Online econometrics tests solve tas. Econometrics test (beginner level)
For a long time, there were two different options definitions of econometrics: from “econometrics in the broad sense of the word” to “econometrics in the narrow sense of the word”. "Econometrics in the broadest sense of the word" refers to the totality of various kinds of economic research carried out using mathematical methods. “Econometrics in the narrow sense of the word” refers mainly to the use of mathematical and statistical methods in economic research: the construction of mathematical and statistical models of economic phenomena, the estimation of parameters in models of any type, etc.
The name "econometrics" was introduced by the founder of this direction in economics in 1926, Ragnar Frisch. Linguistically, the term "econometrics" is of German origin (Okonometrie). For the first time this term appeared in 1910 in a German book on accounting, the author of which understood by it the theory of accounting. Literally translated, econometrics means “measurements in economics” (can be compared with biometrics, scientometrics, astrometrics, sociometrics, psychometrics, political metrics).
However, it is currently possible with complete confidence claim that the definition given by S.A. Ayvazyan and V.S. Mkhitaryan in their latest textbook is the most objective, modern and accurate:
Definition: Econometrics is an independent scientific discipline that combines a set of theoretical results, techniques, methods and models designed to
- economic theory,
- economic statistics,
- mathematical and statistical tools
- to give a specific quantitative expression to the general (quantitative) laws determined by economic theory.
As you can see, this definition is fully consistent with the one introduced by R. Frisch seventy years ago. He believed that econometrics should follow the triune formula, combining theoretical analysis, empirical data and mathematical methods.
Speaking about economic theory within the framework of econometrics, researchers are interested not only in identifying objectively existing (at a qualitative level) economic laws and links between economic indicators, but also in approaches to their formalization. When considering economic statistics as an integral part of econometrics, researchers are only interested in that aspect of this independent discipline that is directly related to information support analyzed econometric model. And, finally, under the mathematical and statistical tools of econometrics is meant, of course, not mathematical statistics in its traditional sense, but only its individual sections (classical and generalized linear models of regression analysis, time series analysis, construction and analysis of systems of simultaneous equations). These sections of mathematical statistics should be supplemented with some information (special types of regression models, approaches to solving problems of specification, identifiability and verifiability of models, etc.).
In all the activities of an econometrician, the use of a model is essential. Therefore, it is very important to trace the entire “chain” of definitions related to this concept.
Mathematical model Is an abstraction of the real world in which the relations between real elements of interest to the researcher are replaced by suitable relations between mathematical categories.
Economic and mathematical model - is any mathematical model that describes the mechanism of functioning of a certain hypothetical economic system or socio-economic system. Sometimes the same model can be called simply economic ... (An example of such a model is the simplest version of the so-called “spider-web model”, which describes the process of formation of demand and supply of a certain product or type of service in a competitive market).
If the definition of an economic and mathematical model is not about any mathematical model, but about a model built using the apparatus of the theory of probability and mathematical statistics, then one can already get an idea of the econometric model. But for this, the following definitions should be remembered.
Probability model - it is a mathematical model that simulates the mechanism of functioning hypothetical(not specific) real phenomenon (or system) of stochastic nature.
Probabilistic statistical model - it is a probabilistic model, the values of individual characteristics (parameters) of which are estimated according to the results of observations (initial statistical data) characterizing the functioning of the modeled specific(and not hypothetical) phenomenon (or system).
Finally, we can talk about the econometric model:
Econometric model is called a probabilistic-statistical model that describes the mechanism of functioning of an economic or socio-economic system.
In any econometric model, all variables involved in it, depending on the final applied goals, are subdivided into exogenous, endogenous and predetermined:
exogenous variables(ekzo-outside, genous-origin)- these are variables that are set, as it were, "from the outside", autonomously, and to a certain extent are controllable (planned);
endogenous variables(endo-inside, genous-origin) are variables whose values are formed in the process and inside the functioning of the analyzed socio-economic system to a significant extent under the influence of exogenous variables and, of course, in interaction with each other; in the econometric model, they are the subject of explanation;
predefined variables Are variables that act in the system as factors - arguments, or explaining variables.
A set of predefined variables is formed from all exogenous variables (which can be “tied” to the past, current and future points in time) and the so-called lagged endogenous variables, those. of such endogenous variables, the values of which are included in the equations of the analyzed econometric system measured in past(with respect to the current) moments of time, and therefore are already known, given.
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Econometrics tests
Tests in econometrics, for testing knowledge in the section "Problems with Macroeconomic Models". 10 test questions - correct options, highlighted in red. 1. Below is the macroeconomic model Given: Consumption function: Ct = a0 + a1Yt + a2Yt-1 + u1 Investment function: It = b0 + b1Yt + u2 Income identity: Yt = Ct + It + Gt, where Ct is the final consumption in period t; Yt, Yt-1 - income in years [...]
