Substitutionseffekten theoretisch zu entsprechen versucht. Auf dieser Basis läßt sich eine Reihe von Problemen des optimalen Wachstums (welfaremaximierende Pfade, optimale Beschäftigungspfade, konsummaximierende Pfade) als klassische Variationsprobleme formulieren. Schließlich wurde ein allgemeiner Ausblick auf andere Interpretationen (steuerbare Prozesse) und diesen angemessene Lösungstechniken größerer Leistungsfähigkeit (Maximumprinzip von Pontrjagin) eröffnet.Mit 1 Textabbildung     

On the (nonlinear) relationship between exchange rate uncertainty and trade — An investigation of US trade figures in the group of seven

In this paper bilateral models formalizing monthly growth of US imports and exports are employed to investigate the potential of nonlinear relationships linking exchange rate uncertainty and trade growth. Parametric linear and nonlinear as well as semiparametric time series models are evaluated in terms of fitting and ex ante forecasting. The overall impact of exchange rate variations on trade growth is found to be weak. In periods of large exchange rate variations, trade growth forecasts gain from conditioning on volatility. Empirical results support the view that the relationship of interest might be non-linear and, moreover, lacks homogeneity across countries and imports vs. exports. JEL no. C14, C22, F31, F41     

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution. For a cross section of 18 stock market indices, we notice that (threshold) (T)GARCH-implied model innovations are likely at odds with the commonly held IID assumption. Two complementary strategies are pursued to evaluate the conditional distributions of consecutive TGARCH innovations, a non-parametric approach and a class of standardized copula distributions. Modelling higher order dependence patterns is found to improve standard TGARCH-implied conditional value-at-risk and expected shortfall out-of-sample forecasts that rely on the notion of IID innovations.     

Network topology of the interbank market

We provide an empirical analysis of the network structure of the Austrian interbank market based on Austrian Central Bank (OeNB) data. The interbank market is interpreted as a network where banks are nodes and the claims and liabilities between banks define the links. This allows us to apply methods from general network theory. We find that the degree distributions of the interbank network follow power laws. Given this result we discuss how the network structure affects the stability of the banking system with respect to the elimination of a node in the network, i.e. the default of a single bank. Further, the interbank liability network shows a community structure that exactly mirrors the regional and sectoral organization of the current Austrian banking system. The banking network has the typical structural features found in numerous other complex real-world networks: a low clustering coefficient and a short average path length. These empirical findings are in marked contrast to the network structures that have been assumed thus far in the theoretical economic and econo-physics literature.