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serial correlation相关的网络例句

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与 serial correlation 相关的网络例句 [注:此内容来源于网络,仅供参考]

This research evaluates the effects of serial correlation, heteroscedasticity, and state dependence on disaggregate choice models.

本研究对序列相关、异质性、与状态相依对个体选择模式之影响深入讨论。

However, in some situations, models are hard to satisfy these assumptions at the same time. We meet a number of random errors with serial correlation and heteroscedasticity in practical problems. The rationality of these assumptions for random errors is doubtable. Therefore, it is necessary to test for correlation and heteroscedasticity in regression, which is an important step of dealing with regression problems and plays an important role in theory and practice.

但实际问题中,回归模型很难同时满足Gauss-Markov的三个假设,人们在实际问题中发现许多随机误差出现序列相关和方差不同的现象,因此对回归模型的随机误差的独立性和方差齐性的假设产生了质疑,从而展开了回归模型的相关性和异方差检验的研究,它是处理回归问题的重要步骤,在理论和应用上都有十分重要的意义。

Mr Smithers shows that this conclusion is empirically false: stock markets exhibit "negative serial correlation".

史密瑟斯提出,从经验来看,这一结论是错误的:股市呈现的是"负序列相关"。

In the empirical study, we find that conventional tests over reject the null when the event forecasts and event realization of all countries have high serial correlation, which means the event forecasts have the timing ability. According to the evidence of sample correlation coefficient, however, it seems that the event forecasts have no timing ability by using these the HAC robust tests.

尔后,利用实际的资料,考虑六个国家股票市场每月之报酬率发现,所有国家在事件预测值与事件实现值存在高度的序列相关下,传统检定方法出现了过度拒绝虚无假设的结果,认为事件预测值具有预测能力;而考虑序列相关问题的检定方法则认为事件预测值不具有预测能力。

So we can use the way of serial Correlation Test.The conclusion is that Chinese Futures Market is not efficient.

通过单位根检验,发现Xt的分布是平稳的,在此前提下,通过序列相关检验得到的结论是我国的期货市场不是有效的。

Hence, in this paper, we particularly investigate serial correlation test in these models, and obtain some useful results.

因此,本文在方差齐性的假设下,详细研究了这些模型的序列相关检验问题,得出了一些有益的结果。

In this paper, ARCH effects and serial correlation of stock returns are tested.

本文检验了我国股市的 ARCH效应和序列相关性。

When r is positive and large, first differencing can eliminate most of the serial correlation.

当r 为正而且很大时,一阶差分可以消除大部分的序列相关性。

Therefore, it is necessary to test the presence of serial correlation and heteroscedasticity before statistic inference.

因此,在统计推断之前,检验模型是否存在序列相关和异方差很有必要。

When OLS and FGLS produce similar estimates, and there is evidence of serial correlation, FGLS is preferred since it uses the specific information about the serial correlation, hence is more efficient.

当OLS和FGLS产生相似的结果,而且存在序列相关性的证据时,我们偏好FGLS,因为它利用了序列相关这一信息从而是更加有效的。

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这两个团体间的分歧难以掩饰。

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这种做法不仅鼓励了更多的反应,而且减少跟风的可能性。

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