Testing for unit roots in panel time-series models with multiple level breaks

This paper proposes two new unit root tests that are appropriate in the presence of an unknown number of structural breaks in the level of the data. One is based on a single time series and the other is based on a panel of multiple series. For the estimation of the number of breaks and their locations, a simple procedure based on outlier detection is proposed. The limiting distributions of the tests are derived and evaluated in small samples using simulation experiments. The implementation of the tests is illustrated using as an example purchasing power parity.

Continuous plankton records stand the test of time: evaluation of flow rates, clogging and the continuity of the CPR time-series

The Continuous Plankton Recorder (CPR) survey is one of the most extensive biological time-series in existence and has been in operation over major regions of the North Atlantic since 1932. However, there is little information about the volume of water filtered through each sample, but rather a general assumption has persisted that each sample represents 3 m3. Data from electromagnetic flowmeters, deployed on CPRs between 1995 and 1998, was examined. The mean volume filtered through samples was 3.11 m3 and the effect of clogging on filtration efficiencies was not great. Consequently, even when the likely variations in flow due to clogging are taken into account, previously identified links between zooplankton abundance and climatic signals remain strong.

Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.

Model based analysis of fMRI/EEG data

This thesis addresses two major topics in neuroscience literature and drawbacks from existing literature are addressed by utilising state space models and Bayesian estimation techniques. Particle filter-based joint estimation of the physiological model for time-series analysis of fMRI data is demonstrated first in the thesis and secondly the Granger causality-based effective connectivity analysis of EEG data is investigated.

A unit root model for trending time-series energy variables

In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.

Energy consumption and output: time series evidence from non-OECD developing countries

Statistically significant association between energy consumption and economic growth is now well established in the literature. However, it still remains an unsettled issue whether economic growth is the cause or effect of energy consumption. The importance of identifying the direction of causality emanates from its relevance in national policy-making issues regarding energy conservation. Energy conservationissue is more important when energy acts as a contributing factor in economic growth than when it is used as a result of higher economic growth. In this backdrop, it is justified to search causal relationship between energy consumption and national output (GDP) of those countries that are expected to have higher energy consumption in future. Evidence shows that countries classified as non-OECD Asia will have the highest growth in energy consumption (3.7 percent) over the period 2003-2030. This forecasted energy consumption in these countries will have significant policy implication in the area of energy conservation. Hence, the present paper attempts to identify the direction of causality between energy consumption and output in the context of six major energy dependent non-OECD Asian countries.However, since the traditional bivariate approach suffers from omitted variable problems (Stern 1993, Masih and Masih, 1996 and Asafu-Adjaye, 2000), this paper employs a trivariate demand side approach consisting of energy consumption, income and prices. The countries selected for this purpose are Bangladesh, China, India, Malaysia, Pakistan and Thailand. Moreover, according to the Energy Information Administration (EIA) data of 2005, these six countries contribute 81.35% of the energyconsumption by all non-OECD Asian countries (aggregate energy consumption of 2005 by all non-OECD Asian countries is 113.60 quadrillion BTU while for these six countries alone the consumption is 92.42 quadrillion BTU).