A new integer-valued autoregressive process (INAR) with Generalised Lagrangian Katz (GLK) innovations is defined. We show that our GLK-INAR process is stationary, discrete semi-self-decomposable, infinite divisible, and provides a flexible modelling framework for count data allowing for under- and over-dispersion, asymmetry, and excess of kurtosis. A Bayesian inference framework and an efficient posterior approximation procedure based on Markov Chain Monte Carlo are provided. The proposed model family is applied to a Google Trend dataset which proxies the public concern about climate change around the world. The empirical results provide new evidence of heterogeneity across countries and keywords in the persistence, uncertainty, and long-run public awareness level.
翻译:定义了具有普遍Lagrangian Katz(GLK)创新的新的整数值自动递减进程(INAR ) 。 我们显示,我们的GLK-INAR进程是固定的、离散的半自毁的、无限的可变的,并且为计算数据提供了一个灵活的建模框架,允许存在偏差、不对称和超量的幼崽病数据。提供了巴耶斯推论框架和基于Markov Cain Call Monte Carlo的高效远地点近似程序。 拟议的模型系列适用于谷歌趋势数据集,该数据集代表着公众对世界各地气候变化的关切。 实证结果为各国和关键词之间在持久性、不确定性和长期公众意识水平上的异质性提供了新的证据。