Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

A Note on Proxy Variables and Instrument Variable Regression

This is a note about proxy variables and instruments for identification of structural parameters in regression models. We have experienced that in the econometric textbooks these two issues are treated separately, although in practice these two concepts are very often combined. Usually, proxy variables are inserted in instrument variable regressions with the motivation they are exogenous. Implicitly meaning they are exogenous in a reduced form model and not in a structural model. Actually if these variables are exogenous they should be redundant in the structural model, e.g. IQ as a proxy for ability. Valid proxies reduce unexplained variation and increases the efficiency of the estimator of the structural parameter of interest. This is especially important in situations when the instrument is weak. With a simple example we demonstrate what is required of a proxy and an instrument when they are combined. It turns out that when a researcher has a valid instrument the requirements on the proxy variable is weaker than if no such instrument exists

Exogenous expectations on endogenous uncertainty: recursive equilibrium and survival

This paper analyses general equilibrium models with finite heterogeneous agents having exogenous expectations on endogenous uncertainty. It is shown that there exists a recursive equilibrium with the state space consisting of the past aggregate portfolio distribution and the current state of the nature and that it implements the sequential equilibrium. We establish conditions under which the recursive equilibrium is continuous. Moreover, we use the continuous recursive relation of the aggregate variables to prove that if the economy has two types of agents, the one who commits persistent mistakes on the expectation rules of the future endogenous variables is driven out of the market by the others with correct anticipations of the variables, that is, the rational expectations agents.

Mechanical Measurements Of Two Exogenous Surfactants Using A Langmuir Film Balance:A Comparative Study

Rationale: The primary function of surfactant is to reduce the surface tension at air-liquid interface. In this study, the surface tension behavior of two commercial surfactants, poractant alfa (ChiesiFarmaceuticals,ltaly) and beractant (Abbott Laboratories,USA), were evaluated,using new parameters. Methods: We used a Langmuir film balance (Minitrough,KSV lnstruments,Finland) to measure of surface tension of both poractant alfa and beractant samples. For both samples,we prepared a solution of 1 mg/mdl dissolved in chloroform. The solution (1uL) was applied over a subphase of milli-Q water (175 ml) in the chamber of the balance. The chamber has two moving barriers that can change its surface area between a maximum value of 112.5 cm2 anda minimum value of 22.5 cm2, defining a balance cycle.lhree sample's films were evaluated for each sample, during 20 balance cycles. Here quantify two new variables, which is the mean hysteresis area of the measured curve surface tension of the last 16 balance cycles,defined here as Mean Work Cycle (MWC), and the moment that the surfactant is active in the surface, this measure is defined here as Active Surface Area Critical in the compression (ASACC) and the expansion (ASACE). The test was applied to compare the statistical significance of the results.

Removing exogenous information using pedigree data

Management of certain populations requires the preservation of its pure genetic background. When, for different reasons, undesired alleles are introduced, the original genetic conformation must be recovered. The present study tested, through computer simulations, the power of recovery (the ability for removing the foreign information) from genealogical data. Simulated scenarios comprised different numbers of exogenous individuals taking partofthe founder population anddifferent numbers of unmanaged generations before the removal program started. Strategies were based on variables arising from classical pedigree analyses such as founders? contribution and partial coancestry. The ef?ciency of the different strategies was measured as the proportion of native genetic information remaining in the population. Consequences on the inbreeding and coancestry levels of the population were also evaluated. Minimisation of the exogenous founders? contributions was the most powerful method, removing the largest amount of genetic information in just one generation.However, as a side effect, it led to the highest values of inbreeding. Scenarios with a large amount of initial exogenous alleles (i.e. high percentage of non native founders), or many generations of mixing became very dif?cult to recover, pointing out the importance of being careful about introgression events in population

Androgen responsiveness to competition in humans: the role of cognitive variables

