Oracally Efficient Estimation and Consistent Model Selection for Auto-Regressive Moving Average Time Series with Trend
带趋势ARMA时间序列的预言效率估计与一致模型选择
Summary Most time series that are encountered in practice contain non-zero trend, yet textbook approaches to time series analysis are typically focused on zero-mean stationary auto-regressive moving average (ARMA) processes. Trend is often estimated by ad hoc methods and subtracted from time series, and the residuals are used as the true ARMA noise for data analysis and inference, including parameter estimation, lag selection and prediction. We propose a theoretically…