Date on Master's Thesis/Doctoral Dissertation
Applied and Industrial Mathematics, PhD
Committee Co-Chair (if applicable)
cross-validation; autoregressive models; time series; order selection; model selection
There are no set rules for choosing the lag order for autoregressive (AR) time series models. Currently, the most common methods employ AIC or BIC. However, AIC has been proven to be inconsistent and BIC is inefficient. Racine proposed an estimator based on Shao's work which he hypothesized would also be consistent, but left the proof as an open problem. We will show his claim does not follow immediately from Shao. However, Shao offered another consistent method for cross validation of linear models called APCV, and we will show that AR models satisfy Shao's conditions. Thus, APCV is a consistent method for choosing lag order. Simulations also show that APCV performs as well, and in some cases, performs better than AIC, AICc, and BIC.
Han, Christina, "Cross-validation for autoregressive models." (2022). Electronic Theses and Dissertations. Paper 3958.