War and the Reelection Motive: Examining the Effect of Term Limits

This article investigates the relationship between term limits and international conflict. Theories of political survival and diversionary war both imply term limits should play a role in international relations, whereas “permanent referendum theory,” largely motivated by work in American politics, suggests otherwise. Drawing on these theories, we formulate and test competing hypotheses regarding term limits and international crises. Using dyadic militarized interstate disputes data and information on forty-eight democracies with term limits, we uncover strong evidence to support the claim that leaders reaching final terms in office are more likely to initiate conflict than those still subject to reelection. Moreover, we find that the likelihood of conflict initiation is significantly higher during times of recession, but only in the absence of binding term limits. While binding electoral terms and economic downturns are both independently associated with increased levels of conflict initiation, in concert their conditional effects actually counteract each other

Temporal Dynamics and Impact of Climate Factors on the Incidence of Zoonotic Cutaneous Leishmaniasis in Central Tunisia

Old world cutaneous leishmaniasis is a vector-borne disease occurring in rural areas of developing countries. The main reservoirs are the rodents Psammomys obesus and Meriones shawi. Zoonotic Leishmania transmission cycle is maintained in the burrows of rodents where the sand fly Phlebotomus papatasi finds the ideal environment and source of blood meals. In the present study we showed seasonality of the incidence of disease during the same cycle with an inter-epidemic period ranging from 4 to 7 years. We evaluated the impact of climate variables (rainfall, humidity and temperature) on the incidence of zoonotic cutaneous leishmaniais in central Tunisia. We confirmed that the risk of disease is mainly influenced by the humidity related to the months of July to September during the same season and mean rainfall lagged by 12 to 14 months

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

for the ANZICS Centre for Outcome and Resource Evaluation (CORE) of the Australian and New Zealand Intensive Care Society (ANZICS)BACKGROUND Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. METHODS Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. RESULTS The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag ₄₀ and 35% had autocorrelation through to lag ₄₀; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. CONCLUSIONS The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.John L Moran, Patricia J Solomo

Employment, The Labour Force and Unemployment in Australia: A Disagregegated Approach

This article presents statistical analysis of the distributional aspects of labour market developments in the Australian economy from 1966 to 1985. The focus is on the adjustment of the labour force to changes in employment opportunities. Two sets of data for selected age/sex/marital groups are utilised in the analysis: regressions to test for differences in the sensitivity of labour force participation to employment opportunities and a series of tables documenting population adjusted changes in employment and the labour force. The aggregate relationship between employment changes and labour force changes (and hence changes in unemployment) is explained by the disaggregated data. Copyright 1985 The University of Melbourne, Melbourne Institute of Applied Economic and Social Research.