Cointegration Analysis with State Space Models

This paper presents and exemplifies results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers. Unit root processes, cointegration and polynomial cointegration are defined. Based upon these definitions the major part of the paper discusses how state space models, which are equivalent to VARMA models, can be fruitfully employed for cointegration analysis. By means of detailing the cases most relevant for empirical applications, the I(1), MFI(1) and I(2) cases, a canonical representation is developed and thereafter some available statistical results are briefly mentioned.State space models, unit roots, cointegration, polynomial cointegration, pseudo maximum likelihood estimation, subspace algorithms

A Comparison of Johansen's, Bierens and the Subspace Algorithm Method for Cointegration Analysis

The methods listed in the title are compared by means of a simulation study and a real world application. The aspects compared in the simulations are: The performance of the tests of the different methods for the dimension of the cointegrating space and the quality of the estimated cointegrating space. It turns out that the subspace algorithm method, formulated in the state space framework and thus applicable for ARMA processes, performs at least comparable to the Johansen procedure and both perform significantly better than Bierens' method. The real world application is an investigation of the long-run properties of the neoclassical growth model for Austria. It turns out that the results do not fully support the theoretical predictions and that they are very versatile across the employed methods. The degree of versatility depends strongly upon the number of variables. For the case of 6 variables and about 100 observations huge differences occur, which lead us to conclude that the results of this typical situation in the applied literature should be interpreted with more caution than is commonly done.Cointegration; State Space Models; Subspace Algorithms; Simulation; Neoclassical Growth Model

The Balassa-Samuelson Effect in 'East & West'. Differences and Similarities

Based on two detailed Balassa-Samuelson (BS) studies, Wagner and Hlouskova (2004) for eight Central Eastern European countries (CEECs) and Wagner and Doytchinov (2004) for ten Western European countries (WECs), this study assesses the differences and similarities of the BS effect between these two country groups. The econometric results show that the BS effect may have been overestimated in previous studies due to application of inappropriate first generation panel cointegration methods. When appropriately quantified, the BS effect itself explains RER movements respectively inflation differentials only to a small extent. However, extended BS relationships that include additional variables allow for an adequate modelling of inflation. Based on the comparative analysis we draw some conclusions for monetary policy in the future enlarged Euro Area.Balassa-Samuelson effect, Central and Eastern Europe, Western Europe, Non-stationary panels, Inflation simulations

On PPP, Unit Roots and Panels

This paper re-assesses the panel (unit root test) evidence for PPP on four monthly data sets. We discuss and illustrate that commonly-used first generation panel unit root tests are inappropriate for PPP analysis since they are constructed for cross-sectionally uncorrelated panels. Given that real exchange rate panel data sets are – almost by construction – highly cross-sectionally correlated, so called second generation panel unit root methods that allow for and model cross-sectional dependence should be applied. Using inappropriate first generation tests, quite strong evidence for PPP is found. However, this evidence vanishes entirely when resorting to an appropriate method (e.g. the one developed in Bai and Ng, 2004a) for nonstationary cross-sectionally correlated panels. We strongly believe that our findings are relevant beyond the data sets investigated here for illustration.PPP, Real exchange rate index, Unit root, Panel, Cross-sectional dependence, Factor model

Locally Stable Marriage with Strict Preferences

We study stable matching problems with locality of information and control. In our model, each agent is a node in a fixed network and strives to be matched to another agent. An agent has a complete preference list over all other agents it can be matched with. Agents can match arbitrarily, and they learn about possible partners dynamically based on their current neighborhood. We consider convergence of dynamics to locally stable matchings -- states that are stable with respect to their imposed information structure in the network. In the two-sided case of stable marriage in which existence is guaranteed, we show that the existence of a path to stability becomes NP-hard to decide. This holds even when the network exists only among one partition of agents. In contrast, if one partition has no network and agents remember a previous match every round, a path to stability is guaranteed and random dynamics converge with probability 1. We characterize this positive result in various ways. For instance, it holds for random memory and for cache memory with the most recent partner, but not for cache memory with the best partner. Also, it is crucial which partition of the agents has memory. Finally, we present results for centralized computation of locally stable matchings, i.e., computing maximum locally stable matchings in the two-sided case and deciding existence in the roommates case.Comment: Conference version in ICALP 2013; to appear in SIAM J. Disc Mat

