1. Under the Gauss-Markov assumptions in the multiple linear regression model, the OLS estimator isBLUE. This means what?(a) The OLS estimator is an Alien(b) The OLS estimator has wavelength between 440 and 490 nanometers(c) The OLS estimator supports FC Porto(d) None of the answers above is correct2. Which of the following (multiple linear regression) assumptions is violated if the person you hir
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1. Under the Gauss-Markov assumptions in the multiple linear regression model, the OLS estimator is

BLUE. This means what?

(a) The OLS estimator is an Alien

(b) The OLS estimator has wavelength between 440 and 490 nanometers

(c) The OLS estimator supports FC Porto

(d) None of the answers above is correct

2. Which of the following (multiple linear regression) assumptions is violated if the person you hired

to collect data on workers only interviewed people shorter than him (on purpose)?

(a) No perfect collinearity assumption

(b) Random Sampling

(c) Zero conditional mean assumption, necessarily

(d) Zero conditional mean assumption, if there is a systematic relation between omitted factors from

the model (including height) and the variables included

(e) both a) and b) are correct;

(f) both b) and c) are correct;

(g) both b) and d) are correct;

3. What is Econometrics?

(a) Econometrics is a natural science concerned with the study of life and living organisms,

including their structure, function, growth, origin, evolution, distribution and taxonomy.

(b) Econometrics is concerned with the tasks of developing and applying quantitative or

statistical methods to the study of causalities, testing of economic theories, and evaluation and

implementation of government and business policy.

(c) Econometrics is the science concerned with the laws and effects of molecular forces.

(d) all of the answers above are correct.

(e) None of the answers above is correct

4. OLS stands for what in Econometrics?

(a) Optimally Linearized Solution

(b) There is no such thing in Econometrics

(c) The only rock band that Econometricians are crazy about

(d) Ordinary Least Squares

(e) None of the answers above is correct

5. An unbiased estimator is an estimator whose sampling distribution has

a) mean equal to the true parameter value being estimated

b) mean equal to the actual value of the parameter estimate

c) a zero variance

d) none of the above

6. Heteroskedasticity is about

a) errors having different variances across observations

b) explanatory variables having different variances across observations

c) different explanatory variables having different variances

d) none of these

7. Asymptotics refers to what happens when

a) the sample size becomes very large

b) the sample size becomes very small

c) the number of explanatory variables becomes very large

d) the number of explanatory variables becomes very small

8. The terminology ceteris paribus means

A.that if event A precedes event B, A has caused B.

B.that economics deals with facts, not values.

C. other things equal.

D. prosperity inevitably follows recession.

9. In the regression specification y = α + βx + ε

a) y is called the dependent variable or the regressand, and x is called the regressor

b) y is called the dependent variable or the regressor, and x is called the regressand

c) y is called the independent variable or the regressand, and x is called the regressor

d) y is called the independent variable or the regressor, and x is called the regressand

10. In the regression specification y = α + β1x1 + β2x2 + u the parameter β1 is interpreted as the amount

by which y changes when x increases by one and

a) x2 does not change

b) x2 changes by one

c) x2 changes by the amount it usually changes whenever x increases by one

d) none of the above

11. In the regression specification y = α + βx + ε

a) α is called the intercept, β is called the slope, and ε is called the residual

b) α is called the slope, β is called the intercept, and ε is called the residual

c) α is called the intercept, β is called the slope, and ε is called the error

d) α is called the slope, β is called the intercept, and ε is called the error

12. The p value is:

a) the power

b) one minus the power

c) the type II error

d) none of the above

13. Hypothesis testing is based on

a) minimizing the type I error

b) minimizing the type II error

c) minimizing the sum of type I and type II errors

d) none of these

14. The probability of a type II error is determined by

a) the researcher

b) the sample size

c) the degree of falsity of the null hypothesis

d) both b and c

15. A type II error is

a) failing to reject the null when it is false

b) rejecting the null when it is true

16. The probability of a type I error is determined by

a) the researcher

b) the sample size

c) the degree of falsity of the null hypothesis

d) both b) and c) above

17. A type I error is

a) failing to reject the null when it is false

b) rejecting the null when it is true

18. The central limit theorem assures us that the sampling distribution of the mean

a) is always normal

b) is always normal for large sample sizes

c) approaches normality as the sample size increases

d) appears normal only when the sample size exceeds 1,000

19. Given the table below what is the expected value of x?

x | 1 | 2 | 3

Prob (x) | 0.1 | 0.2 | ma) 2.0

b) 2.1

c) 2.6

d) indeterminate

Because: Sum of probabilities (x) should be equal to 1. Therefore, m = 1 – (0.1+0.2) = 0.7.

The last step is to multiply all probabilities by corresponding x. Thus, expected value = 0.1x1 +

0.2x2 + 0.7x3 = 2.6.

20. Given the table below what is the value of m?

x | 1 | 2 | 3

Prob (x) | 0.1 | 0.2 | ma) 0.3

b) 0.5

c) 0.7

d) indeterminate

21. If *Yt **o * 1*X t **t *, then for OLS, it will be:

22. Suppose that Y = AX + B. Then what is the Var(Y) ?

A²Var(X)

23. Which of the following cannot be negative?

a. Covariance

b. Variance

c. E[r]

d. Correlation coefficient

24. What is the homoscedasticity?

A) The same variance given any value of the explanatory variable

B) The error u has an expected value of zero given any value of the explanatory variable

C) The error has not the same variance given any value of the explanatory variable

D) None of the above

25. Which of the following statements regarding simple linear regression is most accurate?

A model where the dependent variable is a linear function of a single

independent variable, plus an error term

26. What is the most appropriate interpretation of a slope coefficient estimate equal to 10.0?

If X increases by 1, we predict Y will increase 10 times.

27. Which of the following is not a necessary assumption of simple linear regression analysis?

Necessary assumptions of simple linear regression analysis include:

(i) linearity of the relationship between dependent and independent variables

(ii) independence of the errors (no serial correlation)

(iii) homoscedasticity (constant variance) of the errors

(iv) normality of the error distribution.

28. Probability distribution of a discrete random variable X

X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7

P(X) | 0.04 | 0.11 | 0.18 | 0.24 | 0.14 | 0.17 | 0.09 | 0.03The probability of X=3

The probability of X=3 is equal to 0.24.

29. Which of the following is not the an example of discrete random variable:

Which of the following is not an example of a discrete random variable?

a. The number of days it rains in a month in New York

b. The number of stocks a person owns

c. The number of persons allergic to penicillin

d. The time spent by a physician with a patient

30. Which of the followings cannot be the probability of the event:

(-∞,0)U(1,+∞)

31. Annual stock prices of ABC Company

2007

22% | 2008

5% | 2009

-7% | 2010

11% | 2011

2% | 2012

11%What is the mean of the ABC company stock?

7.3 %

32. Annual stock prices of ABC Company

2007

22% | 2008

5% | 2009

-7% | 2010

11% | 2011

2% | 2012

11%What is the median of the ABC company stock?

