Missing observations in observation-driven time series models

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  • F. Blasques, Vrije Universiteit Amsterdam, Tinbergen Institute
  • ,
  • P. Gorgi, Vrije Universiteit Amsterdam, Tinbergen Institute
  • ,
  • S. J. Koopman

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.

TidsskriftJournal of Econometrics
Sider (fra-til)542-568
Antal sider27
StatusUdgivet - apr. 2021

Bibliografisk note

Funding Information:
Blasques is thankful to the Dutch National Science Foundation (NWO) grant VIDI.195.099 for financial support. Koopman acknowledges support from CREATES, Aarhus University, Denmark, funded by Danish National Research Foundation, (DNRF78).

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright 2020 Elsevier B.V., All rights reserved.

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