Focused information criterion for locally misspecified vector autoregressive models
Research output: Contribution to journal › Article › Academic › peer-review
This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspecification. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model fit. Any (suxfb03;ciently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspecified parameters. In particular, we show that the inability to consistently estimate locally misspecified parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims.
- Focused information criteria, frequentist model averaging, impulse responses, local misspecification, model selection, model uncertainty, vector autoregressive models, IMPULSE-RESPONSE ANALYSIS, ORDER SELECTION, PREDICTION