统计与管理学院2015年学术报告第52期
【主 题】Identifiability and Estimation of AR-ARCH Models with Measurement Error
【报告人】 Dr. Liqun Wang
University of Manitoba, Canada
【时 间】 2015年11月20日（星期五）15:00－16:00
【地 点】 上海财经大学统计与管理学院大楼1208室
【语 言】 英文
【摘 要】The autoregressive conditional heteroscedasticity (ARCH) model and its various generalizations have been widely used to analyze economic and financial data. Although many variables like GDP, inflation and commodity prices are imprecisely measured, the problem of measurement error in ARCH-type models has not been studied in the literature. We study a dynamic model with ARCH error where the underlying process is latent and subject to additive measurement error. We show that, in contrast to the case of covariate measurement error in regression models, this model is identifiable by using the observations of a proxy process only and no extra information is needed. We propose GMM estimators for the unknown parameters that are consistent and asymptotically normally distributed under general conditions. We also propose a procedure to test the presence of measurement error, which avoids the usual boundary problem of testing variance parameters. We carry out Monte Carlo simulations to study the impact of measurement error on the naive maximum likelihood estimators and have found interesting evidence of possible mathematical formulas of the biases. Moreover, the proposed estimators have fairly good finite sample properties.
This is joint work with Mustafa Salamh.
【邀请人】尤进红
