Chronological changes of government sectors’ fiscal policies and fiscal sustainability in Japan

Since the end of the 1990 s, the sluggish growth of Japan’s gross domestic product (GDP) and the mired fiscal state of its public sector have provoked consternation about its public sector’s fiscal sustainability. Therefore, I estimated the fiscal reaction functions (stemming from Bohn, 1998a, 2008) with time-varying parameters for all Japan’s government sectors (for 1976Q2–2020Q1), i.e., the general government (GG), the central government (CG), the whole of the local governments (WLG), and the whole of the social security funds (WSSF), to chronologically assess their fiscal sustainability using four different models, including a least-squares with breakpoints model and a state-space model with the Kalman filter. My results demonstrate that (1) the least-squares with breakpoints model outperformed the others, and (2) although CG, WLG, and WSSF often sustainably managed their finances during the analysis term, GG has failed to implement a sustainable fiscal policy from the mid-1990 s (3) CG and WSSF adjusted their fiscal postures according to Japan’s economic state. Fiscal severity caused WLG to change its fiscal posture.     

采用降秩多级维纳滤波器的二维DOA估计快速算法

针对双平行线阵的二维波达方向（DOA）估计问题,为有效降低计算复杂度,提出了一种基于降秩多级维纳滤波器（MSWF）的快速算法。首先利用MSWF的前向递推实现信号子空间的快速估计,无需估计协方差矩阵和特征分解;然后,通过MUSIC算法对方位角和俯仰角的估计进行分维估计,使二维DOA估计退化为两个一维DOA估计问题,且方位角和俯仰角自动配对,进一步降低了运算量。仿真结果表明,该方法的估计精度优于同样基于双平行线阵提出的波达方向矩阵法（DOAM）,俯仰角兼并时同样适用,计算复杂度低,适用于实时性要求高的应用背景。     

基于改进粒子滤波的GPS接收机自主完好性监测算法

针对基本粒子滤波重采样过程中粒子权值退化和多样性丧失的问题,将遗传算法引入基于神经网络的权值调整粒子滤波算法中,结合了遗传算法全局寻优的收敛性与神经网络局部寻优的快速性优点。将提出的算法与对数似然比方法结合用于GPS接收机自主完好性监测,通过建立一致性检验统计量实现对故障卫星的检测与隔离。通过采集实测数据进行验证,结果表明：该算法可以成功检测和隔离故障卫星,其性能优于基于基本粒子滤波的接收机自主完好性监测,验证了该算法应用于GPS接收机自主完好性监测的可行性和有效性。     

Do the Hamilton and Beveridge–Nelson filters provide the same information about output gaps? An empirical comparison for practitioners

I compare the empirical performances of the recently-developed Hamilton and Beveridge–Nelson filters of nonstationary time series, using quarterly data on real gross state product in U.S. states. There is meaningful overlap between the two filters, with average correlation coefficients ranging between 0.60 and 0.97. The Hamilton filter and its more recent modification produce cycles of greater volatility and amplitude than the Beveridge–Nelson filter and appear to outperform in pseudo-out-of-sample forecasting exercises of future GSP growth and inflation (though the outperformance is not generally statistically significant). The Beveridge–Nelson filter is, however, less sensitive to realizations of new data.     

The role of observed and unobserved heterogeneity in the duration of unemployment

This paper studies the degree to which observable and unobservable worker characteristics account for the variation in the aggregate duration of unemployment. I model the distribution of unobserved worker heterogeneity as time varying to capture the interaction of latent attributes with changes in labor-market conditions. Unobserved heterogeneity is the main explanation for the duration dependence of unemployment hazards. Both cyclical and low-frequency variations in the mean duration of unemployment are mainly driven by one subgroup: workers who, for unobserved reasons, stay unemployed for a long time. In contrast, changes in the composition of observable characteristics of workers have negligible effects.     

卡尔曼滤波在iBeacon室内定位系统中的应用

为了抑制iBeacon信号在传播过程中由于人员扰动、多径效应等引起的噪声，以及移动定位过程中定位结果的不稳定，在iBeacon室内定位系统中引入了卡尔曼滤波算法分别对采集到的iBeacon信号与移动定位结果进行处理。首先通过卡尔曼滤波对采集的信号进行处理，降低了信号噪声，提高了信号的质量，其次应用卡尔曼滤波对移动定位结果进行二次处理，提高了移动定位结果的稳定性与精度。实验结果表明,在办公室环境下采用卡尔曼滤波进行处理后可将1 m以内的定位精度提高到80%以上。