(11 + 5) / 2 = 8 %.

33. In testing multiple exclusion restrictions in the multiple regression model under the Classical

assumptions, we are more likely to reject the null hypothesis that some coefficients are zero if:

A) The R-squared of the restricted model is large relative to that of the unrestricted model

B) The R-squared of the restricted model is small relative to that of the unrestricted model

C) The total sum of squares, SST, is large

D) The intercept parameter is greater than the significance level

E) Both a and d

F) Both c and d

34. Consider a multiple linear regression model satisfying the Gauss-Markov assumptions. The

variances of the OLS estimators for individual coefficients decrease if, all else equal:

A) The total sum of squares of the dependent variable increases

B) The total sum of squares of the regressors decreases

C) The total sum of squares of the regressors increases

D) The sample size decreases

E) A, b and c are correct

F) C and d are correct

G) A and d are correct

35. In a multiple linear regression, a low R2 indicates which of the following?

A) The regressors explain a low fraction of the variation in the dependent variable

B) The regressors explain a large fraction of the variation in the dependent variable

C) The regressors are highly correlated in the sample

D) There is low correlation among regressors in the sample

E) The OLS estimators have a large variance

F) Both a and e are correct

36. Consider the following unrestricted model:

y = + β1x1 + β2x2 + … + u

whereas a restricted model is:

y = + β1x1 + β2x2 + … + u

What is the name of the test of multiple exclusion restrictions (implied by the restricted model) and

what are (null hypothesis) and (alternative hypothesis) of this test?

A) The test is called t-test : = 0, ……………….., and : is false

B) The test is called Chi-square test where : = 0, …..………….., and :

is true

C) The test is called F-test where : = 0, …..………….., and : is true

D) The test is called F-test where : = 0, …..………….., and says that all

of these parameters are different from 0

E) The test is called F-test where : = 0, …..………….., and says that

at least one of these parameters is different from 0

37. Suppose you are given the multiple regression model with following assumptions:

y = + β1x1 + β2x2 + … + u

A1) Linearity in the explanatory variables

A2) Random sampling

A3) E(u|x1; x2; x3;…; xk) = 0

A4) No perfect collinearity

A5) Linearity in the parameters

A6) Heteroskedasticity of the error term

A7) Homoskedasticity of the error term

A8) Normality of the error term and independence from the regressors

Under which assumptions from the above (A1-A8), E( ) = for j = 0; 1; … ; k, where is the

best linear unbiased estimator (BLUE) for ?

A) A1+A2+A3+A4+A5

B) A2+A3+A4+A5+A7

C) A1+A2+A3+A4+A7

D) A2+A3+A4+A5+A6

E) A2+A3+A4+A5+A8

F) None of the answers above is correct

38. Suppose you are given the multiple regression model with following assumptions:

y = + β1x1 + β2x2 + … + u

A1) Linearity in the explanatory variables

A2) Random sampling

A3) E(u|x1; x2; x3;…; xk) = 0

A4) No perfect collinearity

A5) Linearity in the parameters

A6) Heteroskedasticity of the error term

A7) Homoskedasticity of the error term

A8) Normality of the error term and independence from the regressors

Under which assumptions from the above (A1-A8), E( ) = for j = 0; 1; … ; k, where is the

OLS estimator of parameter ?

A) A1+A2+A3+A4

B) A2+A3+A4+A7

C) A1+A3+A4+A7

D) A2+A3+A4+A6

E) A2+A3+A4+A5

F) None of the answers above is correct

39. In a multiple linear regression, what means heteroskedasticity of the error term?

A) All error terms have the same variance across all observations i = 1, 2, …, n

B) The regression have each the same variance across all random observations i = 1, 2, …, n

C) Two error terms and with j ≠ I have always a different variance for i = 1, 2, …, n

D) The error terms do not have the same variance across all i = 1, 2, …, n

E) The regressors and the error term are linearly dependent

40. Suppose a researcher wants to test the hypothesis that coefficient *βi *is equal to *a *in a multiple linear

regression model. What is the difference in the alternative hypothesis between a one-tailed test and a

two-tailed test?

A) The alternative hypothesis for a one-tailed test is either : or :

and for a two-tailed test it is :

B) The alternative hypothesis for a one-tailed test is : and for a two-tailed test it is

:

C) The alternative hypothesis for a one-tailed test is : and for a two-tailed test it is

:

D) The alternative hypothesis for a one-tailed test is : and for a two-tailed test it is

either : or :

41. In a linear regression model, if the error term is homoskedastic, then, necessarily:

(a) the OLS estimator is unbiased.

(b) we can guarantee the OLS estimator is BLUE.

(c) we can guarantee the OLS estimator is the minimum variance unbiased estimator.

(d) the OLS estimator is useless.

(e) both (b) and (c) are true.

(f) both (b) and (d) are true.

(g) none of the answers above is correct

42. Including irrelevant variables (i.e., variables whose effect on the dependent variable is 0) in a

multiple linear regression does not increase the variance of the OLS estimator of the remaining

parameters if

(a) the irrelevant variables are correlated with the error term

(b) the irrelevant variables are uncorrelated with the error term

(c) the irrelevant variables are uncorrelated with the relevant variables

(d) the irrelevant variables are correlated with the relevant variables

(e) the answer depends on the exact value of the relevant coefficients

43. Consider the following definitions regarding data sets:

D1) consists of a time series for each cross-sectional member in the data set.

D2) consists of observations on several variables over time.

D3) consists of a sample of individuals, households, firms, cities, states, and etc., taken at a given

point in time

Given the definitions above (D1-D3), indicate a correct statement:

(a) Cross-sectional data is D3, time-series data is D2, and panel data is D1.

(b) Cross-sectional data is D1, time-series data is D2, and panel data is D3.

(c) Cross-sectional data is D2, time-series data is D3, and panel data is D1.

(d) Cross-sectional data is D2, time-series data is D1, and panel data is D3.

(e) All of the above are correct

(f) None of the above is correct

44. In testing multiple exclusion restrictions in the multiple regression model under the Classical

assumptions, we are more likely to reject the null that some coefficients are zero if:

A) The Residuals sum of squares of the restricted model is large relative to that of the

unrestricted model

B) The Residuals sum of squares of the restricted model is small relative to that of the unrestricted

model

C) The total sum of squares, SST, is large

D) The intercept parameter is greater than the significance level

E) Both a and d

F) Both c and d

45. Of the definitions below, which best describes the property "Unbiasedness of the OLS estimators"?

A) The average of the OLS estimates across a big number of samples equals the true parameter

value

B) The distribution of the OLS estimators has small mass for values far away from the true

parameter value

C) The average of the OLS estimates across an infinite number of samples equals the true

parameter value

D) The distribution of the OLS estimators has a mean very close to the true parameter value

E) None of the above descriptions describes properly “Unbiasedness of the OLS estimators”

46. Consider the simple linear regression model for cross-sectional data y = β0+β1x+u. If E[u] = 0 and x

and u are independent, this implies:

A) E[u|x] = 0

B) x and u are uncorrelated

C) E[u|x²] = 0

D) Both b and c are correct

E) Both a and c are correct

F) A, b and c are correct

47. We estimate the model *Wagei *= 370. 25 + 20.43*Educi *+ 13.87*Hoursi *by OLS, where *Wage *is the

monthly of a worker measured in euros, *Educ *measures the number of years of schooling and *Hours*

is the number of hours worked per day. Suppose the R2 of this regression is 0.37, and that the

number of observations was n = 150. What can you conclude?

