Lag length selection in vector autoregressive models: symmetric and asymmetric lags

Abstract

This study used Monte Carlo simulations to study the performance of alternative lag selection criterion for symmetric lag and asymmetric lag vector autoregressive models. Lag models with short lags and with long lags were considered. The alternative criteria considered were the AIC, SIC, Phillips' Posterior Information Criterion, and Keating's modification of the AIC and SIC. The alternative criteria were evaluated by computing the frequency distribution of lags selected, by computing the out-of-sample forecasting performance of models with lags selected using each criterion, and by comparing the ability of models with lags selected using each criterion to mimic the ‘true’ impulse response functions for the lag model.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 91 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.