Blame for fineval/json/econometrics/val.json

[
 {
  "id": "econometrics_0",
  "question": "用一组有30个观测值的样本估计模型$\\hat{Y_{i}}=\\beta_{0}+\\beta_{1}X_{1i}+\\beta_{2}X_{2i}+\\mu_{i}$后，在显著性水平0.05下对方程的显著性作检验，此检验的备择假设是____。",
  "A": "$\\beta_{0}=0$",
  "B": "$\\beta_{1}=\\beta_{2}=0$",
  "C": "$\\beta_{1}=\\beta_{2}\\ne0$",
  "D": "$\\beta_{1}$和$\\beta_{2}$不全为0",
  "answer": "D"
 },
 {
  "id": "econometrics_1",
  "question": "G-Q检验可以用于检验____的异方差。",
  "A": "单调递增或单调递减",
  "B": "单调递减或复杂型",
  "C": "单调递增或复杂型",
  "D": "各种类型",
  "answer": "A"
 },
 {
  "id": "econometrics_2",
  "question": "如果方差膨胀因子$VIF=100$，那认为什么问题是最严重的____。",
  "A": "异方差问题",
  "B": "序列相关问题",
  "C": "多重共线性问题",
  "D": "解释变量与随机项的相关性",
  "answer": "C"
 },
 {
  "id": "econometrics_3",
  "question": "在多元线性回归模型中，若仅存在多重共线性，则____。",
  "A": "在完全共线性下，可以得到参数的估计量",
  "B": "在近似共线性下，参数估计量的方差最小",
  "C": "在近似共线性下，参数估计量的方差增大",
  "D": "需要发展新的方法估计该模型，这包括普通最小二乘法、差分法等",
  "answer": "D"
 },
 {
  "id": "econometrics_4",
  "question": "下列回归模型中可用DW统计量来检验的是____。（模型中的$\\varepsilon_{t}$是具有零均值、常数方差，且不存在序列相关的随机变量）",
  "A": "$Y_{t}=\\beta_{0}+\\beta_{1}X_{t}+\\mu_{t}$，其中$\\mu_{t}=\\varrho\\mu_{t-1}+\\varepsilon_{t}$，$X_{t}$是非随机变量",
  "B": "$Y_{t}=\\beta_{1}X_{t}+\\mu_{t}$，其中$\\mu_{t}=\\varrho\\mu_{t-1}+\\varepsilon_{t}$，$X_{t}$是非随机变量",
  "C": "$Y_{t}=\\beta_{0}+\\beta_{1}X_{t}+\\mu_{t}$，其中$\\mu_{t}=\\varrho_{1}\\mu_{t-1}+\\varrho_{2}\\mu_{t-2}+\\varepsilon_{t}$，$X_{t}$是非随机变量",
  "D": "$Y_{t}=\\beta_{0}+\\beta_{1}X_{t}+\\mu_{t}$，其中$\\mu_{t}=\\varrho\\mu_{t-1}+\\varepsilon_{t}$，$X_{t}$是随机变量",
  "answer": "A"
 },
 {
  "id": "econometrics_5",
  "question": "下列各项中，属于有限分布滞后模型的修正估计方法的有____。",
  "A": "经验加权法",
  "B": "工具变量法",
  "C": "科伊克方法",
  "D": "普通最小二乘法",
  "answer": "A"
 },
 {
  "id": "econometrics_6",
  "question": "模型设定偏误的类型不包括____。",
  "A": "多选无关变量",
  "B": "模型函数被误设",
  "C": "遗漏相关的解释变量",
  "D": "设定的模型存在异方差",
  "answer": "D"
 },
 {
  "id": "econometrics_7",
  "question": "下列各项中，不属于约化模型的准则的有____。",
  "A": "模型具有包容性",
  "B": "模型具有恒定的参数",
  "C": "解释变量必须是强外生的",
  "D": "模型必须与经济理论相一致",
  "answer": "C"
 },
 {
  "id": "econometrics_8",
  "question": "下列关于二元Probit离散选择模型的表述，错误的是____。",
  "A": "Probit模型的随机误差项$\\mu_{i}^{*}$服从正态分布",
  "B": "Probit模型的随机误差项$\\mu_{i}^{*}$服从$F$分布",
  "C": "重复观测值不可得情形下的模型可以采用完全信息最大似然法中所采用的迭代方法估计参数",
  "D": "重复观测值可得情形下的模型可以采用广义最小二乘估计",
  "answer": "B"
 },
 {
  "id": "econometrics_9",
  "question": "Logit模型是假定二元选择模型$y_{i}^{*}=X_{i}B+\\mu_{i}^{*}$中的随机误差项$\\mu_{i}^{*}$的密度函数为____。",
  "A": "$f(t)=\\frac{e^{-t}}{(1+e^{-t})^{2}}$",
  "B": "$f(t)=\\frac{1}{(1+e^{-t})}$",
  "C": "$f(t)=\\frac{e^{-t}}{(1-e^{-t})^{2}}$",
