Econometric examination questions
Course code: 00 142
First, multiple-choice questions (25 small questions in this big question, 65438+ 0 points for each small question, 25 points * * *)
Of the four options listed in each question, only one meets the requirements of the topic. Please fill in the code in brackets after the title. Wrong selection, multiple selection or no selection will not be scored.
1. There are two kinds of data in econometric research, one is time series data and the other is ().
A.b. cross-sectional data
C. Average data D. Relative data
2. Econometrics originated from economic problems ().
A.b. Applied research
C. Quantitative research D. Qualitative research
3. The following regression equation is definitely wrong ().
A.B.
C.D.
4.Yi represents the actual observed value and predicted value, so the criterion for estimating parameters by ordinary least square method is ().
A.∑(Yi- 1) 2=0 B.∑(Yi- )2=0。
C.∑(Yi-) 2 minimum D.∑(Yi- )2 minimum.
5. When testing the regression model statistically, it is usually assumed that the random error term ui obeys ().
A.N(0，σ2) B.t(n- 1)
C.N(0,) (if i≠j, then ≦)d. t(n)
6. Given that the determinant coefficient of the univariate linear regression model with two positively correlated variables is 0.64, the linear correlation coefficient between the explanatory variable and the explained variable is ().
A.0.32
C.0.64 D.0.8
7. When using linear regression model for interval prediction, the greater the variance of random error term, ()
A. The wider the prediction interval, the lower the accuracy. B the wider the prediction interval, the smaller the prediction error.
C. The narrower the prediction interval, the higher the accuracy. D the narrower the prediction interval, the greater the prediction error.
8. For the sample regression line obtained by ordinary least square method, the following statement is wrong ().
A.∑ei=0 B.∑ei≠0
C.∑eiXi = 0d .∑Yi =√
9. The following methods are not used to test heteroscedasticity ()
A. Ann-Ramsey test
C.Goriser test D. Variance expansion factor test
10. If the variance of the random error term of the linear regression model is proportional to a variable Zi, which of the following methods should be used to estimate the parameters of the model? ( )
A. Ordinary least square method B. Weighted least square method
C. indirect least square method D. instrumental variable method
1 1. If the first-order autocorrelation coefficient of the residuals of a linear regression model is equal to 0.3, the DW statistic is equal to ().
A.0.3
C. 1
12. If DL
A. the random error term has a first-order positive autocorrelation B. The random error term has a first-order negative autocorrelation.
C. There is no first-order autocorrelation in the random error term D, so it is impossible to judge whether there is first-order autocorrelation in the random error term.
13. Remember that ρ is the first-order autocorrelation coefficient of the random error term of the regression equation, and the first-order difference method is mainly applicable to ().
A.ρ≈0 B.ρ≈ 1
c .ρ& gt； 0d .ρ& lt； 0
14. The formula for calculating variance expansion factor is ()
A.B.
C.D.
15. in the finite distribution lag model yt = 0.5+0.6xt-0.8xt-1+0.3xt-2+ut, the short-term impact multiplier is ().
A.0.3
C.0.6 D.0.8
16. For an infinitely distributed lag model, if the signs of model parameters are the same and the parameters decay in geometric order, the model can be transformed into ().
A. Keuk transform model B. Adaptive expectation model
C. Partial adjustment model D. Finite polynomial lag model
17. In the simultaneous equation model, the order condition of identification is ().
A. Sufficient conditions B. Necessary and sufficient conditions
C. Necessary conditions D. Equivalence conditions
18. In the simplified model, all explanatory variables are ().
A.b. endogenous variables
C. Lagging variable D. Predetermined variable
19. For the system equation in the simultaneous equation model, the following statement is correct ().
A. Completely identifiable
C. excessive cognition D. there is no cognitive problem.
20. If the self-price elasticity coefficient of commodity demand >; 0, the commodity is ()
A. Normal commodities B. Abnormal commodities
C. High-grade goods D. Inferior goods
2 1. If the income elasticity coefficient of a commodity demand is 0.
A. Necessities B. High-end goods
C. Inferior goods are Jia Xu goods
22. Let the production function be Y=f(L, k), and for any >; L, if f( L, k) >; F(L, k), the production function is called ().
