This paper investigates the asymptotic theory for a vector ARMA-GARCH model. The conditions for the strict stationarity, ergodicity, and the higherorder moments of the model are established.… (More)

This paper investigates the (conditional) quasi-likelihood ratio test for the threshold in MA models. Under the hypothesis of no threshold, it is shown that the test statistic converges weakly to a… (More)

This paper addresses the problem of fitting a known distribution to the innovation distribution in a class of stationary and ergodic time series models. The asymptotic null distribution of the usual… (More)

This paper obtains the joint limiting distribution of residuals and squared residuals of a general time-series model. Based on this, we propose a mixed portmanteau statistic for testing the adequacy… (More)

This paper studies the residual empirical process of long-and short-memory time series regression models and establishes its uniform expansion under a general framework. The results are applied to… (More)

This paper studies a class of tests useful for testing goodness of fit of a wide variety of time series models. These tests are based on a class of empirical processes marked by certain scores. Major… (More)

This paper investigates the so-called one-step local quasi–maximum likelihood estimator for the unit root process with GARCH ~1,1! errors+ When the scaled conditional errors~the ratio of the… (More)

This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodic time series models. Under simple conditions that are straightforward to check, we establish the… (More)

We investigate the estimation of parameters in the random coefficient autoregressive (RCA) model Xk 1⁄4 (u þ bk)Xk 1 þ ek, where (u, x, r) is the parameter of the process, Eb20 1⁄4 x, Ee0 1⁄4 r. We… (More)

This paper studies the least squares estimator (LSE) of the multiple-regime threshold autoregressive model and establishes its large sample theory. It is shown that the LSE is strongly consistent.… (More)