尔雅Basic Econometrics期末答案(学习通2023课后作业答案)

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Part 1: Basic Statistics: Chapter 1 Estimation: Confidence intervals

Chapter 1.1 Confidence interval随堂测验

1、尔雅What is 末答point estimation?
A、Point estimation is 案学only one observation.
B、point estimation is 习通one value of the sample statistics to estimate the population parameter.
C、point estimation a range to estimate the population parameters.
D、课后none of the abave is 作业correct.

Test 1

1、When estimating population parameters,答案 a point estimate is:
A、A range of possible values for a population parameter.
B、尔雅Always equal to a population value.
C、末答A statistic that estimates a population parameter.
D、案学The习通 population mean.

2、A characteristic of all confidence intervals is 课后that:
A、a confidence interval always includes the population parameter.
B、作业a confidence interval always increases in width as the sample size is 答案increased.
C、a confidence interval cannot include a value of 0.
D、尔雅a confidence interval always increases in width as the level of confidence increases.

3、If we wish to decrease the width (increase the precision) of a confidence interval, we would:
A、increase the size of the sample.
B、reduce the size of the population.
C、raise the level of confidence.
D、compute the mean.

4、We wish to develop a confidence interval for the population mean. The population standard deviation is known. We have a sample of 40 observations. We decide to use the 92 percent level of confidence. The appropriate value of z is:
A、1.75.
B、1.96.
C、1.65.
D、2.58.

5、We wish to develop a confidence interval for the population mean. The population follows the normal distribution, the standard deviation of the population is 3, and we have a sample of 10 observations. We decide to use the 90 percent level of confidence. The appropriate statistic for the level of confidence is:
A、z = 1.645.
B、z = 1.960.
C、t = 1.833.
D、t = 2.262.

6、The fraction of a sample possessing a certain trait is called a:
A、proportion.
B、population.
C、mean.
D、confidence interval.

7、To develop a confidence interval for a proportion:
A、we approximate the binomial distribution with a normal distribution.
B、the sample size should be at least 100.
C、p should be less than .05.
D、we use the t statistic.

8、We wish to develop a confidence interval for the population mean. The population follows the normal distribution and the population standard deviation is 3. We collect a random sample of 10 observations. We decide to use the 90 percent level of confidence. The margin of error for the confidence interval is:
A、± 1.565.
B、± 1.645.
C、± 2.262.
D、± 2.146.

9、We wish to develop a confidence interval for the population mean. The population follows the normal distribution. We collect a random sample of 10 observations and the sample standard deviation is 3. We decide to use the 95 percent level of confidence. The margin of error for the confidence interval is:
A、± 2.146.
B、± 1.859.
C、± 1.960.
D、± 2.262.

10、Which of the following statements is a characteristic of the t distribution?
A、It is a continuous distribution.
B、It is a discrete distribution.
C、It has a mean of 0.
D、It is symmetrical.

Part 1： Basic Statistics：Chapter 2 Hypothesis Testiing

Chapter 2.1 One Sample Tests of Hypothesis随堂测验

1、When we do the one sample of hypothesis test for the population mean but we don't know the population variation, what statistics we can choose to use?
A、T statistics
B、Normal statistics
C、F statistics
D、chi-square

Test 2

1、For a null hypothesis statement, it always includes:
A、a hypothesized value for the sample statistic.
B、a statement of equality: (=), (≤) or (≥).
C、a statement of less than: (<).
D、a statement of greater than: (>).

2、Which of the following statements is true about the alternate hypothesis?
A、It is accepted if the null hypothesis is rejected.
B、It will always contain an equal sign.
C、It is rejected if the null hypothesis is true.
D、It is accepted if the null hypothesis is accepted.

3、A Type I Error is:
A、calculated from sample information.
B、a probability determined from the test statistic.
C、the probability of rejecting the null hypothesis when it is true.
D、the probability of accepting the null hypothesis when it is false.

4、When testing a null hypothesis, a critical value is:
A、calculated from sample information.
B、always positive.
C、the point that divides the acceptance region from the rejection region.
D、a probability determined from the test statistic.

