The method of mortality forecasting proposed by Lee and Carter describes a time series of age-specific log-death rates as a sum of an independent of time age-specific component and a bilinear term in which one of the component is a time-varying factor reflecting general change in mortality and the second one is an age-specific parameter. Such a rigid model structure implies that on average the mortality improvements for different age groups should be proportional, regardless of the calendar period: a single time factor drives the future death rates. This paper investigates the use of multivariate time series techniques for forecasting age-specific death rates. This approach allows for relative speed of decline in the log death rates specific to the different ages. The dynamic factor analysis and the Johansen cointegration methodology are successfully applied to project mortality. The inclusion of several time factors allows the model to capture the imperfect correlations in death rates from 1 year to the next. The benchmark Lee-Carter model appears as a special case of these approaches. An empirical study is conducted with the help of the Johansen cointegration methodology. A vector-error correction model is fitted to Belgian general population death rates. A comparison is performed with the forecast of life expectancies obtained from the classical Lee-Carter model.

We investigate the relationships between pollution and growth in eleven Central and Eastern European (CEE) countries. Aggregate results, robust to different estimators and control variables, reveal an increasing nonlinear link between GDP and CO2 for the group of CEE countries. However, at a disaggregated, country-level, the relationship between GDP and CO2 is characterized by much diversity among CEE countries, namely: N-shaped, inverted-N, U-shaped, inverted-U, monotonic, or no statistical link. Thus, despite an aggregated upward trend, some CEE countries managed to secure both higher GDP and lower CO2 emissions. From a policy perspective, EU policymakers could pay more attention to these countries, and amend the current unique environmental policy to account for country-heterogeneities in order to support economic growth without damaging the environment.

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This article studies the dynamic relationship between premiums and losses on the U.S. property-casualty insurance market, accounting for the external impacts of GDP and interest rate. Compared to the existing literature, the present work innovates in that the dynamic relationships between premiums, losses, GDP, and interest rate are studied in a cointegration framework, single-equation and vector approach, involving the long-and shortrun dynamics. The results suggest a stable long-run equilibrium between premiums, losses, and general economy. On short term, the premiums adjust quickly and significantly to the long-term disequilibrium and have a strong autoregressive behavior. External factors contribute to explain the dynamics of premiums.