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ARMA误差不相关性检验：非标准Andrews–Ploberger检验AI 解读

A problem of interest in economic and finance applications is testing whether ARMA (Autoregressive moving average) errors are uncorrelated under weak assumptions, namely assumptions where the errors are neither iid nor a martingale difference. In this paper, non‐standard versions of the tests of serial correlation introduced by Andrews and Ploberger (1996, hereafter AP) are proposed for diagnostic checking of ARMA errors. The original AP tests are designed for the case where…

序列相关检验：广义Andrews–Ploberger检验AI 解读

This paper considers testing the null hypothesis that a times series is uncorrelated when the time series is uncorrelated but statistically dependent. This case is of interest in economic and finance applications. The GARCH(1, 1) model is a leading example of a model that generates serially uncorrelated but statistically dependent data. The tests of serial correlation introduced by Andrews and Ploberger (1996, hereafter AP) are generalized for the purpose of testing the…

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统计依赖下的零自相关检验AI 解读

The problem addressed in this paper is to test the null hypothesis that a time series process is uncorrelated up to lag K in the presence of statistical dependence. We propose an extension of the Box–Pierce Q -test that is asymptotically distributed as chi-square when the null is true for a very general class of dependent processes that includes non-martingale difference sequences. The test is based on a consistent estimator of the asymptotic covariance matrix of the sample…

含趋势的AR(1)模型中自助t检验的水平与功效AI 解读

This paper considers a first-order autoregressive model which may include an intercept and trend where the innovations are independently and identically distributed. The innovation distribution is assumed unknown. The autoregressive parameter is tested using the conventional t statistic. The paper presents Monte Carlo estimates of the rejection probability of the test with bootstrap-based critical values. The results show that the test with the bootstrap-based critical value…

含趋势 AR(1) 模型中 Bootstrap t 检验的水平与功效AI 解读

This paper considers a first-order autoregressive model which may include an intercept and trend where the innovations are independently and identically distributed. The innovation distribution is assumed unknown. The autoregressive parameter is tested using the conventional t statistic. The paper presents Monte Carlo estimates of the rejection probability of the test with bootstrap-based critical values. The results show that the test with the bootstrap-based critical value…

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ARMA 模型中的镜像与不变分布AI 解读

The finite sample distributions of estimators and test statistics in ARMA time series models are generally unknown. For typical sample sizes, the approximations provided by asymptotic distributions are often unsatisfactory. Hence simulation or numerical integration methods are used to investigate the distributions. In practice only a limited part of the parameter space is examined using these methods. Thus any results which allow us to infer properties from one portion of…