A vector of quarters representation for bivariate time series

In this paper it is shown that several models for a bivariate nonstationary quarterly time series are nested in a vector autoregression with cointegration restrictions for the eight annual series of quarterly observations. Or, the Granger Representation Theorem is extended to incorporate, e.g., seasonal and periodic cointegration.     

A vector of quarters representation for bivariate time series

In this paper it is shown that several models for a bivariate nonstationary quarterly time series are nested in a vector autoregression with cointegration restrictions for the eight annual series of quarterly observations. Or, the Granger Representation Theorem is extended to incorporate, e.g., seasonal and periodic cointegration.     

Influence of urban landscape structure on bird fauna: a case study across seasons in the city of Valencia (Spain)

Studies on bird fauna of urban environments have had a long history, but the potential of studies mapping the distribution of birds in cities probably has not fully developed. The bird fauna of the municipality of Valencia (Spain) was studied to determine the influence of urbanization on bird species richness and abundance. Birds were censused during winter and the breeding season of years 1997–1998 in 197 squares measuring 49 ha each from a rural and an urbanized area. Across seasons the number of species decreased around 40% in the city compared with the rural landscape surrounding it. Such pattern could be attributed to the low number of farmland species capable to use the habitats inside the city, and the limited ability of urban parks in attracting woodland species. In the urban landscape, the influence of the dimensions and spatial arrangement of habitat patches was outweighed by the amount of each habitat per square. Bird richness and the abundance of most species were negatively related with the amount of built-up habitat per square and positively with the amount of urban parks, and of habitat diversity. Conversely, bird fauna was largely independent of mean park size per square especially during winter, indicating that at the landscape scale even small patches of habitat could play an ecological role. Conservation of urban bird diversity could benefit of two complementary strategies: (i) the protection of the surrounding rural landscape from urban development; (ii) habitat enhancement within the city. Particularly, a proper design and habitat management of urban parks could improve their suitability for urban bird fauna.     

THE BEHAVIOR OF HEGY TESTS FOR QUARTERLY TIME SERIES WITH SEASONAL MEAN SHIFTS

ABSTRACT

This paper studies the behavior of the HEGY statistics for quarterly data, for seasonal autoregressive unit roots, when the analyzed time series is deterministic seasonal stationary but exhibits a change in the seasonal pattern. We analyze also the HEGY test for the nonseasonal unit root. the data generation process being trend stationary too. Our results show that when the break magnitudes are finite, the HEGY test statistics are not asymptotically biased toward the nonrejection of the seasonal and nonseasonal unit root hypotheses. However, the finite sample power properties may be substantially affected, the behavior of the tests depending on the type of the break.     

Seasonal patterns of global oil consumption: Implications for long term energy policy

The dynamic evolution of the seasonal patterns in world oil consumption is dictated by complex interactions between regional consumers. Although this global pattern was stable and predictable in the past, recently it has undergone dramatic changes that have not been well understood yet. This paper contributes to literature on oil consumption behaviours by analysing the counter-balance of ‘coincident’ and ‘counter-directional’ regional seasonal patterns that have time-varying amplitude relative to their longer-term trends. It is shown that the recent global seasonal changes have been mainly driven by long-run demand trends in fast-growing emerging markets and, to a lesser extent, by idiosyncratic changes in regions’ seasonal amplitude. Our analysis is relevant to energy policy in general as both global and regional oil consumption seasonality have important implications for oil pricing, investment decisions, hedging, geopolitics and energy security.     