5、In a one-tailed hypothesis test:
A、the rejection region is in only one of the tails of a distribution.
B、the rejection region is split between the tails of a distribution.
C、the area of the rejection region equals the p-value.
D、the area of the rejection region is twice the value of the p-value.

6、To use a z statistic in a one-sample hypothesis test of a mean, we need to know:
A、the population mean.
B、the population standard deviation.
C、the degrees of freedom.
D、the margin of error.

7、A p-value is equal to the:
A、population proportion.
B、significance level.
C、fraction of the population that has a particular characteristic.
D、probability of finding a value of the test statistic that is equal or more extreme (greater than or less than) than the value of the test statistic assuming the null hypothesis is true.

8、A Type II Error occurs when we:
A、accept the null hypothesis when it is false.
B、reject the alternate hypothesis when it is true.
C、reject the null hypothesis when it is false.
D、accept the null hypothesis when it is true.

9、Which of the following statements is true about the level of significance?
A、It is the likelihood of rejecting the null hypothesis when it is true.
B、It is the likelihood of rejecting the null hypothesis when it is false.
C、It is a probability.
D、It can be any value between 0 and 1.

10、Which of the following statements are correct when deciding whether to use the z or the t distribution?
A、Use the z distribution in a test of proportions when nπ and n(1 - π) are greater than or equal to 5.
B、Use the z distribution when we have a normal population and we know the population standard deviation.
C、Use t when the population is normal and the population standard deviation is not known.
D、Use the t distribution in a test of proportions when nπ and n(1 - π) are greater than or equal to 5.

Part 2: Basic Econometrics: Chapter 1 What is Econometrics

Part2: Test 2.1

1、What are the shortcoming of Minimize Mean Absolute Error?
A、Mistakes don’t cancel out.
B、Implicitly treats cost of a mistake as being proportional to the mistake’s size.
C、Absolute values don’t go well with differentiation.
D、None of the above is right.

2、Economic theory: qualitative results while Econometrics: quantitative results

3、Statistics: “summarize the data faithfully” while Econometrics: “ what do we learn from economic theory AND the data at hand.”

Part 2: Basic Econometrics: Chapter 2 Monte Carlo Simulations

Chapter 2.2 Monte Carlo Simulations随堂测验

1、We choose all the rules for how our data are created. The underlying rules are the “Data Generating Process” (DGP).We choose to use the Gauss-Markov Assumptions. What are the followings are not necessery asumption?
A、linear model
B、x_i are fixed across samples
C、Var(e_i)=\sigma^2 homoscedasticity
D、Cov(e_i, e_j)=0

Chapter 2.2 Monte Carlo Simulations随堂测验

1、Monte Carlo simulation can compare the estimators by hand by can not tell us who is the best one of all the possible estimators.

Part2: Test 2

1、Which assumption of the sampling is matter to the simulation?
A、The high value of X is matter.
B、Normality.
C、The value of Beta is matter.
D、None of the above

2、Why we prefer the mean square error rather than absolute error and mean error in this simulation? which one is not right?
A、Mean square error shrink the smaller error and enlarge the bigger error.
B、Mean square error is easier to deal with by calculus.
C、Mean square error are nonnegtive
D、Mean error may equal to 0 but the data set still pretty spread out.

3、Why the OLS are better than the other estimators in this "Horse race"?
A、OLS has more weight.
B、OLS is BLUE estimator under Gauss-Markov Assumptions.
C、OLS are parametres
D、None of above is right

4、Monte Carlo simulation can show us who is BLUE estimator.

