Is the US Phillips curve stable? Evidence from Bayesian vector autoregressions*

It has been claimed that the fall in US inflation during the Great Recession was surprisingly small. One possible explanation for this is that the Phillips curve is unstable and that its slope was lower around the Great Recession. We investigate the importance of time-varying parameters using Bayesian vector autoregressions for inflation and unemployment. We find support for time variation in the inflation equation and an unstable Phillips curve that was somewhat flatter between 2005 and 2013. However, conditional forecasts mostly suggest that inflation was not unexpectedly high around the Great Recession, which puts the claim of a “missing disinflation” into question.     

A note of caution on the relation between money growth and inflation

We assess the bivariate relation between money growth and inflation in the euro area and the United States using hybrid time-varying parameter Bayesian VAR models. Model selection based on marginal likelihoods suggests that the relation is statistically unstable across time in both regions. The effect of money growth on inflation weakened notably after the 1980s before strengthening after 2020. There is evidence that this time variation is related to the pace of price changes, as we find that the maximum impact of money growth on inflation is increasing in the trend level of inflation. These results caution against asserting a simple, time-invariant relationship when modeling the joint dynamics of monetary aggregates and consumer prices.     

电力电缆实时载流量评估系统设计

为了提高电力电缆的利用率,合理调配电缆的实时负荷,利用实时环境参数监测终端、GPRS数据传输模块和上位机构成了高压电力电缆实时载流量评估系统。并利用DS18B20、风力传感器、土壤温湿度传感器、光照传感器和STM32嵌入式单片机对环境参数进行实时监测,实现了对环境温度、土壤湿度、土壤温度、环境风力、环境光照等参数的实时采集,并利用GPRS数据传输模块将环境参数传送到上位机,利用上位机结合VB6.0和ANSYS编制了高压电力电缆温度场和载流量计算软件,实现了电力电缆参数数据库存储、敷设模式界面化操作、环境参数的实时监测与显示、实时动态载流量的在线计算等功能。计算结果表明,高压电力电缆的实时动态载流量系统能够准确计算出受环境参数影响的高压电力电缆的载流量,提高了计算效率,为电力部门调配电缆的实时负荷提供了依据。     

灰色组合预测模型优化及科技人才需求预测

科技人才需求预测是国家合理制订人才政策的重要依据。为此，本文基于科技人才需求的数据特征，构建适用于科技人才需求预测的新型离散灰色模型FODGM(r,1,kθ,u)，该模型实现了系统发展灰信息非线性规律的较好反映以及累加阶数作用范围全实域拓展，缓解了原始序列中极值对模型性能的影响，能够有效模拟科技人才需求的发展趋势与演变规律。应用该模型对我国科技人才需求量进行预测，结果显示未来我国科技人才需求量呈逐步上升趋势，预计2026年我国科技人员全时当量将达729.258万人年，科技人才需求端压力较大。相关部门可以根据预测结果制定缓解我国科技人才需求端压力的对策。