講座主題：Seasonal adjustment of time series observed at mixed frequencies using singular value decomposition with wavelet thresholding

主講嘉賓：林蔚，對外經濟貿易大學國際經濟貿易學院

講座時間：2022年4月27日14:00-16:00

講座地點：騰訊會議ID 909 188 160

嘉賓簡介：林蔚，對外經濟貿易大學國際經濟貿易學院副教授。在加州大學河濱分校獲得經濟學博士學位，主要研究領域包括計量經濟學、季節調整和非參數方法。他在Journal of Econometrics, Journal of Business and Economic Statistics, Journal of Applied Econometrics等國際學術期刊上發表學術論文數篇，主持兩項國家自科基金。

内容摘要：In this paper, we propose a novel seasonal adjustment method that accommodates the time series observed at mixed frequencies and possessing possibly multiple abrupt changes in seasonality, under the assumption that the nonseasonal component is difference stationary. Through a generalized difference, we remove the stochastic trend of the mixed frequency time series. Meanwhile, we express the seasonal component in terms of a matrix with a low rank SVD structure. The right and left singular vectors correspond to the seasonal patterns and their time-varying amplitudes. To estimate the SVD structure of seasonality and thus recover the seasonal component, we propose an effective algorithm that applies the wavelet thresholding technique to the left singular vectors. Our proposed method not only accommodates the persistence feature of seasonality, but also allows for the existence of possibly multiple abrupt changes in seasonality. Using both simulated and real data, we find that (i) when the seasonality is moderate or strong our proposed method performs well and correctly detecting the underlying seasonality structure; and (ii) for single frequency time series, the performance of our proposed method compares well with those of the traditional X-12-ARIMA and SEATS methods, especially in the case when the seasonality is strong.