計(jì)量經(jīng)濟(jì)第四章ppt課件-wenkub
2023-05-18 07:37:11 本頁(yè)面
【正文】 0對(duì) H1: bj 0,當(dāng) tbj c時(shí)我們拒絕 H0，當(dāng) tbj =c ，則不能拒絕 H0 yi = b0 + b1xi1 + … + bkxik + ui H0: bj = 0 H1: bj 0 c 0 a ?1 ? a? OneSided Alternatives (cont) 單邊替代假設(shè) Fail to reject reject Example: Student Performance and School Size 例子：學(xué)生表現(xiàn)與學(xué)校規(guī)模 ? Question: Does larger class size results in poorer student performance? 問(wèn)題：是不是較大的班級(jí)意味著較差的學(xué)生表現(xiàn)？ ? Use 408 high schools in Michigan for year 1993, perform the following regression: 應(yīng)用 1993年 408個(gè)密歇根州中學(xué)的數(shù)據(jù)，進(jìn)行如下回歸 ^math10=++ () () () –.0002enroll () math10: percentage of students passing the MEAP standardized grade 10 math test 通過(guò) MEAP標(biāo)準(zhǔn)化 10年級(jí)數(shù)學(xué)測(cè)驗(yàn)的學(xué)生百分比 totp: average annual teacher’s pensation 平均教師年度補(bǔ)償 staff : the number of staff per one thousand students 每千個(gè)學(xué)生對(duì)應(yīng)的工作人員數(shù)目 enroll : student enrollment 學(xué)生錄取 ? Decide the testing hypotheses: 確定被檢驗(yàn)的假設(shè) ? H0 :βenroll=0 versus H1 :βenroll0 ? Compute the t statistic, 計(jì)算 t統(tǒng)計(jì)量 t=? Since nk1=404, we use the standard normal critical value. At the 5% level, the critical value is –. 由于 nk1=404，我們使用標(biāo)準(zhǔn)正態(tài)的臨界值。當(dāng)我們用某一特定樣本計(jì)算此統(tǒng)計(jì)量時(shí)，我們得到這個(gè)檢驗(yàn)統(tǒng)計(jì)量的一個(gè)實(shí)現(xiàn)（ t）。如果為 5％的檢驗(yàn)中錯(cuò)誤地拒絕零假設(shè)。 Background Review 背景知識(shí)回顧 ? Two kinds of mistakes are possible in hypothesis testing. 在假設(shè)檢驗(yàn)中存在兩種可能的錯(cuò)誤。 CLM Assumptions 經(jīng)典線性模型假設(shè) ? Assumptions – are called the classical linear model (CLM) assumptions. 假設(shè) ? We refer to the model under these six assumptions as the classical linear model. 我們將滿足這六個(gè)假設(shè)的模型稱為經(jīng)典線性模型 ? Under CLM, OLS is not only BLUE, but also the minimum variance unbiased estimator, that is, among linear and nonlinear estimators, OLS estimator gives the smallest variance. ? 在經(jīng)典線性模型假設(shè)下， OLS不僅是 BLUE，而且是 最小方差無(wú)偏估計(jì)量 ，即在所有線性和非線性的估計(jì)量中， OLS估計(jì)量具有最小的方差。Multiple Regression Analysis: Inference 多元回歸分析：推斷 (1) y = b0 + b1x1 + b2x2 + . . . bkxk + u Lecture Outline 本課提綱 ? CLM assumptions and Sampling Distributions of the OLS Estimators 經(jīng)典假設(shè)與 OLS估計(jì)量的樣本分布 ? Background review of hypothesis testing 假設(shè)檢驗(yàn)的背景知識(shí) ? Onesided and twosided t tests 單邊與雙邊 t檢驗(yàn) ? Calculating the p values 計(jì)算 p值 Assumption (Normality) 假設(shè) （正態(tài)） ? So far, we know that given the GaussMarkov assumptions, OLS is BLUE, 我們已經(jīng)知道當(dāng) Gauss－ Markov假設(shè)成立時(shí)， OLS是最優(yōu)線性無(wú)偏估計(jì)。 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？ ?
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