(a) 55.5% (=0.37*150) of the total variation in wages is explained by the proposed

model

(b) 37% of the total variation in wages is explained by the proposed model

(c) The R² has to be greater than 1 for the regression to be profitable

(d) b) and c) are correct

(e) None of the above

48. Take an observed (that is, estimated) 95% confidence interval for a parameter of a multiple linear

regression. If you increase the confidence level to 99% then, necessarily:

(a) The length of the confidence interval decreases

(b) The length of the confidence interval remains unchanged

(c) The length of the confidence interval increases

(d) None of the answers above is correct

49. In a multiple linear regression model, if we know that E[ui|xi] = 0 we also know that:

A) E[ ] = 0

B) Var( ) = 0

C) Var( ) =

D) Var( ) = σ²

E) Both a and b

F) Both a and c

G) Both a and d

50. In the estimated model log(qi) = 2.25 – 0.7 log(pi) + 0.02yi; where *p *is the price and *q *is the

demanded quantity of a certain good and *y *is disposable income, what is the meaning of the

coefficient on *y*?

A) If disposable income increases by a thousand dollars, the demanded quantity will be 0.02%

higher on average, ceteris paribus

B) If disposable income increases by a thousand dollars, the demanded quantity will be

0.0002% higher on average, ceteris paribus

C) If disposable income increases by a thousand dollars, the demanded quantity will be

2% higher on average, ceteris paribus

D) None of the answers is correct

51. In the estimated model log(qi) = 2.25 – 0.7 log(pi) + 0.02yi; where *p *is the price and *q *is the

demanded quantity of a certain good and *y *is disposable income, what is the meaning of the

coefficient on log(p)?

A) If the price increases by 1%, the demanded quantity will be 0.007% lower on average, ceteris

paribus

B) If the price increases by 1%, the demanded quantity will be 70% lower on average, ceteris

paribus

C) If the price increases by 1%, the demanded quantity will be 0.7% lower on average,

ceteris paribus

D) None of the answers is correct

52. When choosing observations from a population of interest that will be studied using Cross-Section

Econometrics, we should:

(a) Chose all the elements of the population, even if it costs e 700 billion

(b) Make sure all observations have the same probability of being chosen

(c) Select the largest (feasible) number of observations

(d) b) and c) are correct

(e) a) and b) are correct;

(f) All of the above are correct

(g) None of the above is correct

53. Of the following assumptions, which one(s) is (are) necessary to guarantee unbiasedness of the OLS

estimator in a multiple linear regression context?

(a) Linearity of the model in the parameters

(b) Zero conditional mean of the error term

(c) Absence of multicollinearity

(d) Homoskedasticity of the error term

(e) Random sampling

(f) All of the above except e)

(g) All of the above except d)

(h) All of the above

54. The accompanying graph is an example of

A) experimental data.

B) cross-sectional data.

C) a time series.

D) longitudinal data.

55. Most economic data are obtained

A) through textbook examples typically involving ten observation points.

B) through randomized controlled experiments.

C) by observing real-world behavior.

D) by calibration methods

56. Analyzing the effect of minimum wage changes on teenage employment across the 48 contiguous

U.S. states from 1980 to 2004 is an example of using

A) time series data.

B) panel data.

C) having a treatment group vs. a control group, since only teenagers receive minimum wages.

D) cross-sectional data.

Answer: B

57. Studying inflation in the United States from 1970 to 2006 is an example of using

A) randomized controlled experiments

B) time series data

C) panel data

D) cross-sectional data

58. Analyzing the behavior of unemployment rates across U.S. states in March of 2006 is an example of

using

A) time series data

B) panel data

C) cross-sectional data

D) experimental data

59. Multicollinearity occurs whenever

a) the dependent variable is highly correlated with the independent variables

b) the independent variables are highly orthogonal

c) there is a close linear relationship among the independent variables

d) there is a close nonlinear relationship among the independent variables

60. In general, omitting a relevant explanatory variable creates

a) bias and increases variance

b) bias and decreases variance

c) no bias and increases variance

d) no bias and decreases variance

61. Suppose that y = α + βx + δw + ε but that you have ignored w and regressed y on only x. If x and w

are negatively correlated in your data, the OLS estimate of β will be biased downward if

a) β is positive

b) β is negative

c) δ is positive

d) δ is negative

62. Omitting a relevant explanatory variable when running a regression

a) never creates bias

b) sometimes creates bias

c) always creates bias

Omitting a relevant explanatory variable when running a regression usually

a) increases the variance of coefficient estimates

b) decreases the variance of coefficient estimates

c) does not affect the variance of coefficient estimates

63. Suppose we are regressing wage on an intercept, education, experience, gender, and dummies for

black and hispanic (the base being white). To find the restricted SSE to calculate an F test to test the

null hypothesis that the black and hispanic coefficients are equal we should regress wage on an

intercept, education, experience, gender, and a new variable. If the null hypothesis is true then,

compared to the unrestricted SSE, the restricted SSE should be

a) smaller

b) the same

c) larger

d) unpredictable

64. Suppose we are regressing wage on an intercept, education, experience, gender, and dummies for

black and hispanic (the base being white). To find the restricted SSE to calculate an F test to test the

null hypothesis that the black and hispanic coefficients are equal we should regress wage on an

intercept, education, experience, gender, and a new variable constructed as the

a) sum of the black and hispanic dummies

b) difference between the black and hispanic dummies

c) product of the black and hispanic dummies

d) none of these

65. As the sample size becomes very large, the t distribution

a) collapses to a spike because its variance becomes very small

b) collapses to normally-distributed spike

c) approximates more and more closely a normal distribution with mean one

d) approximates more and more closely a standard normal distribution

66. To conduct a t test we need to

A) estimate something that is supposed to be zero and divide it by its mean

B) estimate something that is supposed to be zero and divide it by its variance

C) divide a parameter estimate by its standard error

D) estimate something that is supposed to be zero and see if it is zero

E) All of the above

67. You have obtained the following regression results using data on law students from the class of 1980

at your university:

