LADE-based inferences for autoregressive models with heavy-tailed G-GARCH(1, 1) noise
重尾 G-GARCH(1,1) 噪声下自回归模型的 LAD 推断
This paper explores the least absolute deviation (LAD) estimator of the autoregressive model with heavy-tailed G-GARCH(1, 1) noise. When the tail index α∈(1,2], it is shown that the LAD estimator asymptotically converges to a linear function of a series of α-stable random vectors with a rate of convergence n1−1/α. The result is significantly different from that of the corresponding least square estimator which is not consistent, and partially solves the problem on the…