Missing observations in observation-driven time series models

Francisco Blasques (), P. Gorgi and Siem Jan Koopman

Journal of Econometrics, 2021, vol. 221, issue 2, 542-568

Abstract: We argue that existing methods for the treatment of missing observations in time-varying parameter observation-driven models lead to inconsistent inference. We provide a formal proof of this inconsistency for a Gaussian model with time-varying mean. A Monte Carlo simulation study supports this theoretical result and illustrates how the inconsistency problem extends to score-driven and, more generally, to observation-driven models, which include well-known models for conditional volatility. To overcome the problem of inconsistent inference, we propose a novel estimation procedure based on indirect inference. This easy-to-implement method delivers consistent inference. The asymptotic properties of the new method are formally derived. Our proposed estimation procedure shows a promising performance in a Monte Carlo simulation exercise as well as in an empirical study concerning the measurement of conditional volatility from financial returns data.

Keywords: Missing data; Observation-driven models; Consistency; Indirect inference; Volatility (search for similar items in EconPapers)
JEL-codes: C22 C58 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:221:y:2021:i:2:p:542-568

DOI: 10.1016/j.jeconom.2020.07.043

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