Probabilistic population forecasting: Short to very long-term

Population forecasts are used by governments and the private sector for planning, with horizons up to about three generations (around 2100) for different purposes. The traditional methods are deterministic using scenarios, but probabilistic forecasts are desired to get an idea of accuracy, assess changes, and make decisions involving risks. In a significant breakthrough, since 2015, the United Nations has issued probabilistic population forecasts for all countries using a Bayesian methodology that we review here. Assessment of the social cost of carbon relies on long-term forecasts of carbon emissions, which in turn depend on even longer-range population and economic forecasts, to 2300. We extend the UN method to very-long range population forecasts by combining the statistical approach with expert review and elicitation. While the world population is projected to grow for the rest of this century, it will likely stabilize in the 22nd century and decline in the 23rd century.     

Labour market rigidity and expansionary austerity

This study provides new evidence on how labour market rigidities affect the transmission of fiscal consolidations using a sample of 17 OECD countries. Owing to a novel empirical approach, the outcomes of consolidations are modelled as a function of employment and wage rigidities. The evidence confirms that tax-based consolidations are distortionary, while expenditure-based consolidations have wealth effects. These effects are then magnified by flexible employment and rigid wages, while they are moderated by rigid employment and flexible wages. This indicates that labour market conditions influence how fiscal consolidation is propagated in the economy by affecting both the magnitude and the transmission channels of consolidation plans. This result has crucial policy implications and suggests that the design of consolidation plans should account for the labour market structure.     

The non-linear response of US state-level tradable and non-tradable inflation to oil shocks: The role of oil-dependence

This paper investigates the effects of oil supply, oil-specific consumption demand, oil inventory demand shocks, and global economic activity shocks on state-level tradable and non-tradable inflation in the US. We use oil shock data following the work of Baumeister and Hamilton (2019) and estimate both linear and non-linear impulse responses using a lag-augmented local projections model in a panel context. Our results from a linear model show that both supply and demand-side oil shocks have a statistically significant impact on both types of inflation. While supply, global economic activity, and demand shocks have a greater impact on tradable inflation, non-tradable inflation responds more strongly to inventory shocks. Further, the non-linear model results provide evidence of heterogeneity in the magnitude and persistence of impact between high- and low-oil dependence regimes. Non-tradable inflation is more sensitive to nearly all components of oil price shocks in the high-oil dependence regime.     

Multi-population mortality projection: The augmented common factor model with structural breaks

Multi-population mortality forecasting has become an increasingly important area in actuarial science and demography, as a means to avoid long-run divergence in mortality projections. This paper aims to establish a unified state-space Bayesian framework to model, estimate, and forecast mortality rates in a multi-population context. In this regard, we reformulate the augmented common factor model to account for structural/trend changes in the mortality indexes. We conduct a Bayesian analysis to make inferences and generate forecasts so that process and parameter uncertainties can be considered simultaneously and appropriately. We illustrate the efficiency of our methodology through two case studies. Both point and probabilistic forecast evaluations are considered in the empirical analysis. The derived results support the fact that the incorporation of stochastic drifts mitigates the impact of the structural changes in the time indexes on mortality projections.     

投影寻踪模型在行蓄洪区运用风险评价中的应用

将行蓄洪区运用风险评价指标体系应用于基于信息熵的投影寻踪模型,得到风险评价投影值。根据结果把淮河干流行蓄洪区运用风险等级分为4类,风险等级及分类区间从高到低依次为Ⅰ级,投影值≥4.16,包括4个蓄洪区和寿西湖、鲍集圩2个行洪区;Ⅱ级,投影值为4.10~4.16,包括汤渔湖等4个行洪区;Ⅲ级,投影值为3.90~4.16,包括香浮段等7个行洪区;Ⅳ级,投影值为0~3.90,包括邱家湖等4个行洪区。