計(jì)量經(jīng)濟(jì)學(xué)（安徽財(cái)經(jīng)大學(xué)）智慧樹知到期末考試答案+章節(jié)答案2024年安徽財(cái)經(jīng)大學(xué)DW檢驗(yàn)有運(yùn)用的前提條件，只有符合這些條件DW檢驗(yàn)才是有效的。（）

答案:對(duì)模型中引入解釋變量的多個(gè)滯后項(xiàng)容易產(chǎn)生多重共線性。（）

答案:對(duì)變量變換法與加權(quán)最小二乘法實(shí)際是等價(jià)的。（）

答案:對(duì)只有滿足基本假設(shè)條件的計(jì)量經(jīng)濟(jì)模型的普通最小二乘參數(shù)估計(jì)量才具有無(wú)偏性和有效性。（）

答案:對(duì)單方程模型都是隨機(jī)方程。（）

答案:對(duì)擬合優(yōu)度R2檢驗(yàn)和F檢驗(yàn)是沒(méi)有區(qū)別的。（）

答案:錯(cuò)在擬合優(yōu)度檢驗(yàn)中擬合優(yōu)度高，則解釋變量對(duì)被解釋變量的解釋程度就高，可以推測(cè)模型總體線性關(guān)系成；反之亦然。（）

答案:錯(cuò)在簡(jiǎn)單線性回歸模型中對(duì)變量和模型的假定有（）

答案:X雖然是隨機(jī)的，但與隨機(jī)誤差項(xiàng)也是不相關(guān)；###模型對(duì)變量和函數(shù)形式的設(shè)定是正確的，即不存在設(shè)定誤差。###模型中的變量沒(méi)有測(cè)量誤差；###解釋變量X是確定的，非隨機(jī)的；下列說(shuō)法不正確的是（）

答案:多重共線性是完全可以避免的###只有完全多重共線性一種類型###多重共線性是總體現(xiàn)象兩變量X與Y間線性相關(guān)關(guān)系達(dá)到最高時(shí)，相關(guān)系數(shù)r可能等于()

答案:1###-1

答案:G—Q檢驗(yàn)法的應(yīng)用條件是（）

答案:除了異方差外，其他假定條件均滿足###隨機(jī)誤差項(xiàng)服從正態(tài)分布###將觀測(cè)值按解釋變量的大小順序排列###樣本容量盡可能大###將排列在中間的約1/4的觀測(cè)值刪除掉檢驗(yàn)自相關(guān)的方法有（）

答案:偏相關(guān)系數(shù)檢驗(yàn)法###圖形法###DW檢驗(yàn)法###BG檢驗(yàn)法如果模型中解釋變量之間存在共線性，則會(huì)引起如下后果（）

答案:參數(shù)的經(jīng)濟(jì)意義不正確###參數(shù)估計(jì)值不確定###參數(shù)估計(jì)值的方差趨于無(wú)限大應(yīng)用DW檢驗(yàn)方法時(shí)應(yīng)滿足該方法的假定條件，下列不是其假定條件的為（）

答案:被解釋變量為非隨機(jī)的在序列自相關(guān)的情況下，參數(shù)估計(jì)值仍是無(wú)偏的，其原因是（）

答案:零均值假定成立在多元線性回歸模型中，加入一個(gè)新的假定是（）

答案:無(wú)多重共線性F檢驗(yàn)屬于經(jīng)濟(jì)計(jì)量模型評(píng)價(jià)中的（）

答案:統(tǒng)計(jì)準(zhǔn)則對(duì)樣本的相關(guān)系數(shù)r，以下結(jié)論錯(cuò)誤的是（）

答案:︱r︱越接近0，X與Y之間線性相關(guān)程度高下列說(shuō)法正確的是()

答案:偏相關(guān)系數(shù)檢驗(yàn)可用于檢驗(yàn)?zāi)Ｐ褪欠翊嬖谝浑A自相關(guān)###拉格朗日乘數(shù)檢驗(yàn)可用于檢驗(yàn)?zāi)Ｐ褪欠翊嬖谝浑A自相關(guān)###DW檢驗(yàn)可用于檢驗(yàn)?zāi)Ｐ褪欠翊嬖谝浑A自相關(guān)要使得計(jì)量經(jīng)濟(jì)模型擬合的好，就必須增加解釋變量。（）