Biomass and biodiversity of nocturnal aerial insects in an Adelaide City park and implications for bats (Microchiroptera)

Temporal variation of insect communities in urban environments is poorly known and mechanisms driving these changes are unclear, as are the implications for insectivorous predators. We examined the relationships between season and nocturnal aerial insect biomass and biodiversity, and between temperature and insect biomass in the Adelaide zoological gardens from December 2005 to September 2006. We also compared the effectiveness of two insect trap types and used a bat detector to assess bat activity in relation to insect biomass. During the study, 9,939 insects from 13 orders were collected at the Adelaide zoo with a Malaise trap and a light trap. Mass and diversity of insects were highest during warm months, as was bat activity, and bat activity was positively correlated with insect biomass. Winter-active insects consisted predominantly of Diptera and Lepidoptera, which may provide an important winter food resource for insectivorous bats. The Malaise trap attracted fewer insect orders and biomass than did the light trap, and insects congregated within 6 m of artificial lights, so bats that forage at lights may have an advantage in urban areas. A strong need for the inclusion of urban insects to biodiversity inventories exists in the context of bat conservation.     

The beveridge-nelson decomposition: Properties and extensions

Summary This paper discusses the time series properties of the Beveridge-Nelson decomposition and provides extensions in two fundamental directions: in the first place it is shown that anyARIMA(p, 2, q) process can be additively decomposed into anIMA (2, 1) trend and a stationary component; secondly, for the class of seasonally integrated processes, i.e. displaying unit roots at the seasonal frequencies, another component, namely the seasonal component, is identified by the condition that its predictions will average, out to zero over any one-year time span. Furthermore, algorithms for the extraction of the components are given which exploit the Kalman filter recursions once the data generating process is cast in the state space form.     

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Electricity market prices are highly volatile and often have high spikes. Both government authorities and market participants require sophisticated models and techniques for forecasting future prices and managing relevant financial risks in such a volatile market. This article extends the conditional autoregressive geometric process (CARGP) model (Chan et al., 2012 Chan, J. S.K., Lam, C. P.Y., Yu, P. L.H., Choy, S. T.B., Chen, C. W.S. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computat. Statist. Data Anal. 56:30063019.[Crossref], [Web of Science ®] , [Google Scholar]) to the CARGP model with thresholds and jumps, which is abbreviated as CARGP-TJ model in this article. We will demonstrate that the proposed CARGP-TJ model not only captures the unique features of the electricity price but also performs better than other existing models. For robustness consideration, a heavy-tailed error distribution is adopted. Model implementation relies on the powerful Bayesian Markov chain Monte Carlo simulation techniques via WinBUGS software. The analysis of the daily maximum electricity prices of the New South Wales, Australia reveals that the proposed CARGP-TJ model captures the price spikes well for both in-sample estimation and out-of-sample forecast.     

Periodic Autoregressive Conditional Heteroscedasticity

Most high-frequency asset returns exhibit seasonal volatility patterns. This article proposes a new class of models featuring periodicity in conditional heteroscedasticity explicitly designed to capture the repetitive seasonal time variation in the second-order moments. This new class of periodic autoregressive conditional heteroscedasticity, or P-ARCH, models is directly related to the class of periodic autoregressive moving average (ARMA) models for the mean. The implicit relation between periodic generalized ARCH (P-GARCH) structures and time-invariant seasonal weak GARCH processes documents how neglected autoregressive conditional heteroscedastic periodicity may give rise to a loss in forecast efficiency. The importance and magnitude of this informational loss are quantified for a variety of loss functions through the use of Monte Carlo simulation methods. Two empirical examples with daily bilateral Deutschemark/British pound and intraday Deutschemark/U.S. dollar spot exchange rates highlight the practical relevance of the new P-GARCH class of models. Extensions to discrete-time periodic representations of stochastic volatility models subject to time deformation are briefly discussed.     

Decisions on Seasonal Unit Roots

Decisions on the presence of seasonal unit roots in economic time series are commonly taken on the basis of statistical hypothesis tests. Some of these tests have absence of unit roots as the null hypothesis, while others use unit roots as their null. Following a suggestion by Hylleberg (1995) to combine such tests in order to reach a clearer conclusion, we evaluate the merits of such test combinations on the basis of a Bayesian decision setup. We find that the potential gains over a pure application of the most common test due to Hylleberg et al. (1990) can be small.