TESTING FOR STATIONARITY IN HETEROGENEOUS PANEL DATA IN THE CASE OF MODEL MISSPECIFICATION

This paper investigates the performance of the tests proposed by Hadri and by Hadri and Larsson for testing for stationarity in heterogeneous panel data under model misspecification. The panel tests are based on the well known KPSS test (cf. Kwiatkowski et al.) which considers two models: stationarity around a deterministic level and stationarity around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. We also consider the case of the simultaneous presence of the two types of models in a panel. We employ two asymptotics: joint asymptotic, T, N -> infinity simultaneously, and T fixed and N allowed to grow indefinitely. We use Monte Carlo experiments to investigate the effects of misspecification in sample sizes usually used in practice. The results indicate that the assumption that T is fixed rather than asymptotic leads to tests that have less size distortions, particularly for relatively small T with large N panels (micro-panels) than the tests derived under the joint asymptotics. We also find that choosing a deterministic trend when a deterministic level is true does not significantly affect the properties of the test. But, choosing a deterministic level when a deterministic trend is true leads to extreme over-rejections. Therefore, when unsure about which model has generated the data, it is suggested to use the model with a trend. We also propose a new statistic for testing for stationarity in mixed panel data where the mixture is known. The performance of this new test is very good for both cases of T asymptotic and T fixed. The statistic for T asymptotic is slightly undersized when T is very small (

PANEL DATA UNIT ROOT TEST WITH FIXED TIME DIMENSION

The consideration of the limit theory in which T is fixed and N is allowed to go to infinity improves the finite-sample properties of the tests and avoids the imposition of the relative rates at which T and N go to infinity.

The Movebank data model for animal tracking

Studies of animal movement are rapidly increasing as tracking technologies make it possible to collect more data of a larger variety of species. Comparisons of animal movement across sites, times, or species are key to asking questions about animal adaptation, responses to climate and land-use change. Thus, great gains can be made by sharing and exchanging animal tracking data. Here we present an animal movement data model that we use within the Movebank web application to describe tracked animals. The model facilitates data comparisons across a broad range of taxa, study designs, and technologies, and is based on the scientific questions that could be addressed with the data.

Testing the Prebish-Singer hypothesis using second-generation panel data stationarity tests with a break

In this paper, we test the Prebish-Singer (PS) hypothesis, which states that real commodity prices decline in the long run, using two recent powerful panel data stationarity tests accounting for cross-sectional dependence and a structural break. We find that the hypothesis cannot be rejected for most commodities other than oil.

A Data Model for Content Modelling of Temporal Media

This paper describes a data model for content representation of temporal media in an IP based sensor network. The model is formed by introducing the idea of semantic-role from linguistics into the underlying concepts of formal event representation with the aim of developing a common event model. The architecture of a prototype system for a multi camera surveillance system, based on the proposed model is described. The important aspects of the proposed model are its expressiveness, its ability to model content of temporal media, and its suitability for use with a natural language interface. It also provides a platform for temporal information fusion, as well as organizing sensor annotations by help of ontologies.