Tests in econometrics, for testing knowledge in the section "Systems of Simultaneous Equations". 9 test questions - correct options, highlighted in red. 1. The system of simultaneous equations can be written in the form of: structural form of functional form of reduced form of generalized form 2. A set of interrelated regression models in which the same variables can simultaneously be endogenous in some [...]
Tests in econometrics, for testing knowledge in the "Time series" section. 17 test questions - correct options, highlighted in red. 1. The trend (Trend) of the time series characterizes the totality of factors that have a long-term influence and form the general dynamics of the studied indicator, which have a seasonal effect, have a one-time effect that do not affect the level of the series 2. Smoothly changing component of the time series, reflecting [...]
Tests in econometrics, for testing knowledge in the section "Assessing the quality of a regression model." 41 test questions - correct options are highlighted in red. 1. The tightness of the statistical connection between the variable y and the explanatory variables X is measured by: Student's t-criterion, the coefficient of determination, the correlation coefficient, Fisher's F-criterion 2. The coefficient of paired linear correlation characterizes: the tightness of the linear relationship between the two variables,
Tests in econometrics, for testing knowledge in the section "Nonlinear Regression Models". 8 test questions - correct options, highlighted in red. 1. Nonlinear regression equation is nonlinear with respect to the variables (factors) included in it, the results of parameters of random variables 2. An example of nonlinear dependence economic indicators is the classic hyperbolic dependence of demand on the price linear dependence of revenue on the amount of working capital [...]
Tests in econometrics, for testing knowledge in the section "Model of linear multiple regression". 4 test questions - correct options, highlighted in red. 1. The linear multiple regression equation between the dependent variable Y and the independent variable X, where a, b are model parameters, can be as follows: Y = a + bX Y = a + bX2 Y = a + b1X1 + b2X2 Y = bX 2. Equation linear multiple regression between dependent [...]
Tests on econometrics, for testing knowledge in the section "Examples of linear paranoid regression". 5 test questions - correct options, highlighted in red. 1. An example of a linear dependence of economic indicators is the classic hyperbolic dependence of demand on price, the dependence of a worker's wages on his output with piecework wages, the dependence of sales volume on the week of implementation. 2. An example of a linear dependence of economic [...]
Tests in econometrics, for testing knowledge in the section "Linear Pair Regression". 4 test questions - correct options, highlighted in red. 1. The linear pairwise regression equation between the dependent variable Y and the independent variable X, where a, b are the model parameters, can be as follows: Y = a + bX Y = a + bX2 Y = a + b1X1 + b2X2 2. Linear pair regression equation between the dependent variable Y and [...]
Econometrics Tests - Page # 1/1
Econometrics tests
Introduction
The econometric model is
y = fx
y = a + b1x + b2x2
y = fx + ε
y = fx
Set correspondence
Answer: a-3, b-2, c-4
Regression is
dependence of the values of the resultant variable on the values of the explanatory variables (factors)
the rule according to which each value of one variable is associated with a single value of another variable
the rule according to which each value of the independent variable is associated with the value of the dependent variable
dependence of the mean value of the effective variable on the values of the explanatory variables (factors)
Least square method …
Allows to obtain estimates of the parameters of linear regression, based on the condition i = 1nyi-yi2 → min
Allows to obtain estimates of the regression parameters based on the condition ln (i = 1nf (yi, ) → max
Allows you to check the statistical significance of the regression parameters
Allows to obtain estimates of nonlinear regression parameters based on the condition i = 1ny-yi2 → min
Linear multiple regression
Linear multiple regression equation
y = a + bx
y = a + b1x1 + b2x2 +… + bpxp
y = ax1b1x2b2 ... xpbp
yt = Tt + St + Et
For a linear multiple regression equation, match
Answer: a-4, b-1, c-6, d-5
Regression model specification problem includes
Selection of factors included in the regression equation
Estimating the parameters of the regression equation
Evaluation of the reliability of the results of regression analysis
Choosing the type of regression equation
1. Requirements for factors included in the linear multiple regression model ...
The number of factors should be 6 times less than the volume of the population
Factors should represent time series
Factors must be of the same dimension
There shouldn't be a high correlation between the factors.
