計(jì)量經(jīng)濟(jì)第四章ppt課件-免費(fèi)閱讀
【正文】 Computing pvalues for t Tests 計(jì)算 t檢驗(yàn)的 p值 ? Suppose at 40 degrees of freedom, a calculated t ratio is , the related 5% and 1% critical values are and , respectively. Should we reject or not to reject the null? 假設(shè)自由度為 40，算得 t 值為 ，對應(yīng) 5%和1%的臨界值分別為 和 。當(dāng) t的 絕對值 大于臨界值 c時(shí)，拒絕零假設(shè)。來估計(jì)分布，因?yàn)槲覀円米⒁膺@是一個(gè)假設(shè)下，有在221j221j ? ~ ??C L M1:f r e ed o m of d eg r ee s t h eN o t e?by es t i m at e t oh av e w eb ec a u s e n o r m al ) ( v son d i s t r i b u t i a is t h i sN o t e)( ~ ?? sas s u m p t i o n C L M U n d er t h essbbbssbbbttseknttseknjjknjj????????The t Test (cont) t檢驗(yàn) ? Knowing the sampling distribution for the standardized estimator allows us to carry out hypothesis tests 知道標(biāo)準(zhǔn)化估計(jì)量的樣本分布后，便可以進(jìn)行假設(shè)檢驗(yàn) ? Start with a null hypothesis 由零假設(shè)出發(fā) ? For example, H0: bj=0 () 例如， H0: bj=0 ? If accept null, then accept that xj has no partial effect on y, controlling for other x’s 如果接受零假設(shè)，則認(rèn)為控制 x其它分量后， xj對 y沒有邊際影響。 ? The significance level (level) of a test is the probability of a Type I error. 一個(gè)檢驗(yàn)的顯著性水平是發(fā)生第一類錯(cuò)誤的概率。 ? In order to do classical hypothesis testing, we need to add another assumption (beyond the GaussMarkov assumptions) 為了進(jìn)行經(jīng)典的假設(shè)檢驗(yàn)，我們要在 Gauss－ Markov假設(shè)之外增加另一假設(shè)。 ? Our goal: use the evidence in a randomly selected sample of data to decide whether to accept the null hypothesis. 我們的目的：利用一個(gè)隨機(jī)選取的樣本提供給我們的證據(jù)來決定是否應(yīng)當(dāng)接受零假設(shè)。 Background Review 背景知識回顧 ? A test statistic (T) is some function of the random sample. When we pute the statistic for a particular sample, we obtain an oute of the test statistic (t). 一個(gè)檢驗(yàn)統(tǒng)計(jì)量（ T）是關(guān)于隨機(jī)樣本的一個(gè)函數(shù)。 Example: Student Performance and School Size 例子：學(xué)生表現(xiàn)與學(xué)校規(guī)模 The Twosided Alternatives 雙邊替代假設(shè) ? H1: bj ? 0 is a twosided alternative. Under this alternative, we have not specified the sign of the partial effect of xj on y. H1: bj ? 0為雙邊替代假設(shè)。 Summary for H0: bj = 0 總結(jié) ? Unless otherwise stated, the alternative is assumed to be twosided 除非特別指出，我們總認(rèn)為替代假設(shè)是雙邊的 ? If we reject the null, we typically say “xj is statistically significant at the a % level” 如果拒絕了零假設(shè)，我們通常說 “ xj 在 a % 水平下顯著 ” ? If we fail to reject the null, we typically say “xj is statistically insignificant at the a % level” 如果不能拒絕零假設(shè)，我們通常說 “ xj 在 a % 水平下不顯著 ” Testing other hypotheses 其他假設(shè)檢驗(yàn) ? A more general form of the t statistic recognizes that we may want to test something like H0: bj = aj 如果我們想對形如 H0: bj = aj 的假設(shè)進(jìn)行檢驗(yàn)，需要更一般的 t統(tǒng)計(jì)量 ? In this case, the appropriate t statistic is 此時(shí)，恰當(dāng)?shù)? t 統(tǒng)計(jì)量是 ? ?? ?0 t e s ts t a n d a r d f o r t h e 0 w h e r e,??????jjjjjaaseat當(dāng)進(jìn)行標(biāo)
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