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高维时间序列的时变网络估计AI 解读

We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method…

COVID-19 的时间趋势：一项面板数据研究AI 解读

In this paper, we study the trending behaviour of COVID-19 data at country level, and draw attention to some existing econometric tools which are potentially helpful to understand the trend better in future studies. In our empirical study, we find that European countries overall flatten the curves more effectively compared to the other regions, while Asia & Oceania also achieve some success, but the situations are not as optimistic elsewhere. Africa and America are still…

非参数回归曲线的多尺度聚类AI 解读

In a wide range of modern applications, we observe a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modelled by a nonparametric regression equation, one may in particular assume that the observed time series can be partitioned into a small number of groups whose members share the same nonparametric regression function. We develop a…

交叉分位相关图：时间序列分位依赖与方向可预测性检验AI 解读

This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional predictability to another time series. We establish the asymptotic distribution of the cross quantilogram and the corresponding test statistic. The limiting distributions depend on nuisance parameters. To construct consistent confidence intervals we employ the stationary bootstrap procedure; we show…

无限阶回归函数的 Nadaraya–Watson 型估计量及其渐近性质AI 解读

We consider a class of nonparametric time series regression models in which the regressor takes values in a sequence space. Technical challenges that hampered theoretical advances in these models include the lack of associated Lebesgue density and difficulties with regard to the choice of dependence structure in the autoregressive framework. We propose an infinite-dimensional Nadaraya-Watson type estimator, and investigate its asymptotic properties in detail under both…

局部多项式 M 回归的一致 Bahadur 表示及加性模型应用AI 解读

We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary processes $\{(Y_{i},\underline{X}_{i})\}$. We establish a strong uniform consistency rate for the Bahadur representation of estimators of the regression function and its derivatives. These results are fundamental for statistical inference and for applications that involve plugging in such estimators into other functionals where some control over higher order…