A0359
Title: Inference for the panel ARMA-GARCH model when both N and T are large
Authors: Bing Su - The University of Hong Kong (China) [presenting]
Ke Zhu - University of Hong Kong (Hong Kong)
Abstract: A panel ARMA-GARCH model is proposed to capture the dynamics of large panel data with N individuals over T time periods. For this model, a two-step estimation procedure is provided to estimate the ARMA parameters and GARCH parameters stepwisely. Under some regular conditions, it is shown that all of the proposed estimators are asymptotically normal with the convergence rate $1/\sqrt(NT)$, and they have asymptotic biases when both N and T diverge to infinity at the same rate. Particularly, the asymptotic biases result is found from the fixed effect, estimation effect, and unobservable initial values. To correct the biases, the bias-corrected version is further proposed by estimators by using either the analytical asymptotics or the jackknife method. The asymptotic results are based on a new central limit theorem for the linear-quadratic form in the martingale difference sequence when the weight matrix is uniformly bounded in rows and columns. Simulations and one real example are given to demonstrate the usefulness of the panel ARMA-GARCH model.