Part 2: Basic Econometrics: Chapter 3 Mathematical Tools for Econometrics

Chapter 2. 3 Mathematical Tools for Econometrics随堂测验

1、which statment is not true?
A、In a sample, we know exactly the mean, variance, covariance, etc. We can calculate the sample statistics directly.
B、We must infer the statistics for the underlying population.
C、Means in populations are also called expectations.
D、onThe descriptive statistics we use to describe data can not describe populations s

Part 2: Test 2. 3

1、How to let the statistics cheat you profesionally? There is a story. The average salary of the whole city seems not very low. but there are lots of poor people in the city. Why?
A、Median is sensitive to large value.
B、The mean is highly sensitive to a few large. The rich people in the city are 20% but hold 80% properties. so the average salary are drag up by the few rich people's value and the mojorities are poor. So median is lower then mean.
C、The distribution is skewed.
D、It is not normal distribution

2、which statement is right?
A、We can observe the entire population.
B、A sample drawn from the population of interest. is not adequate to discribe population.
C、In the Monte Carlo demonstration from last time, an individual dataset was the sample and the Data Generating Process described the population.
D、none of the above is right

3、Why Did We Do the Monte Carlos?
A、To emphasize estimators have sampling distributions.
B、To emphasize sampling properties (Mean Error, Mean Absolute Error, Mean Squared Error) are criteria for selecting estimators
C、To emphasize that estimators’ traits depend on where the data come from.
D、none of the above is right

4、Computer simulations let you see what happens in the worlds you design. For broader findings, we turn to mathematics.

Part 2: Basic Econometrics: Chapter 4 Regression with One Explanator

4.1 Regression with One Explanator随堂测验

1、What are the two Ways to Screw Up in Econometrics?
A、Your Data Generating Process assumptions missed a fundamental aspect of reality (your DGP is not a useful approximation)
B、Your estimator did a bad job for your DGP.
C、Your monte carlo simulation is wrong.
D、The data size is too small

4.1 Regression with One Explanator随堂测验

1、The analyst also tells you that shares in Chris Mining plc have no systematic risk, in other words that the returns on its shares are completely unrelated to movements in the market. The value of beta and its standard error are calculated to be 0.214 and 0.186, respectively. The model is estimated over thirty-eight quarterly observations. what is the null hypotheses?
A、The returns on its shares are completely unrelated to movements in the market.
B、the returns on its shares are completely unrelated to movements in the market.
C、The returns on its shares are related to movements in the market.
D、The model is estimated over thirty-eight quarterly observations.

Test 2.4

1、To have a low Mean Squared Error, what do we do?
A、Decrease the variance
B、? Mean Squared Error = Variance + Bia^2 ? To have a low Mean Squared Error, we want two things: a low bias and a low variance.
C、Decrease the bias
D、None of the above is right

2、Why Weight More Heavily Observations With High X ’s?
A、Under our Gauss–Markov DGP the disturbances are drawn the same for all values of X
B、To compare a high X choice and a low X choice, ask what effect a given disturbance will have for each
C、high x value get more weight for the estimators.
D、High value of x may release the influnce of the disturbance to the estimation

3、What are the Linear Estimators and Efficiency?
A、If we want an unbiased estimator, with a lower variance is called efficiency estimator.
B、Linear estimator is weighted sum of the Y_i.
C、lower biase
D、lower variance

4、According to the Gauss–Markov Theorem, OLS is the BLUE Estimator for the Gauss–Markov DGP

Part 2: Basic Econometrics: Chapter 6 Multiple Regression

Chapter 6.1 Multiple Regression随堂测验

1、In most instances, adding another regressor increases R-square.

Test 2.6

1、OLS is still BLUE under Gauss-Markov Assumptions for multiple regression.

2、In the linear multiple regression model, the interpretation of the paremeters B1 is the change in Y from a 1-unit change in X1 , holding X2…Xk FIXED

3、Even though the disturbance term in the CLRM is not normally distributed, the OLS estimators are still unbiased.

4、The coefficient of determination R-square (two-variable case) is a summary measure that tells how well the sample regression line fits the data.

5、The coefficient of correlation r is a measure of the degree of association between two variables. It must be positive and lies between the limits of 0 and 1.

Part 2: Basic Econometrics: Chapter 5 Interpreting Regression Results Logarithms

Test 2.5

1、The coefficient of determination means the sample regression line 99% fits the data.

2、Given the assumptions of the classical linear regression model, The OLS estimators are BLUE.

3、The coefficient of correlation r is a measure of the degree of association between two variables. It lies between the limits of -1 and 1.

