Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2018-02-09
Number: 18-013/III
Author-Name: Francisco (F.) Blasques
Author-Workplace-Name: VU Amsterdam, The Netherlands
Author-Name: Paolo Gorgi
Author-Workplace-Name: VU Amsterdam, The Netherlands
Author-Name: Siem Jan (S.J.) Koopman
Author-Workplace-Name: VU Amsterdam, The Netherlands
Title: Missing Observations in Observation-Driven Time Series Models
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.
Classification-JEL: C22, C58
Keywords: missing data, observation-driven models, consistency, indirect inference, volatility
File-Url: https://papers.tinbergen.nl/18013.pdf
File-Format: application/pdf
File-Size: 687475 bytes
Handle: RePEc:tin:wpaper:20180013