In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models

Francisco Blasques (), Siem Jan Koopman, Katarzyna Łasak and Andre Lucas

No 15-083/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The bands are applicable to a large class of observation driven models and a wide range of estimation procedures. A Monte Carlo study is conducted for time-varying parameter models such as generalized autoregressive conditional heteroskedasticity and autoregressive conditional duration models. Our results show clear differences between the actual coverage provided by the different methods. We illustrate our findings in a volatility analysis for monthly Standard & Poor’s 500 index returns.

Keywords: autoregressive conditional duration; delta-method; generalized autoregressive (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
Date: 2015-07-09
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20150083

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