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A CENTRAL LIMIT THEOREM FOR MIXING TRIANGULAR ARRAYS OF VARIABLES WHOSE DEPENDENCE IS ALLOWED TO GROW WITH THE SAMPLE SIZE

依赖程度可随样本量增长的强混合三角阵列中心极限定理

Conditions ensuring a central limit theorem for strongly mixing triangular arrays are given. Larger samples can show longer range dependence than shorter samples. The result is obtained by constraining the rate growth of dependence as a function of the sample size, with the usual trade-off of memory and moment conditions. An application to heteroskedasticity and autocorrelation consistent estimators is proposed.

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Diagnostic Checking in ARMA Models With Uncorrelated Errors

误差不相关的 ARMA 模型诊断检验

We consider tests for lack of fit in ARMA models with nonindependent innovations. In this framework, the standard Box–Pierce and Ljung–Box portmanteau tests can perform poorly. Specifically, the usual text book formulas for asymptotic distributions are based on strong assumptions and should not be applied without careful consideration. In this article we derive the asymptotic covariance matrix of a vector of autocorrelations for residuals of ARMA models under weak…

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COMMENTS ON THE PAPER BY MINXIAN YANG: “SOME PROPERTIES OF VECTOR AUTOREGRESSIVE PROCESSES WITH MARKOV-SWITCHING COEFFICIENTS”

对马尔可夫转换系数向量自回归过程性质一文的评论

This paper discusses the stationarity conditions proposed by M. Yang (2000, Econometric Theory 16, 23–43), in the framework of Markov-switching first-order autoregressions. A weaker second-order stationarity assumption is proposed.

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ESTIMATING WEAK GARCH REPRESENTATIONS

弱 GARCH 表示的估计

The classical definitions of GARCH-type processes rely on strong assumptions on the first two conditional moments. The common practice in empirical studies, however, has been to test for GARCH by detecting serial correlations in the squared regression errors. This can be problematic because such autocorrelation structures are compatible with severe misspecifications of the standard GARCH. Numerous examples are provided in the paper. In consequence, standard (quasi-) maximum…

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QUASI-INDIRECT INFERENCE FOR DIFFUSION PROCESSES

扩散过程的准间接推断

We discuss an estimation procedure for continuous-time models based on discrete sampled data with a fixed unit of time between two consecutive observations. Because in general the conditional likelihood of the model cannot be derived, an indirect inference procedure following Gouriéroux, Monfort, and Renault (1993, Journal of Applied Econometrics 8, 85–118) is developed. It is based on simulations of a discretized model. We study the asymptotic properties of this…

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