We study Granger Causality in the context of wide-sense stationary time series. The focus of the analysis is to understand how the underlying topological structure of the causality graph affects graph recovery by means of the pairwise testing heuristic. Our main theoretical result establishes a sufficient condition (in particular, the graph must satisfy a polytree assumption we refer to as strong causality) under which the graph can be recovered by means of unconditional and binary pairwise causality testing. Examples from the gene regulatory network literature are provided which establish that graphs which are strongly causal, or very nearly so, can be expected to arise in practice. We implement finite sample heuristics derived from our theory, and use simulation to compare our pairwise testing heuristic against LASSO-based methods. These simulations show that, for graphs which are strongly causal (or small perturbations thereof) the pairwise testing heuristic is able to more accurately recover the underlying graph. We show that the algorithm is scalable to graphs with thousands of nodes, and that, as long as structural assumptions are met, exhibits similar high-dimensional scaling properties as the LASSO. That is, performance degrades slowly while the system size increases and the number of available samples is held fixed. Finally, a proof-of-concept application example shows, by attempting to classify alcoholic individuals using only Granger causality graphs inferred from EEG measurements, that the inferred Granger causality graph topology carries identifiable features.

This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$, $\textsf{R}$, and $\textsf{MATLAB}$. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.

The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.

Foodborne and waterborne gastrointestinal infections and their associated outbreaks are preventable, yet still result in significant morbidity, mortality and revenue loss. Many enteric infections demonstrate seasonality, or annual systematic periodic fluctuations in incidence, associated with climatic and environmental factors. Public health professionals use statistical methods and time series models to describe, compare, explain and predict seasonal patterns. However, descriptions and estimates of seasonal features, such as peak timing, depend on how researchers define seasonality for research purposes and how they apply time series methods. In this review, we outline the advantages and limitations of common methods for estimating seasonal peak timing. We provide recommendations improving reporting requirements for disease surveillance systems. Greater attention to how seasonality is defined, modelled, interpreted and reported is necessary to promote reproducible research and strengthen proactive and targeted public health policies, intervention strategies and preparedness plans to dampen the intensity and impacts of seasonal illnesses.

The European Union (EU) has been using economic sanctions both as a foreign policy tool and as a liberal alternative to military action. Since 2006, it has been implementing general sanctions against the whole economy of Iran, affecting their trade relations, and since 2007, following the imposition of sanctions by the UN Security Council, it has also been using smart sanctions targeting Iranian entities and natural persons associated with the country's military activities. In a nonlinear autoregressive distributed lag (NARDL) model, this paper investigates the impact of general and targeted EU sanctions against Iran on quarterly bilateral trade values between the 19 members of the euro area (EA19) and Iran between the first quarter of 1999 and the fourth quarter of 2018. In a robustness NARDL specification, trade between Iran and the 28 members of the EU is analysed. In addition, a gravity model of bilateral trade between Iran and the EU member states is run in a robustness check. The results indicate that the EU's general sanctions have strongly hampered trade flows between the two trading partners in almost all sectors, except for the primary sectors. Furthermore, our study finds that the impact of smart sanctions targeting Iranian entities and natural persons is much smaller than the impact of general sanctions on total trade values and the trade values of many sectors. Smart sanctions affect the exports of most sectors from the EA19 and the EU28 to Iran, while they are statistically insignificant for the imports of many sectors from Iran. Thus, this paper provides evidence of the motivations behind smart sanctions, which target specific individuals and entities rather than the whole economy, unlike general sanctions, which have a negative impact on ordinary people.

During the Great Recession, governments across the continent implemented austerity policies. A large literature claims that such policies are surprisingly popular and have few electoral costs. This article revisits this question by studying the popularity of governments during the economic crisis. The authors assemble a pooled time-series data set for monthly support for ruling parties from fifteen European countries and treat austerity packages as intervention variables to the underlying popularity series. Using time-series analysis, this permits the careful tracking of the impact of austerity packages over time. The main empirical contributions are twofold. First, the study shows that, on average, austerity packages hurt incumbent parties in opinion polls. Secondly, it demonstrates that the magnitude of this electoral punishment is contingent on the economic and political context: in instances of rising unemployment, the involvement of external creditors and high protest intensity, the cumulative impact of austerity on government popularity becomes considerable.

Weather conditions can impact infectious disease transmission, causing mortalities in humans, wild and domestic animals. Although rainfall in dry tropical regions is highly variable over the year, rainfall is thought to play an important role in the transmission of tick-borne diseases. Whether variation in rainfall affects disease-induced mortalities, is, however, poorly understood. Here, we use long-term data on monthly rainfall and Boran cattle mortality (1998–2017) to investigate associations between within-year variation in rainfall and cattle mortalities due to East Coast fever (ECF), anaplasmosis and babesiosis in Laikipia, Kenya, using ARIMAX modelling. Results show a negative correlation between monthly rainfall and cattle mortality for ECF and anaplasmosis, with a lag effect of 2 and 6 months, respectively. There was no association between babesiosis-induced mortalities and monthly rainfall. The results of this study suggest that control of the tick-borne diseases ECF and anaplasmosis to reduce mortalities should be intensified during rainy periods after the respective estimated time lags following dry periods.

