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预测能力相等的最大子抽样检验AI 解读

In comparing the accuracy of any two competing forecasts, under the assumption that the loss differentials are covariance stationary, Diebold and Mariano (DM) proposed a test statistic that is asymptotically normal. This test is further studied by Giacomini and White (GW). However, under the DM-GW framework, the estimator of the variance of the DM test can be inaccurate in small samples, which yields size distortions; see, for example, Coroneo and Iacone (CI). To alleviate…

分位数相关与分位数自回归建模AI 解读

In this article, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box–Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from…

利用 lasso 收缩与选择回归系数及自回归阶数AI 解读

Summary The least absolute shrinkage and selection operator (‘lasso’) has been widely used in regression shrinkage and selection. We extend its application to the regression model with autoregressive errors. Two types of lasso estimators are carefully studied. The first is similar to the traditional lasso estimator with only two tuning parameters (one for regression coefficients and the other for autoregression coefficients). These tuning parameters can be easily calculated…

回归模型选择：残差似然方法AI 解读

Summary We obtain the residual information criterion RIC, a selection criterion based on the residual log-likelihood, for regression models including classical regression models, Box–Cox transformation models, weighted regression models and regression models with autoregressive moving average errors. We show that RIC is a consistent criterion, and that simulation studies for each of the four models indicate that RIC provides better model order choices than the Akaike…

欠拟合回归与时间序列模型中修正 AIC 的偏差AI 解读

The Akaike Information Criterion, AIC (Akaike, 1973), and a bias-corrected version, AICC(Sugiura, 1978; Hurvich & Tsai, 1989) are two methods for selection of regression and autoregressive models. Both criteria may be viewed as estimators of the expected Kullback-Leibler information. The bias of AIC and AICC is studied in the underfitting case, where none of the candidate models includes the true model (Shibata, 1980, 1981; Parzen, 1978). Both normal linear regression and…

小样本自回归模型选择中 Kullback–Leibler 信息的改进估计AI 解读

A new estimator, AICl, of the Kuliback-Leibler information is proposed for Gaussian autoregressive time series model selection. The expected information is decomposed into two terms, the first being estimated without bias. The second term is a penalty function which depends only weakly on the model parameters, and hence can be tabulated by simulation from a white noise process. The estimated information, AICl, is very nearly unbiased when the model is either correct or…

小样本中的回归与时间序列模型选择AI 解读

A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better…

异方差一阶自回归模型的得分检验AI 解读

A score test is proposed for simultaneous testing of independence and homoscedasticity in the first-order autoregressive model with nonconstant variance. The relationships between the score test statistic and the local influence of minor perturbations on a statistical model are examined.

异方差一阶自回归模型的得分检验AI 解读

A score test is proposed for simultaneous testing of independence and homoscedasticity in the first-order autoregressive model with nonconstant variance. The relationships between the score test statistic and the local influence of minor perturbations on a statistical model are examined.