  "D": "$f(t)=\\frac{1}{(1-e^{-t})}$",
  "answer": "A"
 },
 {
  "id": "econometrics_10",
  "question": "关于Probit模型和Logit模型的参数估计，下列表述错误的是____。",
  "A": "在重复观测值不可得的情形下，Logit模型可采用完全信息最大似然法中所采用的迭代方法",
  "B": "在重复观测值可得的情形下，两模型可采用广义最小二乘法",
  "C": "在重复观测值不可得的情形下，两模型可采用完全信息最大似然法",
  "D": "在重复观测值不可得和可得两种情形下，两模型都要采用完全信息最大似然估计的迭代法",
  "answer": "D"
 },
 {
  "id": "econometrics_11",
  "question": "为检验平行数据模型$y_{it}=\\alpha_{i}+X_{it}\\beta_{i}+\\mu_{it}$行的参数在横截面上有无变化，可进性的检验假设为____。",
  "A": "斜率在不同的横截面样本点上和时间上都是相同的，但截距不同",
  "B": "截距斜率在不同的横截面样本点上和时间上都是相同的",
  "C": "检验$\\alpha$和$\\beta$，先进行$\\alpha$的检验，再进行$\\beta$的检验",
  "D": "检验$\\alpha$和$\\beta$，先进行$\\beta$的检验，再进行$\\alpha$的检验",
  "answer": "D"
 },
 {
  "id": "econometrics_12",
  "question": "生产函数的要素替代弹性表示的是____。",
  "A": "两种要素的边际替代率的变化率与比例的变化率之比",
  "B": "维持产出不变，减少一单位的某一要素投入，需增加另一要素投入数量",
  "C": "要素替代弹性等于$\\frac{\\mathrm{d}\\ln(\\frac{K}{L})}{\\mathrm{d}\\ln(\\frac{MP_{L}}{MP_{K}})}$",
  "D": "要素的替代弹性就是要素的边际替代率",
  "answer": "C"
 },
 {
  "id": "econometrics_13",
  "question": "在产量一定的情况下，某一种要素的增加与另一种要素的减少之间的比例称为____。",
  "A": "要素的替代弹性",
  "B": "要素的边际替代率",
  "C": "要素的产出弹性",
  "D": "技术进步率",
  "answer": "B"
 },
 {
  "id": "econometrics_14",
  "question": "下列生产函数中，要素替代弹性为1的是____。",
  "A": "C-D生产函数",
  "B": "投入产出生产函数",
  "C": "VES生产函数",
  "D": "线性生产函数",
  "answer": "A"
 },
 {
  "id": "econometrics_15",
  "question": "多元线性回归模型为：$Y=\\beta_{0}+\\beta_{1}X_{1}+\\beta_{2}X_{2}+\\cdots+\\beta_{k}X_{k}+\\mu$。其中，$k$为解释变量的数目，$\\beta_{j}(j=0,1,2,\\cdots,k)$为回归系数，$\\mu$为随机误差项。设$\\hat{Y}$为条件期望$E(Y\\mid X)$的估计量，$\\hat{\\beta_{j}}$为$\\beta_{j}(j=0,1,2,\\cdots,k)$的估计量。该模型对应的样本回归函数为____。",
  "A": "$Y=\\hat{\\beta_{0}}+\\hat{\\beta_{1}}X_{1}+\\hat{\\beta_{2}}X_{2}+\\cdots+\\hat{\\beta_{k}}X_{k}$",
  "B": "$Y=\\hat{\\beta_{0}}+\\hat{\\beta_{1}}X_{1}+\\hat{\\beta_{2}}X_{2}+\\cdots+\\hat{\\beta_{k}}X_{k}+\\mu$",
  "C": "$\\hat{Y} =\\hat{\\beta_{0}}+\\hat{\\beta_{1}}X_{1}+\\hat{\\beta_{2}}X_{2}+\\cdots+\\hat{\\beta_{k}}X_{k}+e$（其中$e$为残差项）",
  "D": "$\\hat{Y} =\\hat{\\beta_{0}}+\\hat{\\beta_{1}}X_{1}+\\hat{\\beta_{2}}X_{2}+\\cdots+\\hat{\\beta_{k}}X_{k}$",
  "answer": "D"
 },
 {
  "id": "econometrics_16",
  "question": "在含有$G$个内生变量的完备联立方程组中，当识别的阶条件为$M-N_{i}<G-1$（$M$为联立方程组中内生变量和前定变量的总数，$N_{i}$为第$i个方程中内生变量和前定变量的总数）时，则表示____。",
  "A": "第$i$个方程恰好识别",
  "B": "第$i$个方程不可识别",
  "C": "第$i$个方程过度识别",
  "D": "第$i$个方程具有唯一统计形式",
  "answer": "B"
 },
 {
  "id": "econometrics_17",
  "question": "在DW检验中，不能判定的区域是____。",
  "A": "$0<d<d_{l},4-d_{l}<d<4$",
  "B": "$d_{u}<d<4-d_{u}$",
  "C": "$d_{l}<d<d_{u},4-d_{u}<d<d_{l}$",
  "D": "以上都不对",
  "answer": "D"
 }
]