Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses.

Debate on the use of lagged dependent variables has a long history in political science. The latest contribution to this discussion is Wilkins (2018, Political Science Research and Methods, 6, 393–411), which advocates the use of an ADL(2,1) model when there is serial dependence in the outcome and disturbance. While this specification does offer some insurance against serially correlated disturbances, this is never the best (linear unbiased estimator) approach and should not be pursued as a general strategy. First, this strategy is only appropriate when the data-generating process (DGP) actually implies a more parsimonious model. Second, when this is not the DGP—e.g., lags of the predictors have independent effects—this strategy mischaracterizes the dynamic process. We clarify this issue and detail a Wald test that can be used to evaluate the appropriateness of the Wilkins approach. In general, we argue that researchers need to always: (i) ensure models are dynamically complete and (ii) test whether more restrictive models are appropriate.

The prespecification of the network is one of the biggest hurdles for applied researchers in undertaking spatial analysis. In this letter, we demonstrate two results. First, we derive bounds for the bias in nonspatial models with omitted spatially-lagged predictors or outcomes. These bias expressions can be obtained without prior knowledge of the network, and are more informative than familiar omitted variable bias formulas. Second, we derive bounds for the bias in spatial econometric models with nondifferential error in the specification of the weights matrix. Under these conditions, we demonstrate that an omitted spatial input is the limit condition of including a misspecificed spatial weights matrix. Simulated experiments further demonstrate that spatial models with a misspecified weights matrix weakly dominate nonspatial models. Our results imply that, where cross-sectional dependence is presumed, researchers should pursue spatial analysis even with limited information on network ties.

Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.

Drawing on a landscape analysis of existing data-sharing initiatives, in-depth interviews with expert stakeholders, and public deliberations with community advisory panels across the U.S., we describe features of the evolving medical information commons (MIC). We identify participant-centricity and trustworthiness as the most important features of an MIC and discuss the implications for those seeking to create a sustainable, useful, and widely available collection of linked resources for research and other purposes.

Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

Solvency II came into force on 1 January 2016 and included a transitional measure on technical provisions (“TMTP”) designed to help smooth in the capital impact of Solvency II over a 16-year period. The working party’s view is that the main intention of the TMTP is to mitigate the impact of the introduction of the risk margin, which significantly increases the technical provisions of firms, relative to their Solvency I Pillar 2 liabilities.

The majority of firms who hold a TMTP have now had at least one recalculation approved by the Prudential Regulation Authority (PRA); or are in the process of applying for a recalculation. Despite this large number of approved recalculations, there remains significant uncertainty in the industry around the approach and triggers for recalculation.

This paper considers aspects of TMTP recalculation for regulated UK life firms, for example practicalities of the calculation, asset and liability considerations, and communications/announcements.

In this paper, we outline the need for pragmatism when considering the approach to recalculation of a measure originally intended to serve as the bridge between two regimes. We call for an allowance for doing what is sensible in a principles-based regime balancing what might be more theoretically correct with what is practical and possible to support effective management of the business.

Most agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that the main concern with fixed effects models of rare events data is not inaccurate or inefficient coefficient estimation, but instead biased marginal effects. In short, only evaluating event-experiencing units gives an inaccurate estimate of the baseline risk, yielding inaccurate (often inflated) estimates of predictor effects. As a solution, we propose a penalized maximum likelihood fixed effects (PML-FE) estimator, which retains the complete sample by providing finite estimates of the fixed effects for each unit. We explore the small sample performance of PML-FE versus common alternatives via Monte Carlo simulations, evaluating the accuracy of both parameter and effects estimates. Finally, we illustrate our method with a model of civil war onset.

Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditions required for valid instruments. First, when cross-unit dependence exists in the endogenous predictor, other cross-unit relationships—spillovers and interdependence—likely exist as well and risk violations of the exclusion restriction. Second, spatial instruments produce simultaneity in the first-stage equation, as left-hand side outcomes are included as right-hand side predictors. Because the instrument and the endogenous variable are simultaneously determined, the exclusion restriction is, necessarily and by construction, violated. Taken together, these concerns lead us to conclude that spatial instruments are rarely, if ever, valid.

The discovery of the first electromagnetic counterpart to a gravitational wave signal has generated follow-up observations by over 50 facilities world-wide, ushering in the new era of multi-messenger astronomy. In this paper, we present follow-up observations of the gravitational wave event GW170817 and its electromagnetic counterpart SSS17a/DLT17ck (IAU label AT2017gfo) by 14 Australian telescopes and partner observatories as part of Australian-based and Australian-led research programs. We report early- to late-time multi-wavelength observations, including optical imaging and spectroscopy, mid-infrared imaging, radio imaging, and searches for fast radio bursts. Our optical spectra reveal that the transient source emission cooled from approximately 6 400 K to 2 100 K over a 7-d period and produced no significant optical emission lines. The spectral profiles, cooling rate, and photometric light curves are consistent with the expected outburst and subsequent processes of a binary neutron star merger. Star formation in the host galaxy probably ceased at least a Gyr ago, although there is evidence for a galaxy merger. Binary pulsars with short (100 Myr) decay times are therefore unlikely progenitors, but pulsars like PSR B1534+12 with its 2.7 Gyr coalescence time could produce such a merger. The displacement (~2.2 kpc) of the binary star system from the centre of the main galaxy is not unusual for stars in the host galaxy or stars originating in the merging galaxy, and therefore any constraints on the kick velocity imparted to the progenitor are poor.