Strange and charm meson masses from twisted mass lattice QCD

We present first results of a 2+1+1 flavor twisted mass lattice QCD computation of strange and charm meson masses. We focus on D and D_s mesons with spin J = 0,1 and parity P = -,+

Endocrine disruptors in bottled mineral water : total estrogenic burden and migration from plastic bottles

Background, aim, and scope Food consumption is an important route of human exposure to endocrine-disrupting chemicals. So far, this has been demonstrated by exposure modeling or analytical identification of single substances in foodstuff (e.g., phthalates) and human body fluids (e.g., urine and blood). Since the research in this field is focused on few chemicals (and thus missing mixture effects), the overall contamination of edibles with xenohormones is largely unknown. The aim of this study was to assess the integrated estrogenic burden of bottled mineral water as model foodstuff and to characterize the potential sources of the estrogenic contamination. Materials, methods, and results In the present study, we analyzed commercially available mineral water in an in vitro system with the human estrogen receptor alpha and detected estrogenic contamination in 60% of all samples with a maximum activity equivalent to 75.2 ng/l of the natural sex hormone 17beta-estradiol. Furthermore, breeding of the molluskan model Potamopyrgus antipodarum in water bottles made of glass and plastic [polyethylene terephthalate (PET)] resulted in an increased reproductive output of snails cultured in PET bottles. This provides first evidence that substances leaching from plastic food packaging materials act as functional estrogens in vivo. Discussion and conclusions Our results demonstrate a widespread contamination of mineral water with xenoestrogens that partly originates from compounds leaching from the plastic packaging material. These substances possess potent estrogenic activity in vivo in a molluskan sentinel. Overall, the results indicate that a broader range of foodstuff may be contaminated with endocrine disruptors when packed in plastics. Keywords Endocrine disrupting chemicals - Estradiol equivalents - Human exposure - In vitro effects - In vivo effects - Mineral water - Plastic bottles - Plastic packaging - Polyethylene terephthalate - Potamopyrgus antipodarum - Yeast estrogen screen - Xenoestrogen

What's Really the Story with this Balassa-Samuelson Effect in the CEECs?

This paper offers a detailed assessment of the Balassa-Samuelson (BS) effect in eight Central and Eastern European countries (CEEC8). Several features distinguish this study from others: First, we investigate a variety of specifications of extended models. Non-homogeneity of wages, deviations from PPP in tradables and demand side variables are found to importantly contribute to explain inflation differentials. Second, a variety of specifications is investigated. Third, we rely upon bootstrap inference for panel unit root and panel cointegration analysis. The bootstrap results are rather clear: No evidence for cointegration remains when resorting to bootstrap inference. To quantify the bias that may arise from incorrectly using cointegration techniques, we also quantify the BS effect from equations containing (nonstationary) "cointegration" terms. Fourth, we present inflation simulations based on well specified scenarios. The results are as follows: Evidence for the BS effect is found. The BS effect is, however, rather small (around half a percent per annum) and not sufficient to explain the observed inflation differentials between the CEEC8 and the EU11. Using, despite the lacking evidence, cointegration techniques results throughout in substantially larger estimated effects. This suggests that studies relying upon cointegration may have overestimated the BS effect. The additional explanatory variables in the extended BS models allow for a satisfactory modelling of the observed inflation rates. The mean inflation simulations for the CEEC8 countries, based on the extended models, range from 2.77% for the Slovak Republic to 6.75% for Poland. These are well above the 2% inflation objective for the European Monetary UnionBalassa-Samuelson effect; Central and Eastern Europe; transition economies; non-stationary panels; bootstrapping; inflation simulations