Income = 11 + .24GPA - .15Female + .14Married - .02Married*Female

where the variables are self-explanatory. Consider married individuals with equal GPAs. Your results

suggest that compared to female income, male income is higher by

a) 0.01

b) 0.02

c) 0.15

d) 0.17

68. Multicollinearity occurs when

a) the dependent variable is highly correlated with all of the explanatory variables

b) an explanatory variable is highly correlated with another explanatory variable

c) the error term is highly correlated with an explanatory variable

d) the error term is highly correlated with the dependent variable

69. Your data produce the regression result y = 8 + 5x. If the x values were scaled by multiplying them

by 0.5 the new intercept and slope estimates will be:

a) 4 and 2.5

b) 8 and 2.5

c) 8 and 10

d) 16 and 10

70. Suppose your data produce the regression result y = 10 + 3x. Consider scaling the data to express

them in a different base year dollar, by multiplying observations by 0.5. If y is scaled but x is not

(because y is measured in dollars and x is measured in physical units, for example), the new

intercept and slope estimates will be

5 and 1.5

71. Suppose your data produce the regression result y = 10 + 3x. Consider scaling the data to express

them in a different base year dollar, by multiplying observations by 0.9. If both y and x are scaled,

the new intercept and slope estimates will be:

a) 10 and 3

b) 9 and 3

c) 10 and 2.7

d) 9 and 2.7

72. Suppose we run a regression of y on x and save the residuals as e. If we now regress e on x the slope

estimate should be:

a) zero

b) one

c) minus one

d) nothing can be said about this estimate

73. If the expected value of the error term is 5, then after running an OLS regression

a) the average of the residuals should be approximately 5

b) the average of the residuals should be exactly zero

c) the average of the residuals should be exactly five

d) nothing can be said about the average of the residuals

74. You have regressed y on x to obtain ŷ = 3 + 4x. If x increases from 7 to 10, what is your forecast of

y?

a) 12

b) 31

c) 40

d) 43

75. Suppose we have obtained the following regression results using observations on 87 individuals: ŷ =

3 + 5x where the standard errors of the intercept and slope are 1 and 2, respectively. If an individual

increases her x value by 4, what impact do you predict this will have on her y value? Up by

a) 4

b) 5

c) 20

d) 23

76. You have 46 observations on y (average value 15) and on x (average value 8) and from an OLS

regression have estimated the slope of x to be 2.0. Your estimate of the mean of y conditional on x is

a) 15

b) 16

c) 17

d) none of the above

77. Suppose the regression specification y = α + βx + ε was estimated as y = 1 + 2x. We have a new

observation for which x = 3 and y = 11. For this new observation the residual is:

a) zero

b) 4

c) –4

d) unknown because the error is unknown

78. In the regression specification y = α + βx + ε if the expected value of epsilon is a fixed number but

not zero

a) the regression cannot be run

b) the regression is without a reasonable interpretation

c) this non-zero value is accommodated by the βx term

d) this non-zero value is incorporated into α

79. You have an estimate 1.75 of a slope coefficient which you know is distributed normally with

unknown mean beta and known variance 0.25. You wish to test the null that beta = 1 against the

alternative that beta > 1 at the 5% significance level. The critical value to use here is:

a) 1.28

b) 1.65

c) 1.96

d) none of these

80. When you calculate a 95% confidence interval for an unknown parameter beta, the interpretation of

this interval is that:

a) the probability that the true value of beta lies in this interval is 95%

b) 95% of repeated calculations of estimates of beta from different samples will lie in this interval

c) 95% of intervals computed in this way will cover the true value of beta

d) none of the above

81. As the sample size becomes larger, the type I error probability

a) increases

b) decreases

c) does not change

d) can’t tell

82. Which of the following is TRUE?

83. There was estimated urban travel time between locations in Baku. Data was collected for

motorcycles and passenger cars. Simple linear regression was conducted using data sets for both

types of vehicles, where Y = urban travel time in minutes and X= distance between locations in

kilometers. The following results were obtained:

Regression Results for Travel Times Between Distances in Baku

Passenger cars: | Y=1.85+3.86X | R2=0.758

Motorcycles | Y=2.50+1.93X | R2=0.676If the motorcycle traveler is planning to move 18 km farther from his previous workplace in Baku

how much the travel time will increase

1.93 * 18 = 34.74

84. There was estimated urban travel time between locations in Baku. Data was collected for

motorcycles and passenger cars. Simple linear regression was conducted using data sets for both

types of vehicles, where Y = urban travel time in minutes and X= distance between locations in

kilometers. The following results were obtained:

Regression Results for Travel Times Between Distances in Baku

Passenger cars: | Y=1.85+3.86X | R2=0.758

Motorcycles | Y=2.50+1.93X | R2=0.676Based on regression results, which model are more reliable?

A model for Passenger cars as R² is larger.

85. Probability distribution of a discrete random variable X

X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7

P(X) | 0.04 | 0.11 | 0.18 | 0.24 | 0.14 | 0.17 | 0.09 | 0.03The cdf of 5 or F(5)

F(5) = 0.04+0.11+0.18+0.24+0.14+0.17 = 0.88

86. Probability distribution of a discrete random variable X

X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7

P(X) | 0.04 | 0.11 | 0.18 | 0.24 | 0.14 | 0.17 | 0.09 | 0.03The probability that X is greater than 3 is:

1 – (0.04+0.11+0.18+0.24) = 0.43

87. Probability distribution of a discrete random variable X

X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7

P(X) | 0.04 | 0.11 | 0.18 | 0.24 | 0.14 | 0.17 | 0.09 | 0.03What is P(2≤X≤5)?

P = 0.18+0.24+0.14+0.17 = 0.73

88. Probability distribution of a discrete random variable X

X | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7

P(X) | 0.04 | 0.11 | 0.18 | 0.24 | 0.14 | 0.17 | 0.09 | 0.03What is the expected value of random variable X?

expected value = 0.04*0 + 0.11*1 + 0.18*2 + 0.24*3 + 0.14*4 + 0.17*5 + 0.09*6 + 0.03*7 = 3.35

89. For the continuous random variable X, the probability of any single value of X is:

For a continuous random variable, the probability of any single value is zero.

90. The correlation of the price of Good A and B is 0.50.The covariance between these two goods is

0.0043 and standard deviation if the Good B is 26%. What is the variance of Good A?

0.5 = 0.0043/0.26*x

x = 0.033 var = 0.001089

91. For the sample of 25 observations the average and standard deviation are 20 and 3 respectively. Find

the interval for population mean with 95% confidence (consider the critical value of t for 95%

confidence interval and 24 d.f. to be 2).

CI = [18.8; 21.2]

20 – 2 * 0.6 = 18.8

20 + 2 * 0.6 = 21.2

92. For the sample of 25 observations the average and standard deviation are 15 and 3 respectively. Find

the interval for population mean with 90% confidence (consider the critical value of t for 90%

confidence interval and 24 d.f. to be 1.7).

CI = [13.98; 16.02]

15 – 1.7 * 0.6 = 13.98

15 + 1.7 * 0.6 = 16.02

93. The folowing regression was estimated for the university teachers. Choose the correct statement:

*Yi = 5 + 40Xi + 11Di + ei*

Yi – weekly salary of the i-th teacher

*Xi *– level of education of the i-th teacher

*Di *– qualitative dummy, it is equal to 1 if the teacher is male and 0 otherwise

94. Annual stock prices of ABC Company

2007

22% | 2008

5% | 2009

-7% | 2010

11% | 2011

2% | 2012

11%Assume that distribution of ABC Company stock returns is population. What is the population

variance?

