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Missing Observations in Observation-Driven Time Series Models

Francisco Blasques (), Paolo Gorgi and Siem Jan Koopman ()
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Paolo Gorgi: VU Amsterdam, The Netherlands

No 18-013/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We argue that existing methods for the treatment of missing observations in 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 are formally derived. Our proposed method shows a promising performance in both a Monte Carlo study and 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)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2018-02-09
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