4、In OLS Method, we choose the SRF(Sample Regression Function) in such a way that the sum of the residuals is as small as possible.

5、We can not use linear model to deal with the nonlinear relationship.

Final Exam

Basic Econometrics Final Exam

1、The alternative hypothesis makes a claim about a ________.
A、Population parameter
B、Sample statistic
C、Sample mean
D、Type II error

2、Which distribution is used to build a confidence interval of population mean when the standard error ? is known_______
A、F-statistic
B、t-statistic
C、Chi-square statistic
D、z-statistic

3、For small sample size and normal distribution of the sample means, the testing of a population mean by the sample mean when population standard deviation ? is unknown base on the _______.
A、t statistic
B、Z statistic
C、F statistic
D、Chi-square statistic

4、PRF (population Regression Function) is not directly observable, we estimate it from SRF (Sample Regression Function).

5、Given the assumptions of the classical linear regression model, The OLS estimators are BLUE.

6、The Gauss Markov theorem is based on the assumptions of CLRM (Classical Linear Regression Model) .

7、Assumption=0, implies .

8、, , are equivalent to .

9、In OLS Method, we choose the SRF in such a way that the sum of the residuals is as small as possible.

10、Since the correlation between two variables, and X, can range from ?1 to +1, this also means that Cov（Y,X） also lies between these limits.

11、The coefficient of correlation r is a measure of the degree of association between two variables. It must be positive and lies between the limits of 0 and 1.

12、The coefficient of determination (two-variable case) is a summary measure that tells how well the sample regression line fits the data. Its limits are .

13、Even though the disturbance term in the CLRM is not normally distributed, the OLS estimators are still unbiased.

14、Linear estimator is a weighted sum of the Y’s

15、An linear estimator is unbiased if

学习通Basic Econometrics

Basic Econometrics是一门经济学中的基础课程，它主要介绍了经济数据的分析方法和模型，是经济学领域中非常重要的一门课程。本文将介绍学习通Basic Econometrics的相关内容。

学习内容

学习通Basic Econometrics的课程内容主要包括：

1. 经济数据的基本概念
2. 经济数据的描述性统计分析
3. 单变量回归分析
4. 多元线性回归分析
5. 非线性回归分析
6. 时间序列分析
7. 面板数据分析
8. 计量经济学的基本假设检验

学习方法

学习通Basic Econometrics的学习方法主要包括：

1. 认真听课：学习通Basic Econometrics的课程视频非常详细，所以要认真听课才能掌握每个知识点。
2. 做笔记：可以在学习过程中做笔记，以便后续查阅。
3. 做题：学习通Basic Econometrics的课程中有很多练习题和作业，可以通过做题来巩固知识。
4. 参加讨论：学习通Basic Econometrics的课程中有讨论区，可以在讨论区中与其他学生交流，互相学习，解决疑惑。
5. 重复学习：如果有不理解的地方，可以重复学习相关的视频和资料。

学习成果

通过学习通Basic Econometrics，可以掌握经济数据的分析方法和模型，具备以下能力：

1. 能够对经济数据进行描述性统计分析。
2. 能够应用单变量回归分析和多元线性回归分析来探究经济现象。
3. 能够应用非线性回归分析来解决经济问题。
4. 能够应用时间序列分析来分析经济趋势。
5. 能够应用面板数据分析来研究经济问题。
6. 能够进行计量经济学的基本假设检验。

学习体验

学习通Basic Econometrics的学习体验非常好，它的优点包括：

1. 视频质量高：学习通Basic Econometrics的视频质量非常高，清晰度较高，看起来非常舒适。
2. 知识点详细：学习通Basic Econometrics的知识点讲解非常详细，可以全面了解相关知识。
3. 练习题丰富：学习通Basic Econometrics的练习题和作业非常丰富，可以巩固知识点。
4. 交流互动性好：学习通Basic Econometrics的讨论区互动性很好，可以与其他学生交流，解决疑惑。

总结

学习通Basic Econometrics是一门非常重要的经济学基础课程，通过学习此课程，可以掌握经济数据的分析方法和模型，具备相关能力和技能。