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混频时间序列的潜在因子分析及其应用

• 出版日期:2019-09-25 发布日期:2019-09-25

Latent Factor Analysis for Time Series with Mixed Frequencies and Its Application

Qin Lei et al.

• Online:2019-09-25 Published:2019-09-25

Abstract: Time series generated by macroeconomics are usually assumed to be controlled by a few latent factors. The joint effects of factors lead to the co-movement of series. Factors are important in the analysis and forecasting of time series. However, empirical macroeconomic studies always contain time series with mixed frequencies, which make factor analysis impossible to implement. To this end, this paper proposes two factor analysis methods for time series with mixed frequencies, namely MIDAS-LF and EM-LF. The former benefits from the interpolation of low-frequency sequences by the multivariate MIDAS model, while the latter uses the EM algorithm for iterative solution. The simulation data analysis shows that, compared with methods existing in the literature, MIDAS-LF is better for the analysis of time series with mixed frequencies. The calculation procedure of MIDAS-LF is simple and retains most of the information in original data, which can better estimate the factors and loading matrix, leading to low fitting error and prediction error. The actual data analysis of the macro-economy also confirms the feasibility and correctness of the proposed methods.