答案:錯(cuò)在實(shí)際中，一元回歸幾乎沒(méi)什么用，因?yàn)橐蜃兞康男袨椴豢赡苡梢粋€(gè)解釋變量來(lái)解釋。（）

答案:錯(cuò)如果模型對(duì)樣本有較高的擬合優(yōu)度，F(xiàn)檢驗(yàn)一般都能通過(guò)。（）

答案:對(duì)在經(jīng)濟(jì)計(jì)量分析中，模型參數(shù)一旦被估計(jì)出來(lái)，就可將估計(jì)模型直接運(yùn)用于實(shí)際的計(jì)量經(jīng)濟(jì)分析。（）

答案:錯(cuò)計(jì)量經(jīng)濟(jì)學(xué)是一門經(jīng)濟(jì)學(xué)科，不是數(shù)學(xué)或其他。（）

答案:對(duì)計(jì)量后經(jīng)濟(jì)學(xué)模型解釋經(jīng)濟(jì)活動(dòng)中各種因素之間的理論關(guān)系，用確定性的數(shù)學(xué)方程加以描述。（）

答案:錯(cuò)在多回歸分析中，F(xiàn)檢驗(yàn)顯著，不必進(jìn)行t檢驗(yàn)。（）

答案:錯(cuò)虛擬變量原則上只能取0和1。（）

答案:對(duì)在異方差性的情況下，若采用Eviews軟件中常用的OLS法，必定高估了估計(jì)量的標(biāo)準(zhǔn)誤差。（）

答案:錯(cuò)在對(duì)參數(shù)進(jìn)行最小二乘估計(jì)之前，沒(méi)有必要對(duì)模型提出古典假定。（）

答案:錯(cuò)計(jì)量經(jīng)濟(jì)模型的檢驗(yàn)一般包括內(nèi)容有（）

答案:統(tǒng)計(jì)推斷的檢驗(yàn)###經(jīng)濟(jì)意義的檢驗(yàn)###預(yù)測(cè)檢驗(yàn)###計(jì)量經(jīng)濟(jì)學(xué)的檢驗(yàn)可決系數(shù)的公式為（）

答案:ESS/TSS###1-RSS/TSS###ESS/(ESS+RSS)對(duì)于德賓——瓦森DW檢驗(yàn)，下列結(jié)論中正確的是()。

答案:模型不能含有滯后的因變量###當(dāng)DW＜dL時(shí)，認(rèn)為隨機(jī)誤差項(xiàng)存在一階正自相關(guān)###適用于一階自回歸形式的序列相關(guān)性檢驗(yàn)計(jì)量經(jīng)濟(jì)模型中存在多重共線性的主要原因是（）

答案:滯后變量的引入###樣本資料的限制###經(jīng)濟(jì)變量相關(guān)的共同趨勢(shì)對(duì)分布滯后模型直接采用普通最小二乘法估計(jì)參數(shù)時(shí)，會(huì)遇到的困難有（）

答案:滯后期長(zhǎng)而樣本小時(shí)缺乏足夠自由度###解釋變量間存在多重共線性問(wèn)題###難以預(yù)先確定最大滯后長(zhǎng)度###無(wú)法估計(jì)無(wú)限分布滯后模型參數(shù)在回歸模型中引入虛擬變量的作用有（）

答案:提高模型的精度###檢驗(yàn)屬性類型對(duì)被解釋變量的作用###反映質(zhì)的因素的影響###分離異常因素的影響常見的滯后結(jié)構(gòu)類型有（）

答案:遞減滯后結(jié)構(gòu)###不變滯后結(jié)構(gòu)###倒V型滯后結(jié)構(gòu)降低多重共線性的經(jīng)驗(yàn)方法有（）

答案:橫截面與時(shí)間序列數(shù)據(jù)并用###增大樣本容量###變量或模型變換###利用外部或先驗(yàn)信息關(guān)于虛擬變量，下列表述正確的有（）

答案:取值為1和0###是質(zhì)的因素的數(shù)量化###在有些情況下可代表數(shù)量因素###代表質(zhì)的因素虛擬變量取值為0和1分別代表某種屬性是否存在，其中（）

答案:1表示存在這種屬性###0表示不存在這種屬性在一元線性回歸模型中，樣本回歸方程可表示為（）

答案:下列各種數(shù)據(jù)中，以下不應(yīng)該作為經(jīng)濟(jì)計(jì)量分析所用數(shù)據(jù)的是（）

答案:計(jì)算機(jī)隨機(jī)生成的數(shù)據(jù)時(shí)間序列資料中，大多存在序列相關(guān)問(wèn)題，對(duì)于分布滯后模型，這種序列相關(guān)問(wèn)題就轉(zhuǎn)化為（）

