论文标题
随时间变化的泊松自动性
Time-Varying Poisson Autoregression
论文作者
论文摘要
在本文中，我们提出了一种新的时变计量经济学模型，称为随时间变化的泊松自回归，具有外源协变量（TV-PARX），适用于模型和预测时间序列。 {我们表明，以分数为导向的框架特别适合恢复时变参数的演变，并为模型和预测时间序列提供了所需的灵活性，这些计数的特征是以复杂的非线性动力学和结构上断为特征。}我们研究了TV-PARX模型的无效性能，并在轻度条件下，最大程度地估算（Mimement）（Mimemelt commiment）（Mimement）（MM）的均值（MM）。估计。通过蒙特卡洛模拟评估有限样本的性能和预测精度。通过分析意大利的新每日COVID-19感染和美国公司默认的企业默认值的数量，显示了提出的TV-PARX模型的随时间变化规范的经验实用性。
In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts. {We show that the score-driven framework is particularly suitable to recover the evolution of time-varying parameters and provides the required flexibility to model and forecast time series of counts characterized by convoluted nonlinear dynamics and structural breaks.} We study the asymptotic properties of the TV-PARX model and prove that, under mild conditions, maximum likelihood estimation (MLE) yields strongly consistent and asymptotically normal parameter estimates. Finite-sample performance and forecasting accuracy are evaluated through Monte Carlo simulations. The empirical usefulness of the time-varying specification of the proposed TV-PARX model is shown by analyzing the number of new daily COVID-19 infections in Italy and the number of corporate defaults in the US.
