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Land market regulations are often justified by the assumption that activities of foreign and nonagricultural investors drive up land prices in countries with low land price levels. However, empirical knowledge about the dynamics of agricultural land prices across borders is sparse. Using the German reunification as a natural experiment, we study the effect of the former inner German border on the dynamics of agricultural land prices in East and West Germany. We apply a land price diffusion model with an error correction specification that estimates to what extent agricultural land markets are spatially integrated. A novel feature of our model is its ability to distinguish price diffusion within states and across state borders. We find that local agricultural land markets in Germany are linked by a long-run equilibrium relationship. Spatial market integration, however, does not hold among all counties in our study area. Regarding our main research question, we provide evidence for a persistent border effect given that the fraction of spatially integrated counties is larger within states than across the former border. Moreover, we observe non-significant error correction terms for many counties along the former border. From a policy perspective, it is striking to realize that even 25 years after German reunification, pronounced land price differences persist. It is quite likely that price diffusion through existing borders within the EU would take even more time given language barriers, different administrative procedures for land acquisitions, different tax systems, and information asymmetries between domestic and foreign market participants.

A recipe is provided for producing, from a sequence of procedures in the Gaussian regression model, an asymptotically equivalent sequence in the density estimation model with i. i. d. observations. The recipe is, to put it roughly, to calculate square roots of normalised frequencies over certain intervals, add a small random distortion, and pretend these to be observations from a Gaussian discrete regression model.

It is a well established fact, that – in the case of classical random graphs like variants of Gn,p or random regular graphs – spectral methods yield efficient algorithms for clustering (e. g. colouring or bisec- tion) problems. The theory of large networks emerging recently provides convincing evidence that such networks, albeit looking random in some sense, cannot sensibly be described by classical random graphs. A vari- ety of new types of random graphs have been introduced. One of these types is characterized by the fact that we have a fixed expected degree sequence, that is for each vertex its expected degree is given. Recent theoretical work confirms that spectral methods can be success- fully applied to clustering problems for such random graphs, too – pro- vided that the expected degrees are not too small, in fact ≥ log6 n. In this case however the degree of each vertex is concentrated about its expectation. We show how to remove this restriction and apply spectral methods when the expected degrees are bounded below just by a suitable constant. Our results rely on the observation that techniques developed for the classical sparse Gn,p random graph (that is p = c/n) can be transferred to the present situation, provided we consider a suitably normalized ad- jacency matrix: We divide each entry of the adjacency matrix by the product of the expected degrees of the incident vertices. Given the host of spectral techniques developed for Gn,p this observation should be of independent interest.

The present volume consists of eight studies dealing with various aspects of Prosodic Phonology (see Booij 1983, Nespor & Vogel 1986 and much current work). The languages dealt with below include English, German, Italian, Luganda, Ndebele, Persian, Polish, Spanish, and Tamil.

AbstractThe indigenous populations of inner Eurasia, a huge geographic region covering the central Eurasian steppe and the northern Eurasian taiga and tundra, harbor tremendous diversity in their genes, cultures and languages. In this study, we report novel genome-wide data for 763 individuals from Armenia, Georgia, Kazakhstan, Moldova, Mongolia, Russia, Tajikistan, Ukraine, and Uzbekistan. We furthermore report genome-wide data of two Eneolithic individuals (∽5,400 years before present) associated with the Botai culture in northern Kazakhstan. We find that inner Eurasian populations are structured into three distinct admixture clines stretching between various western and eastern Eurasian ancestries. This genetic separation is well mirrored by geography. The ancient Botai genomes suggest yet another layer of admixture in inner Eurasia that involves Mesolithic hunter-gatherers in Europe, the Upper Paleolithic southern Siberians and East Asians. Admixture modeling of ancient and modern populations suggests an overwriting of this ancient structure in the Altai-Sayan region by migrations of western steppe herders, but partial retaining of this ancient North Eurasian-related cline further to the North. Finally, the genetic structure of Caucasus populations highlights a role of the Caucasus Mountains as a barrier to gene flow and suggests a post-Neolithic gene flow into North Caucasus populations from the steppe.

We define and study the category of symmetric $\mathfrak{sl}_2$-webs. This category is a combinatorial description of the category of all finite dimensional quantum $\mathfrak{sl}_2$-modules. Explicitly, we show that (the additive closure of) the symmetric $\mathfrak{sl}_2$-spider is (braided monoidally) equivalent to the latter. Our main tool is a quantum version of symmetric Howe duality. As a corollary of our construction, we provide new insight into Jones-Wenzl projectors and the colored Jones polynomials. 32 pages, lots of figures, comments welcome

This paper investigates whether multivariate crash risk is priced in the cross-section of expected stock returns. Motivated by a theoretical asset pricing model, we capture the multivariate crash risk of a stock by a combined measure based on its expected shortfall and its multivariate lower tail dependence with the systematic factors of the Carhart (1997) model. We find that stocks with a high exposure to joint crashes of the market and the momentum factor bear a risk premium which is not explained by traditional linear factor models or by other downside risk measures. Our results indicate that accounting for the multivariate crash risk of established state variables helps to understand the cross-section of expected stock returns without further expanding the factor zoo.