Understanding how seasonal patterns change from year to year is important for the management of infectious disease epidemics. Here, we present a mathematical formalization of the application of complex demodulation, which has previously only been applied in an exploratory manner in the context of infectious diseases. This method extracts the changing amplitude and phase from seasonal data, allowing comparisons between the size and timing of yearly epidemics. We first validate the method using synthetic data that displays the key features of epidemic data. In particular, we analyse both annual and biennial synthetic data, and explore the effect of delayed epidemics on the extracted amplitude and phase. We then demonstrate the usefulness of complex demodulation using national notification data for influenza in Australia. This method clearly highlights the higher number of notifications and the early peak of the influenza pandemic in 2009. We also identify that epidemics that peaked later than usual generally followed larger epidemics and involved fewer overall notifications. Our analysis establishes a role for complex demodulation in the study of seasonal epidemiological events.

Some institutional arrangements may be undesirable for democracy by obscuring which political actors are to be held responsible for failed or successful policies and bad or good macroeconomic performances. Much of the work in the area has focused on whether institutions affect the ‘clarity of political responsibility’ and the ability of voters to punish or reward, in turn, governments and elected officials. Not much has been said, however, about the assignment of responsibility outside the electoral context, for a broad range of policy areas. This paper explores these questions in the context of French semi-presidentialism. It demonstrates that the French public is surprisingly quite responsive to the demands imposed by their political system by adjusting reasonably well their evaluations of both actors of the executive in light of major political events and changes in the economic conditions when the circumstances clearly indicate which of the two is ‘in charge’. At other times, however, this particular institutional arrangement obscures instead political responsibility.

In this paper, we investigated the possible exponential decays in the long term optical light curve of the BL Lac {OJ 287}. We developed a method that can be used to decomposing a light curve into a linear combination of exponential decays. The decomposing shows that the decay time scales range from ~ 103.6 to ~ 10−4 days. The power spectra has frequency-dependent power-law with slop ~ 0.5, and the peak of power is at the time scale of decay on ~ 160 days.

Structural instability in economic time series is widely reported in the literature. It is most prevalent in such series as price indices and inflation related data. Many methods have been developed for analysing and modelling structural changes in a univariate time series model. However, most of them assume that the data are generated by one fixed type (linear or non-linear) of the time series processes. This paper proposes a strategy for modelling different segments of an economic time series by different linear or non-linear models. A graphical procedure is suggested for detecting the model change points. The proposed procedure is illustrated by modelling annual United Kingdom price inflation series over the period 1265 to 2005. Stochastic modelling of inflation rates is an important topic to actuaries for dealing with long-term index linked insurance business. The proposed method suggests dividing the U.K. inflation series into four segments for modelling. Inflation projections based on the latest segment of the data are obtained through simulations. To get a better understanding of the impact of structural changes on inflation projections we also perform a forecasting study.

The mortality of the bottlenose dolphin, Tursiops truncatus, on the southern portion of Rio Grande do Sul State coast was investigated based on 914 beach surveys conducted between 1969 and 2006. A total of 188 stranded bottlenose dolphins were recorded during this period, indicating a 1.8M:1F sex-ratio of those animals sexed (N = 79). Mortality was low in calves, high in juveniles and sub-adults and slightly lower than in adults. The overall mortality was clearly seasonal overlapping with higher fishing efforts in the Patos Lagoon Estuary and adjacent coastal areas, where most individuals washed ashore. Analysis of a continuous 14-year long subset (1993–2006) of the data indicated relatively low levels of mortality between 1995 and 2000 and a marked increase between 2002 and 2005 followed by an apparent drop in 2006. By-catch was responsible for at least 43% of the recorded mortality between 2002 and 2006. Juvenile males were more susceptible to incidental catches. Among females, by-catch of adults represented 75%. Results of a potential biological removal analysis suggest that current levels of fishing-related mortality are unsustainable for the small resident population of bottlenose dolphins that inhabits the Patos Lagoon Estuary, and that this population may be declining.

Numerous studies over the past 30 years have suggested there is a causal connection between the motion of the Sun through the Galaxy and terrestrial mass extinctions or climate change. Proposed mechanisms include comet impacts (via perturbation of the Oort cloud), cosmic rays and supernovae, the effects of which are modulated by the passage of the Sun through the Galactic midplane or spiral arms. Supposed periodicities in the fossil record, impact cratering dates or climate proxies over the Phanerozoic (past 545 Myr) are frequently cited as evidence in support of these hypotheses. This remains a controversial subject, with many refutations and replies having been published. Here I review both the mechanisms and the evidence for and against the relevance of astronomical phenomena to climate change and evolution. This necessarily includes a critical assessment of time series analysis techniques and hypothesis testing. Some of the studies have suffered from flaws in methodology, in particular drawing incorrect conclusions based on ruling out a null hypothesis. I conclude that there is little evidence for intrinsic periodicities in biodiversity, impact cratering or climate on timescales of tens to hundreds of Myr. Although this does not rule out the mechanisms, the numerous assumptions and uncertainties involved in the interpretation of the geological data and in particular in the astronomical mechanisms suggest that Galactic midplane and spiral arm crossings have little impact on biological or climate variation above background level. Non-periodic impacts and terrestrial mechanisms (volcanism, plate tectonics, sea level changes), possibly occurring simultaneously, remain likely causes of many environmental catastrophes. Internal dynamics of the biosphere may also play a role. In contrast, there is little evidence supporting the idea that cosmic rays have a significant influence on climate through cloud formation. It seems likely that more than one mechanism has contributed to biodiversity variations over the past half Gyr.