計(jì)量經(jīng)濟(jì)第四章ppt課件-文庫(kù)吧資料
2025-05-09 07:37本頁(yè)面
【正文】 : 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)。在5％顯著水平下，臨界值位－ ? Because –, we fail to reject the null. 由于 ，我們不能拒絕零假設(shè) Example: Student Performance and School Size 例子：學(xué)生表現(xiàn)與學(xué)校規(guī)模 ? If we are also interested in asking whether betterpaid teachers leads to better student performance, we can test 如果我們同樣感興趣是否高收入的教師會(huì)使學(xué)生表現(xiàn)更好，我們可以檢驗(yàn)： ? H0 :βtotp=0 versus H1 :βtotp0 ? The calculated t statistic equals . Since , therefore rejecting the H0 at 1% level. 計(jì)算得到的 t統(tǒng)計(jì)量為 。 Useful information about pvalues 一些關(guān)于 p值的信息 ? Because it is a probability, its range is between 0 and 1. 由于這是一個(gè)概率，其取值范圍在 0， 1之間 ? Small p values are evidence against the null, large p values provide little evidence against the null. 小 p值提供了拒絕零假設(shè)的證據(jù)，大 p值不能提供證據(jù)拒絕零假設(shè)。 Theorem t Distribution for the Standardized Estimators 定理 : 標(biāo)準(zhǔn)化估計(jì)量的 t分布 ? ?? ?? ?? ?。 Background Review 背景知識(shí)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)。 Background Review 背景知識(shí)回顧 ? The critical value of the test statistic is the value of the statistic for which the test just reject the null hypothesis at the given significance level. 檢驗(yàn)統(tǒng)計(jì)量的 臨界值 是使得零假設(shè)剛好在給定顯著性水平上被拒絕的統(tǒng)計(jì)量的值。 ? Commonly specified significance levels: , . If it equals , it means the researcher is willing to falsely reject the null at 5% of the time. 通常設(shè)定的限制性水平為： ,。 ? Type I error: reject the null hypothesis when it is in fact true. 第一類錯(cuò)誤：當(dāng)零假設(shè)為真時(shí)拒絕零假設(shè)（棄真） ? Type II error: fail to reject the null when it is actually false. 第二類錯(cuò)誤：當(dāng)零假設(shè)為假時(shí)未拒絕零假設(shè)（取偽） Background Review 背景知識(shí)回顧 ? Hypothesis testing rules are constructed to make the probability of mitting type I error fairly small. 我們建立一些假設(shè)檢驗(yàn)的規(guī)則使發(fā)生第一類錯(cuò)誤的概率非常小。 ? 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(lái)決定是否應(yīng)當(dāng)接受零假設(shè)。 CLM Assumptions 經(jīng)典線性模型假設(shè) ? We can summarize the population assumptions of CLM as follows 我們對(duì)總體的經(jīng)典線性模型假設(shè)做個(gè)總結(jié) ? y|x ~ Normal(b0 + b1x1 +…+ bkxk, ,s2) ? While for now we just assume normality, sometimes this is not the case 盡管現(xiàn)在我們假設(shè)了正態(tài)，但有時(shí)候并不是這種情況 CLM Assumptions 經(jīng)典線性模型假設(shè) ? What should we do when the normality assumption fails? 如果正態(tài)假設(shè)不成立怎么辦？ ? Using a transformation, especially taking the log, often yields a distribution that is closer to normal. 通過(guò)變換，特別是通過(guò)取自然對(duì)數(shù)，往往可以得到接近于正態(tài)的分布。 ? Assumption (Normality): Assume that u is independent of x1, x2,…, xk and u
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