A statistical model is autoregressive if it predicts future values based on past values (i.e., predicting future stock prices based on past performance).

Trend models are suitable for capturing long-term behavior, whereas autoregressive models are more appropriate for capturing short-term fluctuations. One approach to forecasting is to combine a ...

Learn how ARIMA models use time series data for accurate short-term forecasting. Discover its pros, cons, and essential tips for financial predictions.

In particular, simulating an AR (1) model for the noise term, they found that the standard errors calculated using GLS with an estimated autoregressive parameter underestimated the true standard ...

We describe a Bayesian hierarchical model to analyze autoregressive time series panel data. We develop two algorithms using Markov-chain Monte Carlo methods, a restricted algorithm that enforces ...

Yang Bai, Jian Huang, Rui Li, Jinhong You, SEMIPARAMETRIC LONGITUDINAL MODEL WITH IRREGULAR TIME AUTOREGRESSIVE ERROR PROCESS, Statistica Sinica, Vol. 25, No. 2 (April 2015), pp. 507-527 ...

We propose a multiplicative component conditional autoregressive range (MCCARR) model to capture the "long-memory" effect in volatility. We show both theoretically and empirically that the MCCARR ...