Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/177703 
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2018-013/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
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.
Subjects: 
missing data
observation-driven models
consistency
indirect inference
volatility
JEL: 
C22
C58
Document Type: 
Working Paper

Files in This Item:
File
Size
671.36 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.