| 1. | Approximate power of heteroscedasticity test in nonlinear models with arima errors
误差的非线性回归异方差检验的渐近功效 |
| 2. | Using different methods to cope with heteroscedasticity may result in different models
对异方差不同的处理方法，可能得出不同的模型。 |
| 3. | In the third part , the article mainly gives the methods to deal with the heteroscedasticity in different conditions
本文在第三节中，主要讨论异方差的处理方法。 |
| 4. | Thus , it is of great significance to study the hypothesis testing of heteroscedasticity and statistical inference for the regression models with heteroscedasticity
因此，研究异方差的检验方法及存在异方差时的处理方法具有重要意义。 |
| 5. | Although these models can eliminate the heteroscedasticity , it is still necessary to make a further study to decide which model is much better and more effective
这些模型虽然都能消除异方差，但需要进一步研究哪种模型比较适合，哪个模型更有效。 |
| 6. | However in most economic phenomena , this kind of hypothesis is not necessary true . sometimes the disturbances vary with the observations . this is called heteroscedasticity
但在大多数经济现象中，这种假设不一定成立，有时扰动项u _ i的方差随观察值的不同而变化，这就是异方差性。 |
| 7. | The concept of arch , which stands for autoregressive heteroscedasticity , was first introduced by engle ( 1982 ) to handle time series with a changing conditional variance
具有自回归条件异方差( arch )的时间序列模型，首先是由engle ( 1982 )提出，这类模型在金融和经济领域有着广泛的应用。 |
| 8. | Thus , it makes the hypothesis testing unreliable . so , if heteroscedasticity is found exist through hypothesis testing , it should be dealt with in a suitable way
因此，如果怀疑存在异方差或者已经检测到了异方差的存在，就要想办法克服它，使估计量具有较小的方差，使回归模型有较强的实用性。 |
| 9. | In the last decade , there exist two active lines on the investigation of nonlinear time series . one is the autoregressive conditional heteroscedasticity ( arch ) model , the another is the nonstationary ( unit root ) time series model
对非线性时间序列的研究，近几十年来，有两条研究路线非常活跃，其一是自回归条件异方差( arch )模型，其二是非平稳(单位根)时间序列模型。 |
| 10. | In the third section three different forms of heteroscedasticity are used in the random simulation and then park test , glejser test and goldfeld - quandt test are compared although the existence of heteroscedasticity does not destroy the unbiasedness of the ols estimators , the variances become larger
异方差的存在虽然并不破坏普通最小二乘估计量的无偏性，但是估计量的方差变大了。由于估计量方差的变大，就使通常假设检验的值不可靠。 |
