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Autoren: 
Blasques, Francisco
Gorgi, Paolo
Koopman, Siem Jan
Datum: 
2018
Reihe/Nr.: 
Tinbergen Institute Discussion Paper TI 2018-013/III
Zusammenfassung: 
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.
Schlagwörter: 
missing data
observation-driven models
consistency
indirect inference
volatility
JEL: 
C22
C58
Dokumentart: 
Working Paper
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