A. 6.8%2

B. 7.7%2

C. 10.2%2

D. 80.2%2

95. Annual stock prices of ABC Company

2007

22% | 2008

5% | 2009

-7% | 2010

11% | 2011

2% | 2012

11%Assume that distribution of ABC Company stock returns is sample. What is the sample standard

deviation?

A) 96.3

B) 9.8

C) 7.3

D) 5.0

E) 72.4

96. The rationale behind the F test is that if the null hypothesis is true, by imposing the null hypothesis

restrictions on the OLS estimation the per restriction sum of squared errors

a) falls by a significant amount

b) rises by a significant amount

c) falls by an insignificant amount

d) rises by an insignificant amount

97. Suppose you are using the specification wage = α + βEducation + δMale + θExperience + ε. In your

data the variables Education and Experience happen to be highly correlated because the observations

with a lot of education happen not to have much experience. As a consequence of this negative

correlation the OLS estimates

a) are likely to be better because the movement of one explanatory variable offsets the other,

allowing the computer more easily to isolate the impact of each on the dependent variable

b) are likely to be better because the negative correlation reduces variance making estimates more

reliable

c) are likely to be worse because the computer can’t tell which variable is causing changes in

the dependent variable

d) are likely to be worse because compared to positive correlation the negative correlation increases

variance, making estimates less reliable

98. Suppose wage = α + βexp + ε and we have 50 observations on wage and exp (experience), with

average values 10 and 8, respectively. We have run a regression to estimate the intercept as 6.0.

Consider now a new individual whose exp is 10. For this individual the predicted wage from this

regression is:

a) 6

b) 10

c) 11

d) impossible to predict without knowing the slope estimate

99. In the regression specification y = α + βx + ε the expected value of y conditional on x=1 is

a) the average of the sample y values corresponding to x=1

b) α + β + ε

c) β

d) α + β

100. You have an estimate 1.75 of a slope coefficient which you know is distributed normally

with unknown mean beta and known variance 0.25. You wish to test the null that beta = 1 against the

alternative that beta > 1 at the 10% significance level. You should _____ the null. If you had used a

5% significance level you would ______ the null. The blanks are best filled with:

a) accept; accept

b) accept; reject

c) reject; accept

d) reject; reject

101. It was studied that 60% of the companies use fax machine. From the binomial probability

distribution, what is the probability of that exactly four companies will have a fax machine from

randomly selected six companies?

P(x=4) = = 0.311

102. Which of the following is least likely probability distribution:

Which of the following is least likely a probability distribution?

A) Roll an irregular die: p(1) = p(2) = p(3) = p(4) = 0.2 and p(5) = p(6) = 0.1.

B) Flip a coin: P(H) = P(T) = 0.5.

C) Zeta Corp.: P(dividend increases) = 0.60, P(dividend decreases) = 0.30.

103. If A and B are statistically independent events and P(A|B) = 0.04, P(B) = 0.1 find P(A).

P(A) = P(A|B) = 0.04

104. If A and B are statistically dependent events and P(A|B) = 0.4, P(B) = 0.6, find the value of

P(AB)

P(AB) = 0.4x0.6 = 0.24

105. If R-squared for X and Y is equal to 1, show the correct regression equation for the two

variables.

y=b0+b1x, to yest u = 0

A) Yt=4+2Xt+3

B) Yt=-2X+ut

C) Yt=3+Xt

D) Yt=-2x+4t

E) Yt=5+xT+3

106. In which case we might have the R-squared statistic equal to 1?

when SSR equal to 0

107. Assume the following linear regression:

ln(Y) = 5 + 9ln(X) + u

What does the slope coefficient tell us?

This is an elasticity: a one percent increase in ln(X) volumes implies a 9% increase in predicted

median starting Y, other things equal.

108. The significance level of a test is the probability that you:

A rejection of the null hypothesis when it is true

109. Which of the following is NOT a known step in methodology of econometrics?

There are four main stages in the methodology of econometric research and they include:

o Model specification

o Estimation of the model

o Evaluation of the estimates

o Evaluation of the forecasting validity of the model

110. Yearly GDP growth rate of Argentina, Austria, Belgium and Bolivia together for period of

1998-2000 are examples of…

Panel data

111. Quarterly inflation rate of Azerbaijan for period of 2004-2012 years are examples of…

Times series data

112. Which of following is a definition for independent variable?

In regression analysis, a variable that is used to explain variation in the dependent variable

113. Which of the Computer programs are not primary Econometrics programs?

Primary ones:

On-Line Search Services

Spreadsheet

Text editor

Excel

Eviews

114. OLS stands for what in Econometrics?

OLS stands for what in Econometrics?

(a) Optimally Linearized Solution

(b) There is no such thing in Econometrics

(c) The only rock band that Econometricians are crazy about

(d) Ordinary Least Squares

(e) None of the answers above is correct

115. A residual is…

The difference between the actual value and the fitted (or predicted) value; there is a residual for

each observation in the sample used to obtain an OLS regression line.

116. The dependent variable…

The variable to be explained in a multiple regression model (and a variety of other models).

117. Heteroscedasticity is...

The variance of the error term, given the explanatory variables, is not constant.

118. Which of the following is not part of “BLUE” determined OLS featured…

A) Unbiased

B) Linear

C) Unknown

D) Estimator

E) Linear

119. Which of the below is true for sample populations

120.Which is the followings is true about Conditional Expectations…

121.The total population is...

A) By definition is all total observations for certain specific features.

B) Several prior observations

C) First ten observations

D) Only the half of total observations

122.Which of the below is true about Normal Distributions.

I. It is a bimodal distribution.

II. It can be characterized completely by a single parameter.

III. It ranges from negative infinity to positive infinity.

IV. It is positively skewed.

A. III and IV

B. III only

C. II and III

Which of the following statements is true regarding the normal distribution?

a. Mean, median, mode are not necessarily equal

b. It is symmetrical

c. It can have more than a single peak

d. The points of the curve touch the X-axis at z=-3 and z=3

e. All are true for a normal distribution

123.A disturbance term is…

error term (u) represents factors other than *x *that affect *y*.

124.The goal of OLS is to find the values of the estimated parameters that:

The goal is to find the parameter values for the model which "best" fits the data. The least squares

method finds its optimum when the sum, S, of squared residuals is a minimum. A residual is defined

as the difference between the actual value of the dependent variable and the value predicted by the

model.

125.Which of the below assumptions on error term is true assumptions for OLS:

Assumptions for OLS:

1. Regression is linear in parameters (Linearity)

2. Error term has zero population mean (E(εi)=0)

3. Error term is not correlated with X’s (Exogeneity)

4. No serial correlation

5. No heteroskedasticity

6. No perfect multicollinearity

7. Error term is normally distributed (Normally Distributed Error)

126. Which of the following is TRUE about R-square

A) It is also called the coefficient of determination.

