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# 计量经济学考试代写 Econometrics考试代写 Econometrics代写

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## EXAM RULES

1. Exam takes 90 min.

2. This exam is a closed book exam.

3. Everybody is required to sign on the list.

4. The solution of exercise should be written on the sheet on which the exercise was printed or on the additional sheets on the back of the exam.

5. The minimum to obtain the pass grade is to get 50% from theoretical questions and 50% from exercises.

Each question (both theoretical and exercise points) was graded with 1 point. So, in order to pass theoretical part, one needed to get at least 2 points, and to pass exercises – at least 6 points.

### Theoretical questions

1. Derive OLS estimator for model with multiple explanatory variables.

2. Proof, that in CLRM estimator b is unbiased.

3. Why do we decode discrete variables into dummy variables?

4. Why do we should remove not significant variables from the model?

### Exercise 1.计量经济学考试代写

Consider the following multiple-regression model

𝑦𝑖 = 𝛽1 + 𝛽2𝑥2𝑖 + 𝛽3𝑥3𝑖 + 𝜀𝜀𝑖

where 𝑥3𝑖 = −(2𝑥2𝑖 + 1)

1. Using the method of Ordinary Least Squares, is it possible to obtain estimates of all of the unknown parameters 1, 2, 3? Why or why not?

2. If not, what linear functions of these unknown parameters may be obtained? Explain

### Exercise 2. 计量经济学考试代写

Researcher estimated regression for laptop prices (price_euros), using information about laptop type (type), screen resolution (inches), RAM amount (ram) and weight (weight).

Description of variables is the following:

company – discrete variable, describing company-producer:

1. Acer

2. Asus

3. Dell

4. HP

5. Lenovo

6. MSI

7. Toshiba

typename – discrete variable, describing type of type of machine:

1. 2 in 1 Convertible

2. Gaming

3. Netbook

4. Notebook

5. Ultrabook

6. Workstation

ram – discrete variable, describing amount of RAM:

• 4GB

• 8GB

• 16GB

• 32GB

weight – weight of laptop (in kg)

price_euros – price in EUR

inches – screen resolution in inches

Note: 4GB was a base level for RAM and base level for laptop type was Notebook.

1. Name all significant variables. Interpret coefficients.

2. Examine dependence between price and weight. Which conclusion can you make?

3. Researcher estimated new model, introducing there a new variable weight2 = weight^2. Which changes in fit you can observe?

4. Next, he added interaction between weight and type of laptop. How can you interpret this interaction?

5. Which variables, not included in dataset, could be a good regressors? Justify your opinion

### Exercise 3. 计量经济学考试代写

Using a sample of 1801 individuals, the following earning equation has been estimated

where the standard errors are reported in brackets.

1. Which variables are significant? Explain

2. Interpret the coefficient estimate on female.

3. InterpretR2.

4. Dropping experience and female from the equation gives:

Are experience and female jointly significant in the original equation at the 5% significance level? Please write: (i) the null and alternative hypothesis, (ii) test statistic, (iii) proper critical value, (iv) conclusion.

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