答案:多重共線性問(wèn)題在簡(jiǎn)單線性回歸模型中，認(rèn)為具有一定概率分布的變量是（）

答案:內(nèi)生變量在下列引起序列自相關(guān)的原因中，不正確的是（）

答案:解釋變量之間的共線性回歸分析中，用來(lái)說(shuō)明擬合優(yōu)度的統(tǒng)計(jì)量為（）

答案:可決系數(shù)回歸分析的目的是（）

答案:根據(jù)解釋變量數(shù)值來(lái)估計(jì)或預(yù)測(cè)被解釋變量的總體均值；在回歸模型中，正確表達(dá)了隨機(jī)誤差項(xiàng)序列相關(guān)的是（）

答案:data:image/png;base64,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

答案:0狹義的模型設(shè)定誤差不包括（）

答案:模型中有關(guān)隨機(jī)誤差項(xiàng)的假設(shè)有誤時(shí)間序列平穩(wěn)時(shí)()。

答案:均值函數(shù)是常數(shù)###自協(xié)方差函數(shù)僅依賴于滯后期k,與起始終了期無(wú)關(guān)###方差函數(shù)是常數(shù)檢驗(yàn)序列平穩(wěn)性的方法有()。

答案:ADF檢驗(yàn)###DF檢驗(yàn)###散點(diǎn)圖###自相關(guān)函數(shù)檢驗(yàn)data:image/png;base64,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

答案:錯(cuò)產(chǎn)生虛假回歸的原因是()

答案:序列非平穩(wěn)下面可以做協(xié)整性檢驗(yàn)的有()。

答案:EG檢驗(yàn)###JJ檢驗(yàn)平穩(wěn)的時(shí)間序列數(shù)據(jù)從圖形上看表現(xiàn)為一條圍繞其均值上下波動(dòng)的曲線。()

答案:對(duì)對(duì)于平穩(wěn)的時(shí)間序列,下列說(shuō)法不正確的是()

答案:序列的自協(xié)方差是與時(shí)間間隔和時(shí)間均無(wú)關(guān)的常數(shù)協(xié)整性檢驗(yàn)可以避免偽回歸問(wèn)題的產(chǎn)生。()

答案:對(duì)單位根檢驗(yàn)包括()

答案:DF檢驗(yàn)和ADF檢驗(yàn)有關(guān)EG檢驗(yàn)的說(shuō)法正確的是()。

答案:拒絕回歸殘差為零原假設(shè)說(shuō)明被檢驗(yàn)變量之間存在協(xié)整關(guān)系回歸模型中,虛擬變量的引入數(shù)量,要根據(jù)定性變量的個(gè)數(shù)、每個(gè)定性變量的類型及有無(wú)截距項(xiàng)來(lái)確定。()

答案:對(duì)計(jì)量經(jīng)濟(jì)模型中,加入虛擬變量的途徑有那幾種()

答案:乘法型###加法和乘法的組合###加法類型若干年的某經(jīng)濟(jì)變量月度數(shù)據(jù),假定一年有2月、3月、10月表現(xiàn)出季節(jié)變動(dòng),則應(yīng)引入4個(gè)虛擬變量。()

答案:錯(cuò)關(guān)于虛擬變量,下列表述正確的有()

答案:代表質(zhì)的因素###可取值為1和0###在有些情況下可代表數(shù)量因素###是質(zhì)的因素的數(shù)量化對(duì)于含有截距項(xiàng)的計(jì)量經(jīng)濟(jì)模型,若想將含有m個(gè)互斥類型的定性因素引入到模型中,則應(yīng)該引入虛擬變量個(gè)數(shù)為()。

答案:引入虛擬變量的主要作用()

答案:模型結(jié)構(gòu)變化的檢驗(yàn)###將屬性因素引入到模型中###進(jìn)行分段線性回歸###提高模型的精度當(dāng)質(zhì)的因素引進(jìn)經(jīng)濟(jì)計(jì)量模型時(shí),需要使用()。

答案:虛擬變量虛擬變量只能代表質(zhì)的因素。()

答案:錯(cuò)data:image/png;base64,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

答案:完全的多重共線性虛擬變量()。

答案:主要來(lái)代表質(zhì)的因素,但在有些情況下可以用來(lái)代表數(shù)量因素AIC和SC是用于衡量回歸模型優(yōu)良性的兩種準(zhǔn)則。（）