B) It is also called the coefficient of variati12on.

C) It represents the percent of variation in X that is explained by Y.

D) It represents the percent of variation in the error that is explained by Y.

E) It ranges in value from -1 to + 1.

127.Multicollinearity

when an exact relationship between explanatory variables exists

128.When choosing between regression models it is preferable to choose the one with:

129.Consider the following regression equation estimated by OLS

There are .... degree of freedom in the model.

53 – 2 = 51

130.Which of the following statements is correct?

131.The Gauss-Markov Theorem says that when the classical assumptions are satisfied:

a.) The least squares estimator is unbiased

b.) The least squares estimator has the smallest variance of all linear estimators

c.) The least squares estimator has an approximately normal sampling distribution

d.) The least squares estimator is consistent

e.) None of the above

132.The central limit theorem tells us that the sampling distribution of least squares regression

coefficients:

a.) is always normal

b.) is always normal in large samples

c.) approaches normality as the sample size increases

d.) is normal in Monte Carlo simulations

e.) none of the above

133.The sample average of the OLS residuals is ?

The sum of the OLS residuals is zero; Thus, the sample average of the OLS residuals is zero as

well

134.Assume that according to regression output obtained excel, estimated coefficient of independent

variable is equal to 14 and Standard Error (SE) is equal to 2. What is the value of t-value of above

regression output?

14/2 = 7

135.Which of the following is pure example for regression Analysis?

136.Objective of Correlation Analysis is…

The main objective of correlation analysis is to determine strength of relationship between two

variables

137.Which of the followings is not TRUE about following single equation model. *Y=β1 + β2X+u*.

138.Which of the followings is included to the steps of methodology of econometrics …

There are four main stages in the methodology of econometric research and they include:

o Model specification

o Estimation of the model

o Evaluation of the estimates

o Evaluation of the forecasting validity of the model

139.Which of the features of Correlation Coefficient is TRUE for correlation analysis?

140.Where econometrics can be applied?

141.The following model is given: *Y=β1 + β2X+u*. Which of the following statements is not TRUE for

above model?

142.Which of the following statements is correct?

143.Which is the followings are or not part of methodology of Econometrics?

There are four main stages in the methodology of econometric research and they include:

o Model specification

o Estimation of the model

o Evaluation of the estimates

o Evaluation of the forecasting validity of the model

144.Which of the below is true about following model *Y * *X *2

145.Which of the below is true about following model

146. | Suppose you have run the following regression:

y = α + βx + γUrban + θImmigrant + δUrban*Immigrant + εwhere Urban is a dummy indicating that an individual lives in a city rather than in a rural area, and

Immigrant is a dummy indicating that an individual is an immigrant rather than a native. The

coefficient δ is interpreted as the ceteris paribus difference in y between an urban immigrant and

a) a rural native

b) a rural immigrant

c) an urban native

d) none of these

147. | Suppose you have run the following regression:

y = α + βx + γUrban + θImmigrant + δUrban*Immigrant + εwhere Urban is a dummy indicating that an individual lives in a city rather than in a rural area, and

Immigrant is a dummy indicating that an individual is an immigrant rather than a native. The

coefficient θ is interpreted as the ceteris paribus difference in y between

a) an immigrant and a native

b) a rural immigrant and a rural native

c) an urban immigrant and an urban native

d) none of these

148. | Suppose you have run the following regression:

y = α + βx + γUrban + θImmigrant + δUrban*Immigrant + εwhere Urban is a dummy indicating that an individual lives in a city rather than in a rural area, and

Immigrant is a dummy indicating that an individual is an immigrant rather than a native. The

coefficient γ is interpreted as the ceteris paribus difference in y between

a) an urban person and a rural person

b) an urban native and a rural native

c) an urban immigrant and a rural immigrant

d) none of these

149. A negative coefficient on an explanatory variable x in a Linear Probability Model (LPM)

specification means that an increase in x will, ceteris paribus,

150. The Linear Probability Model (LPM) functional form

151. The Linear Probability Model (LPM) is employed when

152. Researcher A has used the specification:

y=β0 + β1x + β2MaleLeft+β3MaleCenter+β4MaleRight + β5FemaleLeft + β6FemaleCenter + ε

Here MaleLeft is a dummy representing a male on the left; other variables are defined in similar

fashion. Researcher B has used the specification:

y=β0B + β1Bx + β2BMale + β3BLeft + β4BCenter + β5BMale*Left + β6BMale*Center + ε

Here Male*Left is a variable calculated as the product of Male and Left; other variables are defined

in similar fashion. The base categories for specifications A and B are, respectively,

the same so that the estimate of β2 should be equal to the sum of the estimates of β2B,

β3B, and β5B.

153. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. We regress y on an intercept,

Male, Left, and Center. The intercept is interpreted as the intercept of a

a) male

b) male on the right

c) female

d) female on the right

154. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. We regress y on an intercept,

Male, Left, and Center. The base category is

a) a male on the left

b) a female on the left

c) a male on the right

d) a female on the right

155. The dummy variable trap occurs when

a) a dummy is not defined as zero or one

b) there is more than one type of category using dummies

c) the intercept is omitted

d) none of the above

156. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. A variable Fringe is created

as the sum of Left and Right, and a variable x*Male is created as the product of x and Male. Using

Fringe instead of Left and Right separately in this specification is done to force the slopes of Left

and Right to be

a) the same

b) half the slope of Center

c) twice the slope of Center

d) the same as the slope of Center

157. Suppose we specify that y y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right

where Left, Center, and Right refer to the three possible political orientations. A variable Fringe is

created as the sum of Left and Right, and a variable x*Male is created as the product of x and Male.

If we regress y on an intercept, x, Male, Left, and Center, the slope coefficient on Male is interpreted

as the intercept difference between males and females

a) regardless of political orientation

b) assuming a Right political orientation

c) assuming a Left or Center political orientation

d) none of the above

158. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. A variable Fringe is created

as the sum of Left and Right, and a variable x*Male is created as the product of x and Male. The

variable Fringe is interpreted as

The variable Fringe is interpreted as

a) being on the Left or on the Right

b) being on both the Left and the Right

c) being twice the value of being on the Left or being on the Right

d) none of these

159. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. A variable Fringe is created

as the sum of Left and Right, and a variable x*Male is created as the product of x and Male. Which

of the following creates a dummy variable trap? Regress y on an intercept, x,

a) Male and Left

b) Male, Left, and Center

c) Left, Center, and Right

d) None of these

160. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. Which of the following

creates a dummy variable trap? Regress y on an intercept, x,

a) Male and Fringe

b) Male, Center, and Fringe.

