《計量經(jīng)濟第四章》ppt課件-全文預(yù)覽
【正文】 不顯著 ” Testing other hypotheses 其他假設(shè)檢驗 ? 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)進行檢驗，需要更一般的 t統(tǒng)計量 ? In this case, the appropriate t statistic is 此時，恰當?shù)? t 統(tǒng)計量是 ? ?? ?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當進行標準檢驗時bbExample: Campus Crime and Enrollment 例子：校園犯罪與錄取 ? Question: Will 1% increase in enrollment increase campus crime by more than 1%? 問題：錄取量提高 1%是否會導(dǎo)致校園犯罪增加超過 1%? ? Suppose total number of crimes is determined by 假設(shè)犯罪總數(shù)由下式?jīng)Q定 crime=exp(b0 )enrollb1exp(u). ? One can estimate 可以估計 log(crime)=b0 + b1 log(enroll)+u Example: Campus Crime and Enrollment 例子：校園犯罪與錄取 ? And test H0: b1 = 1 H1: b1 1. ? Using data from the FBI’s uniform Crime reports (97 observations), the estimated equation is ? 利用 FBI犯罪報告（ 97個觀察值）的數(shù)據(jù)，估計得到方程 ^log(crime)=+(enroll) () () () The correct t ratio=()/=. The 1% onesided critical value for a t distribution with 95 degrees of freedom is . Therefore reject the null. t值 =()/=。當 a?， c是 nk1自由度的 t分布的 。 Example: Student Performance and School Size 例子：學生表現(xiàn)與學校規(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)。 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)。 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). 一個檢驗統(tǒng)計量（ T）是關(guān)于隨機樣本的一個函數(shù)。 ? 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： ,。 ? 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：利用一個隨機選取的樣本提供給我們的證據(jù)來決定是否應(yīng)當接受零假設(shè)。 ? Assumption (Normality): Assume that u is independent of x1, x2,…, xk and u is normally distributed with zero mean and variance s2: u ~ Normal(0,s2) 假設(shè) （正態(tài)）：假設(shè) u與 x1, x2,…, xk獨立，且 u服從均值為 0，方差為 s2的正態(tài)分布。 ? In order to do classical hypothesis testing, we need to add another assumption (beyond the GaussMarkov assumptions) 為了進行經(jīng)典的假設(shè)檢驗，我們要在 Gauss－ Markov假設(shè)之外增加另一假設(shè)。 Theorem Normal Sampling Distributions 定理 正態(tài)樣本分布 ? ?? ?? ?? ?? ??
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