答案:對(duì)在估計(jì)分布滯后模型中,互相關(guān)分析一般用于初步判斷滯后期長(zhǎng)度（）

答案:對(duì)對(duì)無(wú)限分布滯后模型直接采用普通最小二乘法估計(jì)參數(shù)時(shí),會(huì)遇到的最主要困難為()

答案:無(wú)法估計(jì)無(wú)限分布滯后模型參數(shù)無(wú)限分布滯后模型無(wú)法直接應(yīng)用最小二乘法（）

答案:對(duì)下列屬于一般滯后模型的是()

答案:data:image/png;base64,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阿爾蒙估計(jì)法具有()特點(diǎn)

答案:減少估計(jì)參數(shù)個(gè)數(shù)###緩減多重共線性###原理巧妙、簡(jiǎn)單###滯后期長(zhǎng)度和多項(xiàng)式次數(shù)易受主觀影響data:image/png;base64,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

答案:data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAXCAYAAADtNKTnAAAAg0lEQVQ4Ed2SCQoAIQhFvf+lHYz5oD9lEgpignBJnwuJbjiygaF/h4iI2u2cNPoOSLcLG3saBxCT0L/2k0J8stcrWIBkCZmPYech3IXZ7AuL5UdO8O9eD5BhvJU4aNoB/eiwEw6ubC7ShjBgTFBVy/wAQCJmuRNL9BcAk8sQn8T6PZAHxReqnPgd2McAAAAASUVORK5CYII=###data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA8AAAAXCAYAAADUUxW8AAAAcUlEQVQ4Ed2S2woAIQhE/f+fdvFhljHHCGIRNojM5ngj84tlF6z/BTYzj32ypGoGPs0abZWyAccJu+tfwgyxvQZJsBIqH4J8B3PWsPmeBlYelHj5PKVsleHtcQdD1J2luk6o/DMwZsHZ08BUqTvfHPwAWfT8PNw5GNsAAAAASUVORK5CYII=###data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAYCAYAAAAcYhYyAAAAfklEQVQ4Ed2SgQrAIAhE/f+fbtzgwszDNRqMAlFDn2dkbcOxDYx2OsTMGmzlpNX/gKyqwNrTOoTAM67eJ4X4Zh8r2ADJGrK7CPseQhXwtKgCeVfCBhapplg3QO6kmih+cldCBcorZZMSBfD35Tq+WMWvIVwlA2DY4zdRyg6EXETh8VXGDmZGAAAAAElFTkSuQmCC分布滯后模型中的滯后效應(yīng)產(chǎn)生的原因有()

答案:心理因素###制度因素###技術(shù)因素data:image/png;base64,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

答案:滯后期的適當(dāng)次多

答案:模型產(chǎn)生自相關(guān)性的主要原因有()

答案:經(jīng)濟(jì)活動(dòng)的滯后效應(yīng)###模型中遺漏了重要解釋變量###模型函數(shù)形式的設(shè)定誤差###經(jīng)濟(jì)系統(tǒng)的慣性data:image/png;base64,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

答案:對(duì)利用Durbin估計(jì)法得到的自相關(guān)系數(shù)是有偏估計(jì)。（）

答案:對(duì)如果模型存在自相關(guān)性,則模型參數(shù)的普通最小二乘估計(jì)量()

答案:無(wú)偏但非有效在下列引起自相關(guān)的原因中,不正確的是()

答案:解釋變量之間的共線性布羅斯-戈弗雷檢驗(yàn)通過(guò)建立殘差項(xiàng)關(guān)于所有解釋變量的輔助回歸模型來(lái)判斷原模型是否存在自相關(guān)性。（）

答案:錯(cuò)當(dāng)模型存在自相關(guān)性時(shí),有效的參數(shù)估計(jì)方法是()

答案:廣義差分法模型出現(xiàn)自相關(guān)性帶來(lái)的影響有()

答案:增大模型的預(yù)測(cè)誤差###t檢驗(yàn)可靠性降低在異方差情況下采用的普通最小二乘估計(jì)是無(wú)偏估計(jì)。（）

答案:對(duì)模型產(chǎn)生異方差性的主要原因有()

答案:隨機(jī)因素影響###模型函數(shù)形式的設(shè)定誤差###模型中遺漏了影響逐漸增大的因素常用的檢驗(yàn)異方差性的方法有()