c) Both of the above

d) None of the above

161. Suppose you have estimated wage = 5 + 3education + 2gender, where gender is one for male

and zero for female. If gender had been one for female and zero for male, this result would have

been

a) Unchanged

b) wage = 5 + 3education - 2gender

c) wage = 7 + 3education + 2gender

d) wage = 7 + 3education - 2gender

162. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. Which of the following

creates a dummy variable trap? We regress y on an intercept, x, Male, Left and Center and then do

another regression, regressing y on an intercept, x, and Center and Right. The interpretation of the

slope estimate on Center should be

a) the intercept for those from the political center in both regressions

b) the difference between the Center and Right intercepts in the first regression, and the

difference between the Center and Left intercepts in the second regression

c) the difference between the Center and Left intercepts in the first regression, and the

difference between the Center and Right intercepts in the second regression

d) none of these

163. To predict the value of a dependent dummy variable for a new observation we should predict

it as a one if

164. Suppose we specify that y=β0 + β1x + β2Male + β3Female + β4Left + β5Center + β6Right where

Left, Center, and Right refer to the three possible political orientations. Which of the following

creates a dummy variable trap? Suppose we regress y on an intercept, x, and Male, and then do

another regression, regressing y on an intercept, x, and Female. The slope estimates on Male and on

Female should be

a) equal to one another

b) equal but opposite in sign

c) bear no necessary relationship to one another

d) none of these

165.Yearly GDP growth rate of Azerbaijan for period of 1998-2013 are examples of…

Times Series

166.Number of students in Azerbaijan State Economic University for period of 2004-2010 years is

examples of…

Times Series

167.Which of the Computer programs are primary Econometrics programs?

Excel, Eviews

168.Which of the below is true for sample populations

169.Which of the below is true about Normal Distributions.

170.A disturbance term is…

or Error term-The variable in a simple or multiple regression equation that contains unobserved

factors that affect the dependent variable. The error term may also include measurement errors in the

observed dependent or independent variables.

171. The central limit theorem assures us that the sampling distribution of the mean

a) is always normal

b) is always normal for large sample sizes

c) approaches normality as the sample size increases

d) appears normal only when the sample size exceeds 1,000

172. Suppose that Y = AX - B. Then what is the Var(Y)

A²Var(X)

173. Which of the following cannot be negative?

a. Covariance

b. Variance

c. E[r]

d. Correlation coefficient

174. Which of the followings cannot be the probability of the event:

(-∞,0)U(1,+∞)

175.What is auxiliary regression?

A regression used to compute a test statistic—such as the test statistics for heteroskedasticity and

serial correlation—or any other regression that does not estimate the model of primary interest.

176.Which of the following is TRUE about R-square

A)It is also called the coefficient of variation.

B) It represents the percent of variation in X that is explained by Y.

C) It represents the percent of variation in the error that is explained by Y.

D) It ranges in value from -1 to + 1

Bunlar da duzdur

R-squared cannot decrease when predictor variables are added to the multiple regression model.

R-squared describes the amount of variation in Y explained by the predictor variables.

R-squared equals 1 when all the predictions from the multiple regression model are correct

177.Consider the following regression equation estimated by OLS

There are .... degree of freedom in the model.

df=158-1-1=156

178.Which of the following statements is correct?

(a) all males in the sample earn 2% more than females.

(b) a male earns 2% more than females on average, ceteris paribus.

(c) on average, females have to invest more in education in order to have the same

wage than males, ceteris paribus.

(d) both (b) and (c) above are correct.

(e) none of the above is correct.

179.The Gauss-Markov Theorem says that when the classical assumptions are satisfied:

a.) The least squares estimator is unbiased

b.) The least squares estimator has the smallest variance of all linear estimators

c.) The least squares estimator has an approximately normal sampling distribution

d.) The least squares estimator is consistent

e.) None of the above

180.The central limit theorem tells us that the sampling distribution of least squares regression

coefficients:

a.) is always normal

b.) is always normal in large samples

c.) approaches normality as the sample size increases

d.) is normal in Monte Carlo simulations

e.) none of the above

181.Assume that according to regression output obtained excel, estimated coefficient of independent

variable is equal to 63 and Standard Error (SE) is equal to 3. What is the value of t-value of above

regression output?

Answer: 63/3=21

182.Which of the followings is not TRUE about following single equation model. *Y=β1 + β2X+u*.

183.Which of the features of Correlation Coefficient is not TRUE for correlation analysis?

184.Which of the below is true about following model *Y * *X *2

185. | Suppose you estimated the following regression:

wage = 2.2 + 0.18educ – 0.1married*wage *is the wage rate (in thousands of dollars), *educ *measures the number of years of schooling

completed and *married *is equal 1 if the person is married and 0 otherwise. What is the estimated

average wage rate for a married person with 10 years of schooling and the estimated average wage

rate for an uneducated married person?.

3900 and 2100

186. | Suppose you estimated the following regression:

wage = 3.2 + 0.2educ – 0.5married*wage *is the wage rate (in thousands of dollars), *educ *measures the number of years of schooling

completed and *married *is equal 1 if the person is married and 0 otherwise. What is the estimated

average wage rate for a single person with 5 years of schooling and the estimated average wage rate

for an uneducated married person?

4200 and 2700

187. You collected some data at ABC LLC on workers and estimated the following equation

(assume the Gauss-Markov assumptions hold in the theoretical model):

log(salary)=4.822 + 0.17log(sales) – 0.1late

where *salary *is net monthly wage*, sales *indicates the value of sales inputted to a worker and *late*

indicates the number of days (within a month) the worker came late to work.

Which of the following best describes the results from the estimated equation?

(a) an increase in sales of 1% increases the salary of a worker by 0.17% while one extra

day coming late decreases the salary by 0.1% (on average, ceteris paribus)

(b) an increase in sales of 1% increases the salary of a worker by 1.7% while one extra

day coming late decreases the salary by 1% (on average, ceteris paribus)

(c) an increase in sales of 1% increases the salary of a worker by 0.17% while one extra

day coming late decreases the salary by 10% (on average, ceteris paribus)

(d) an increase in sales of 1% increases the salary of a worker by 0.17$ while one extra

day coming late decreases the salary by 0.1$ (on average, ceteris paribus)

(e) an increase in sales of 1% increases the salary of a worker by 1.7$ while one extra

day coming late decreases the salary by 1$ (on average, ceteris paribus)

(f) an increase in sales of 1% increases the salary of a worker by 17$ while one extra

day coming late decreases the salary by 10$ (on average, ceteris paribus)

188. Consider the following definitions regarding data sets:

D1) consists of observations on several variables over time

D2) consists of a time series for each cross-sectional member in the data set.

D3) consists of a sample of individuals, households, firms, cities, states, and etc., taken at a given

point in time

Given the definitions above (D1-D3), indicate a wrong statement:

(a) Cross-sectional data is D3, time-series data is D2, and panel data is D1.

(b) Cross-sectional data is D1, time-series data is D2, and panel data is D3.

(c) Cross-sectional data is D2, time-series data is D3, and panel data is D1.

(d) Cross-sectional data is D2, time-series data is D1, and panel data is D3.

(e) All of the above are correct

(f) None of the above is correct

Correct: cross-sectional data is D3, time-series data is D1, and panel data is D2 .