Media-based event data—i.e., data comprised from reporting by media outlets—are widely used in political science research. However, events of interest (e.g., strikes, protests, conflict) are often underreported by these primary and secondary sources, producing incomplete data that risks inconsistency and bias in subsequent analysis. While general strategies exist to help ameliorate this bias, these methods do not make full use of the information often available to researchers. Specifically, much of the event data used in the social sciences is drawn from multiple, overlapping news sources (e.g., Agence France-Presse, Reuters). Therefore, we propose a novel maximum likelihood estimator that corrects for misclassification in data arising from multiple sources. In the most general formulation of our estimator, researchers can specify separate sets of predictors for the true-event model and each of the misclassification models characterizing whether a source fails to report on an event. As such, researchers are able to accurately test theories on both the causes of and reporting on an event of interest. Simulations evidence that our technique regularly outperforms current strategies that either neglect misclassification, the unique features of the data-generating process, or both. We also illustrate the utility of this method with a model of repression using the Social Conflict in Africa Database.

Spatial/spatiotemporal interdependence—that is, that outcomes, actions or choices of some unit-times depend on those of other unit-times—is substantively important and empirically ubiquitous in binary outcomes of interest across the social sciences. Estimating and interpreting binary-outcome models that incorporate such spatial/spatiotemporal dynamics directly is difficult and rarely attempted, however. This article explains the inferential challenges posed by spatiotemporal interdependence in binary-outcome models and recent advances in their estimation. Monte Carlo simulations compare the performance of one of these consistent and asymptotically efficient methods (maximum simulated likelihood, using recursive importance sampling) to estimation strategies naïve about (inter-) dependence. Finally, it shows how to calculate, in terms of probabilities of outcomes, the estimated spatial/spatiotemporal effects of (and response paths to) hypotheticals of substantive interest. It illustrates with an application to civil war in Sub-Saharan Africa.

Research was undertaken to clarify the true taxonomic position of the terrestrial tortoise apicomplexan, Haemogregarina fitzsimonsi (Dias, 1953). Thin blood films were screened from 275 wild and captive South African tortoises of 6 genera and 10 species between 2009–2011. Apicomplexan parasites within films were identified, with a focus on H. fitzsimonsi. Ticks from wild tortoises, especially Amblyomma sylvaticum and Amblyomma marmoreum were also screened, and sporogonic stages were identified on dissection of adult ticks of both species taken from H. fitzsimonsi infected and apparently non-infected tortoises. Parasite DNA was extracted from fixed, Giemsa-stained tortoise blood films and from both fresh and fixed ticks, and PCR was undertaken with two primer sets, HEMO1/HEMO2, and HepF300/HepR900, to amplify parasite 18S rDNA. Results indicated that apicomplexan DNA extracted from tortoise blood films and both species of tick had been amplified by one or both primer sets. Haemogregarina  fitzsimonsi 18S rDNA sequences from tortoise blood aligned with those of species of Hepatozoon, rather than those of species of Haemogregarina or Hemolivia. It is recommended therefore that this haemogregarine be re-assigned to the genus Hepatozoon, making Hepatozoon fitzsimonsi (Dias, 1953) the only Hepatozoon known currently from any terrestrial chelonian. Ticks are its likely vectors.

Currently, there is significant ongoing research into the temporal and spatial variability of marine radiocarbon reservoir effects (MREs) through quantification of ΔR values. In turn, MRE studies often use large changes in ΔR values as proxies for changes in ocean circulation. ΔR values are published in a variety of formats with variations in how the errors on these values are calculated, making it difficult to identify trends or to compare values, unless the method of calculating the ΔR is explicitly described or all of the data are made available in the publication. This paper demonstrates the large range in ΔR values (+34 to −122) that can be obtained from a single, secure archaeological context when using the multiple paired sample approach, despite the fact that the terrestrial entities were of statistically indistinguishable 14C ages, as were the marine samples. This demonstrates the inherent variability in the ΔR calculations themselves and we propose that, together with calculation of mean ΔR, the distribution of ΔR values should be displayed, e.g. as histograms in order to illustrate the full data range. This spread is only apparent when employing a multiple paired sample approach as the uncertainty derived on a single pair of samples, taking account only of the errors on the individual 14C ages, will never truly represent the overall variability in ΔR that results from the intrinsic variability in the population of 14C ages in samples that might have been used. Consequently, ΔR values and the associated uncertainty calculated from single pairs should be treated with some caution. We propose that, where possible, when using paired archaeological samples, that a multiple paired approach should be employed as it will test the context security of the material used in the ΔR calculations. When summarizing the values by the weighted average, we also propose that the standard error for predicted values should be employed as this will fully encompass the uncertainty of a future ΔR calculation, using different samples for a similar time and location. Finally, we encourage future publishing of ΔR values using the histogram format, making all of the data available. This will help ensure that ΔR values are comparable across the literature and should provide a framework for standardization of publication methods.