答案:戈里瑟檢驗(yàn)###懷特檢驗(yàn)###戈德菲爾德-匡特檢驗(yàn)在White檢驗(yàn)中,n與輔助回歸模型的判定系數(shù)R2的乘積為6.27,給定顯著水平下的卡方臨界值為5.99,則所建立的模型不存在異方差性。（）

答案:錯(cuò)data:image/png;base64,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

答案:data:image/png;base64,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若模型具有異方差性,常用的估計(jì)方法是()

答案:加權(quán)最小二乘法若回歸模型中的隨機(jī)誤差項(xiàng)存在異方差,則參數(shù)的OLS估計(jì)量()

答案:無(wú)偏但非有效當(dāng)模型存在異方差性,仍然可以用t檢驗(yàn)來(lái)判斷解釋變量影響的顯著性。（）

答案:錯(cuò)關(guān)于異方差性的圖示檢驗(yàn)法,說(shuō)法正確的是()

答案:data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABMAAAAZCAYAAADTyxWqAAAAl0lEQVRIDc2RgQrAIAhE/f+fdji4OMN2CLJNiLT0eZb5oNkgy7+FmZnHqqw+rTLdE6QCtmDc45+wSlWobo8JEPY0OgfKDwCvPb9U9lSwAzhOMECQEHHHUvZevMcKvGBcGD7HCoL7BFMQ1SDB0CF2Vci58BcMgJO60zlAdz0Hyldqk7LXYEpVa8zxN5PPoBI6960PUOBR2AVzlO1nobL3cwAAAABJRU5ErkJggg==###圖示檢驗(yàn)法主要有相關(guān)圖、殘差分布圖###圖示檢驗(yàn)法只能粗略地判定模型是否存在異方差在異方差情況下,參數(shù)的OLS估計(jì)仍然具有無(wú)偏性的原因是()

答案:零均值假定成立下面屬于多重共線性導(dǎo)致的直接后果是()

答案:回歸系數(shù)參數(shù)估計(jì)的標(biāo)準(zhǔn)誤差變大###估計(jì)值的穩(wěn)定性降低###置信區(qū)間變寬###接受備擇假設(shè)犯錯(cuò)的概率增大若某個(gè)解釋變量的VIF(),則一般認(rèn)為這個(gè)解釋變量與其他解釋變量間存在多重共線性

答案:大于10即使存在嚴(yán)重的多重共線性,OLS估計(jì)量仍是無(wú)偏估計(jì)量。()

答案:對(duì)多重共線性問(wèn)題的實(shí)質(zhì)是樣本現(xiàn)象,因此可以通過(guò)增加樣本信息得到改善。()

答案:對(duì)多重共線性產(chǎn)生的原因主要有()

答案:在建模過(guò)程中由于解釋變量選擇不當(dāng),引起了變量之間的多重共線性的判定系數(shù)為1###經(jīng)濟(jì)變量之間往往存在著密切的關(guān)聯(lián)###經(jīng)濟(jì)變量之間往往存在同方向的變化趨勢(shì)###在模型中采用滯后變量也容易產(chǎn)生多重共線性逐步回歸就是先將解釋變量全部引入模型,再逐個(gè)檢驗(yàn)每個(gè)解釋變量的顯著性,并將不顯著的解釋變量予以剔除,直至模型中的解釋變量都顯著為止。（）

答案:錯(cuò)檢驗(yàn)多重共線性的方法有()

答案:輔助回歸模型檢驗(yàn)###逐步回歸法###方差膨脹因子法###簡(jiǎn)單相關(guān)系數(shù)法如果回歸模型中解釋變量之間存在完全的多重共線性,則最小二乘估計(jì)量的值為()

答案:不確定,方差無(wú)限大在下列產(chǎn)生多重共線性的原因中,不正確的是()

答案:解釋變量與隨機(jī)誤差項(xiàng)相關(guān)data:image/png;base64,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

答案:1.33倍在多元回歸分析中,F檢驗(yàn)是用來(lái)檢驗(yàn)()

答案:回歸模型的總體線性關(guān)系是否顯著常見的非線性回歸模型主要有()

答案:半對(duì)數(shù)模型###對(duì)數(shù)模型###多項(xiàng)式模型###倒數(shù)模型若建立計(jì)量經(jīng)濟(jì)模型的目的是用于預(yù)測(cè),則要求模型的遠(yuǎn)期擬合誤差較小。（）

答案:錯(cuò)data:image/png;base64,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