Although androgens are commonly seen as male sex hormones, it has been established over the years that in both sexes, androgens also respond to social challenges. To explain the socially driven changes in androgens, two theoretical models have been proposed: the biosocial model and the challenge hypothesis. These models are typically seen as partly overlapping; however, they generate different predictions that are clarified here. In humans, sports competition and nonmetabolic competitive tasks have been used in the laboratory setting, as a proxy for agonistic interactions in animals. The results reviewed here show that the testosterone (T) response to competition in humans is highly variable – the studies present postcompetition T levels and changes in T that depend on the contest outcome and that cannot be predicted by the current theoretical models. These conflicting results bring to the foreground the importance of considering cognitive factors that could moderate the androgen response to competition. Among these variables, we elect cognitive appraisal and its components as a key candidate modulating factor. It is known that T also modulates the cognitive processes that are relevant to performance in competition. In this article, we reviewed the evidence arising from studies investigating the effect of administering exogenous T and compare those results with the findings from studies that measured endogenous T levels. Finally, we summarized the importance of also considering the interaction between androgens and other hormones, such as cortisol, when investigating the social modulation of T, as proposed by the dual-hormone hypothesis.

The MIMIC model and formative variables:problems and solutions

The use of the multiple indicators, multiple causes model to operationalize formative variables (the formative MIMIC model) is advocated in the methodological literature. Yet, contrary to popular belief, the formative MIMIC model does not provide a valid method of integrating formative variables into empirical studies and we recommend discarding it from formative models. Our arguments rest on the following observations. First, much formative variable literature appears to conceptualize a causal structure between the formative variable and its indicators which can be tested or estimated. We demonstrate that this assumption is illogical, that a formative variable is simply a researcher-defined composite of sub-dimensions, and that such tests and estimates are unnecessary. Second, despite this, researchers often use the formative MIMIC model as a means to include formative variables in their models and to estimate the magnitude of linkages between formative variables and their indicators. However, the formative MIMIC model cannot provide this information since it is simply a model in which a common factor is predicted by some exogenous variables—the model does not integrate within it a formative variable. Empirical results from such studies need reassessing, since their interpretation may lead to inaccurate theoretical insights and the development of untested recommendations to managers. Finally, the use of the formative MIMIC model can foster fuzzy conceptualizations of variables, particularly since it can erroneously encourage the view that a single focal variable is measured with formative and reflective indicators. We explain these interlinked arguments in more detail and provide a set of recommendations for researchers to consider when dealing with formative variables.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.

Identification of critical input variables for risk-based cost estimates for road maintenance and rehabilitation

An estimation of costs for maintenance and rehabilitation is subject to variation due to the uncertainties of input parameters. This paper presents the results of an analysis to identify input parameters that affect the prediction of variation in road deterioration. Road data obtained from 1688 km of a national highway located in the tropical northeast of Queensland in Australia were used in the analysis. Data were analysed using a probability-based method, the Monte Carlo simulation technique and HDM-4’s roughness prediction model. The results of the analysis indicated that among the input parameters the variability of pavement strength, rut depth, annual equivalent axle load and initial roughness affected the variability of the predicted roughness. The second part of the paper presents an analysis to assess the variation in cost estimates due to the variability of the overall identified critical input parameters.

Using Trauma Injury Severity Score (TRISS) variables to predict length of hospital stay following trauma in New Zealand

Aim – To develop and assess the predictive capabilities of a statistical model that relates routinely collected Trauma Injury Severity Score (TRISS) variables to length of hospital stay (LOS) in survivors of traumatic injury. Method – Retrospective cohort study of adults who sustained a serious traumatic injury, and who survived until discharge from Auckland City, Middlemore, Waikato, or North Shore Hospitals between 2002 and 2006. Cubic-root transformed LOS was analysed using two-level mixed-effects regression models. Results – 1498 eligible patients were identified, 1446 (97%) injured from a blunt mechanism and 52 (3%) from a penetrating mechanism. For blunt mechanism trauma, 1096 (76%) were male, average age was 37 years (range: 15-94 years), and LOS and TRISS score information was available for 1362 patients. Spearman’s correlation and the median absolute prediction error between LOS and the original TRISS model was ρ=0.31 and 10.8 days, respectively, and between LOS and the final multivariable two-level mixed-effects regression model was ρ=0.38 and 6.0 days, respectively. Insufficient data were available for the analysis of penetrating mechanism models. Conclusions – Neither the original TRISS model nor the refined model has sufficient ability to accurately or reliably predict LOS. Additional predictor variables for LOS and other indicators for morbidity need to be considered.