A. the return on scale is greater than B. the return on scale is increasing.
C. diminishing returns to scale D. Scale return is less than
23. If the self-price elasticity of a commodity demand = 1, then ()
A. Demand is elastic
C. Elasticity of unit demand
24. In the following models for various purposes, the one that pays special attention to the goodness of fit is ().
A. Economic forecasting model B. Structural analysis model
C. Policy analysis model D. Specialized model
25. If △Yt is a stationary time series, Yt is ().
A.0-order simple integer B. 1 order simple integer
C.2-order simple integral D. cointegration
Second, multiple-choice questions (this big topic ***5 small questions, each small question 2 points, *** 10 points)
Of the five options listed in each question, two to five meet the requirements of the topic. Please fill in the code in the brackets after the title. Wrong selection, multiple selection, less selection or no selection do not score.
26. The following phenomenon is irrelevant ()
A. Residents' consumption expenditure and income B. Commodity sales and sales volume, sales price
C. Price level and commodity demand D. Wheat yield and fertilizer application amount
E. Total score and scores of various courses
27. The common methods to deal with multiple * * * linearity are ().
A. adding sample information B. using non-sample prior information
C. transformation in variable form D. ridge regression estimation method
E. principal component regression estimation method
28. In the regression analysis of consumption (y) and income (x), the following regression equation may be correct ().
A.Y= + X+u B.Y= + D+ X+u
C.Y= + + +u D. Y= + (DX)+u
E.Y= + D+ X+ +u
29. According to the different sample data and analysis period, the macroeconomic econometric model is divided into ().
A. Monthly model B. Quarterly model
C. Semi-annual model D. Annual model
E. Medium and long-term model
30. The following tests belong to the econometric criteria test ().
A. Determination coefficient test B. Sequence correlation test
C. Heterogeneity test of variance D. Multiple linear test
E. identification and judgment of simultaneous equation model
Third, the noun explanation question (this big question ***5 small questions, 3 points for each small question, *** 15 points)
3 1. Econometric analysis
32. Overall design of macroeconomic econometric model
33. Interval prediction
34. Stationary time series
Engel's law
Four, short answer questions (this big question ***4 small questions, each small question 5 points, ***20 points)
36. Briefly describe the difference between regression analysis and correlation analysis.
37. What problems can finite polynomial lag model solve?
38. Briefly describe the definition and types of identity.
39. Compared with the single demand equation model, what are the advantages of the demand equation system model?
V. Calculation problem (this big problem is ***2 small problems, each small problem is 10, and ***20 points)
40. According to the historical data of annual per capita savings (Y) and annual per capita income (X) in a certain area 10, it is calculated that:
∑Xi=293, ∑ Yi =8 1, ∑(Xi-) (Yi-)=200.7, ∑(Xi- )2=992. 1, ∑ (Yi-)2=44.9.
Q:
(1) The linear regression equation of per capita annual savings (y) and per capita annual income (x);
(2) Determinant coefficient of regression equation.
4 1. According to the cross-sectional data of sales volume (y) and sales price (x) of a commodity in 26 cities, the sales volume (y) and sales price (x) are linearly regressed. In order to test whether there is heteroscedasticity in the random error term of the regression model, 26 pairs of observed values are sorted according to the sales price (X). According to the observed value 13, the sales volume (y) linearly regressed with respect to the sales price (x), and the sum of squares of regression residuals was RSS 1= 1536.8. According to the observed value 13, the sales volume (y) is linearly regressed with respect to the sales price (x), and the sum of squares of regression residuals is RSS2=377. 17.
(1) Try to judge whether there is heteroscedasticity in the random error term of the regression model at the significance level of 5%.
(F0.05( 1 1， 1I)=2.82，F0.05( 12， 12)=2.69)
(2) If the random error term of the regression model is heteroscedasticity, what effect will it have on the linear regression analysis?
Six, analysis questions (this big question ***l small question, 10)
42. In the linear regression analysis of the demand Q and price P of a certain beverage, the influences of "regional" factors and "seasonal" factors are comprehensively considered as follows:
(1) "regional" (rural, urban) factors affect its intercept;
(2) The "season" (spring, summer, autumn and winter) factors affect its intercept and slope.
Try to analyze and determine the linear regression model of this kind of beverage demand