189. We estimate the model *Wagei *= 70. 75 + 25.53*Educi *+ 23.94*Hoursi *by OLS, where *Wage *is

the monthly of a worker measured in euros, *Educ *measures the number of years of schooling and

*Hours *is the number of hours worked per day. Suppose the R2 of this regression is 0.37, and that the

number of observations was n = 150. What can you conclude?

(a) 55.5% (=0.37*150) of the total variation in wages is explained by the proposed

model

(b) 37% of the total variation in wages is explained by the proposed model

(c) The R2 has to be greater than 1 for the regression to be profitable

(d) b) and c) are correct

(e) None of the above

190. The following estimated model (by OLS) was given, where *return *is the total return of

holding a firm´s stock during one year, *dkr *is the firm’s debt to capital ratio, *eps *denotes earnings

per share, *netinc *denotes net income and salary denotes total compensation, in millions of dollars,

for the CEO (estimated standard errors of the parameters in parentheses below the estimates). The

model was estimated using data on n = 142 firms.

return = -12.3 + 0.32dkr + 0.043eps - 0.005netinc + 0.0035salary

(6.89) (0.150) (0.078) (0.0047) (0.0022)

n=142 R^2=0.0395

What can you say about the coefficient on *dkr *(consider a one-sided alternative for testing

significance of the parameters and use the Normal approximation)

A) It is statistically significant at a 5% level of significance and also significant at 1% level of

significance

B) It is statistically significant a 1% level of significance

C) It is statistically significant at a 1% level of significance but it is not significant at 5% level

of significance

D) It is statistically significant at a 5% level of significance but it is not significant at 1%

level of significance

E) None of the answers is correct

191. The following estimated model (by OLS) was given, where *return *is the total return of

holding a firm´s stock during one year, *dkr *is the firm’s debt to capital ratio, *eps *denotes earnings

per share, *netinc *denotes net income and salary denotes total compensation, in millions of dollars,

for the CEO (estimated standard errors of the parameters in parentheses below the estimates). The

model was estimated using data on n = 142 firms.

return = -12.3 + 0.32dkr + 0.043eps - 0.005netinc + 0.0035salary

(6.89) (0.150) (0.078) (0.0047) (0.0022)

n=142 R^2=0.0395

The model is estimated without including the variables *dkr *and *eps*, and an R2 =0.0387 was

obtained. What is the value of the F-statistic for testing the null hypothesis that the coefficients on

*dkr *and *eps *are both zero?

A) 32.821

B) 0.0570

C) 0.0808

D) We have not enough information to answer this question, we would need to gather more

information from the restricted model

192. The following estimated model (by OLS) was given, where *return *is the total return of

holding a firm´s stock during one year, *dkr *is the firm’s debt to capital ratio, *eps *denotes earnings

per share, *netinc *denotes net income and salary denotes total compensation, in millions of dollars,

for the CEO (estimated standard errors of the parameters in parentheses below the estimates). The

model was estimated using data on n = 142 firms.

return = -12.3 + 0.32dkr + 0.043eps - 0.005netinc + 0.0035salary

(6.89) (0.150) (0.078) (0.0047) (0.0022)

n=142 R^2=0.0395

What can you say about the estimated coefficient of the variable salary? (consider a one-sided

alternative for testing significance of the parameters and use the Normal approximation)

A) For each additional million dollars in the wage of the CEO, return is predicted to

increase by 0.0035, on average, ceteris paribus. But it is not statistically significant at

5% level of significance

B) For each additional million dollars in the wage of the CEO, return is predicted to decrease by

0.0035, on average, ceteris paribus. And it is statistically significant at 5% level of

significance

C) For each additional million dollars in the wage of the CEO, return is predicted to increase by

0.0035, on average, ceteris paribus. And it is statistically significant at 1% level of

significance

D) It is statistically significant at 5% level of significance but it is not significant at 1% level of

significance

193. In the estimated model log(qi) = 2.25 – 0.7 log(pi) + 0.02yi; where *p *is the price and *q *is the

demanded quantity of a certain good and *y *is disposable income, what is the meaning of the

intercept?

when price and disposal income would be zero then demanded quantity for certain good log(Q) will

be 2.25…log(q) olduguna gore bir az cavablarda daha geniw izahat vere bilerler…

194. As the sample size becomes very large, the t distribution

As the sample size becomes very large, the t distribution

a) collapses to a spike because its variance becomes very small

b) collapses to normally-distributed spike

c) approximates more and more closely a normal distribution with mean one

d) approximates more and more closely a standard normal distribution

195. To conduct a t test we need to

A) estimate something that is supposed to be zero and divide it by its mean

B) estimate something that is supposed to be zero and divide it by its variance

C) divide a parameter estimate by its standard error

D) estimate something that is supposed to be zero and see if it is zero

E) All of the above

196. You have obtained the following regression results using data on law students from the class

of 1980 at your university:

Income = 15 + 0.13GPA - 0.17Female + 0.11Married - 0.06Married*Female

where the variables are self-explanatory. Consider married individuals with equal GPAs. Your results

suggest that compared to married person’s income, single person’s income is lower by

0.11 if male, 0.05 if female

197. Suppose the regression specification y = α + βx + ε was estimated as y = 3x +2. We have a

new observation for which x = 5 and y = 17. For this new observation the residual is:

17 = 2 + 3x5 + 0

So, r = 0

198. Which of the following is TRUE?

199. The folowing regression was estimated for the university teachers. Choose the correct

statement:

*Yi = 5 + 40Xi + 11Di + 0.5Mi+ ei*

Yi – weekly salary of the i-th teacher

*Xi *– level of education of the i-th teacher

*Di *– qualitative dummy, it is equal to 1 if the teacher is male and 0 otherwise

*Mi *- qualitative dummy, it is equal to 1 if the teacher is married and 0 otherwise

200. Suppose you have estimated wage = 11 + 2education +5experience + 3gender, where gender

is one for male and zero for female. If gender had been one for female and zero for male, this result

would have been

wage = 14 + 2education +5experience - 3gender

How to build econometric model:

1) Create economic theory.

2) Empirical analysis.

3) Build economic model.

4) Build econometric model.

na vsakiy slucay: :D

Statistical Software

Link | Vendor | Platforms

Shazam | Shazam | PC, Mac, Sun

Stata | Stata Corporation | PC, Mac, Sun

EViews, MicroTSP | Quantitative Micro Software | PC, Mac

SAS | SAS Institute | PC, Mac

TSP | TSP International | PC, Mac

SPSS, Systat,

DeltaGraph | SPSS, Inc. | PC, Mac

RATS | Estima Corporation | PC, Mac

Statistica | StatSoft, Inc. | PC, Mac

GB-Stat (spreadsheet) | Dynamic Microsystems | PC, Mac

GAUSS | Aptech Systems, Inc. | PC, Sun

LIMDEP | Econometric Software | PC

VORSIM(spreadsheet) | Vernon Oley Roningen | PC

SST | U. California Berkeley | PC

Statgraphics | Statistical Graphics

Corporation | PC

NCSS | NCSS, Inc. | PC

Betahat

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