Inference for Local Autocorrelations in Locally Stationary Models
研究了局部平稳时间序列中局部自相关过程的估计与推断，构建了同时置信带用于检验时变性和零自相关，推广了R函数acf()到局部平稳高斯过程，并通过全球温度序列和S&P 500指数实证展示了方法的应用。
For non-stationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a "V" shape during 1910-1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.