2 true statements about multicollinearity of factors
It is recommended to include multicollinear factors in the linear multiple regression model
The multicollinearity of factors leads to a decrease in the reliability of estimates of the parameters of the regression equation
The multicollinearity of factors is manifested in the presence of paired interfactor correlation coefficients with values greater than 0.7
The multicollinearity of factors is manifested in the presence of paired interfactor correlation coefficients with values less than 0.3
3.Correct statements about the inclusion of factors in the linear multiple regression equation
The inclusion of a factor in the model leads to a noticeable increase in the multiple determination coefficient
The pair correlation coefficient for the factor and the resultant variable is less than 0.3
The value of the Student's t-test for the regression coefficient when the factor is less than the table value
The factor should explain the behavior of the studied indicator according to the accepted provisions of economic theory
4. When constructing a multiple regression model by the method of step-by-step inclusion of variables, at the first stage, a model with ...
One explanatory variable that has the lowest correlation coefficient with the dependent variable
One explanatory variable that has the highest correlation coefficient with the dependent variable
Several explanatory variables that have correlation coefficients in modulus greater than 0.5 with the dependent variable
Complete list of explanatory variables
Parameters for factors in linear multiple regression
y = a + b1x1 + b2x2 + ... + bpxp characterize
The proportion of the variance of the outcome variable explained by the regression in its total variance
The tightness of the relationship between the resultant variable and the corresponding factor, while eliminating the influence of other factors included in the model
By what percentage on average does the effective variable change with a change in the corresponding factor by 1%
5.Standardization of variables is carried out according to the formula
ty = ymaxy
ty = y-y
ty = yσy
ty = y-yσy
The multiple regression equation on a standardized scale is ty = 20 + 0.9tx1 + 0.5tx2 + ε. The effective trait is greatly influenced by:
x1 and x2
no conclusion can be drawn
The multiple regression equation in natural form is
y = 20 + 0.7x1 + 0.5x2 + ε. The effective trait is greatly influenced by:
x1 and x2
no conclusion can be drawn
6.The properties of the regression equation in a standardized form include ...
Regression coefficients for explanatory variables are equal to each other
There is no constant parameter (intercept) of the regression
Standardized regression coefficients are incomparable
The variables included in the equation are dimensionless
7.The tightness of the joint influence of factors on the result in the linear multiple regression equation is estimated by
Pair correlation coefficient
Partial correlation coefficient
8.Set the match
Answer: a-1, b-4, c-3
9. The coefficient of multiple correlation for a linear relationship can be calculated by the formula
Answer: a, d
10 correct statements about the multiple correlation coefficient
The closer the value is to one Ryx1 ... xp, the closer the relationship of the effective trait with all factors
The closer the value to zero Ryx1 ... xp, the closer the relationship of the effective trait with all factors
Ryx1 ... xp takes values from the interval
Ryx1 ... xp takes values from the interval [- 1, 1]
11. The coefficient of multiple determination characterizes
The tightness of the joint influence of factors on the result in the equation of linear multiple regression
The tightness of the relationship between the result and the corresponding factor, while eliminating the influence of other factors included in the model
The proportion of variance of an effective trait explained by regression in its total variance
The average change in the effective variable with a change in the corresponding factor by one, with the same value of other factors fixed at the average level
12. For the total (TSS), regression (RSS) and residual (ESS) sum of squared deviations and the coefficient of determination R2, the equality is fulfilled ...
R2 = RSSTSS
R2 = 1-ESSTSS
R2 = ESSTSS
R2 = 1-RSSTSS
R2 = RSSTSS + ESSTSS
13. The ratio of residual variance to total variance is 0.05. It means …
Determination coefficient R2 = 0.95
Determination coefficient R2 = 0.05
Difference (1-R2) = 0.95, where R2 is the coefficient of determination
Difference (1-R2) = 0.05, where R2 is the coefficient of determination
14.To eliminate the systematic error of the residual variance, to assess the quality of the linear multiple regression model, we use
Multiple determination coefficient
Multiple correlation coefficient
Adjusted multiple determination coefficient
Adjusted partial correlation coefficient
15. The estimation of the statistical significance of the linear multiple regression equation as a whole is carried out using
Student's criterion
Fisher's criterion
Darbin-Watson criterion
Foster-Stewart criterion
16. Evaluation of the statistical significance of the coefficients of linear multiple regression is carried out using
Student's criterion
Fisher's criterion
Darbin-Watson criterion
Foster-Stewart criterion
17. If the regression coefficient is significant, then the conditions
The actual value of the Student's t-test is less than the critical
The actual value of the Student's t-test is greater than the critical
Confidence interval passes through zero
Standard error is less than half of the parameter value
18. If the regression equation is significant, then the actual value of the F-criterion ...
more critical
less critical
close to one
close to zero
19. The prerequisites for MNCs are ...
The variance of random deviations is constant for all observations
The variance of random deviations is not constant for all observations
Random deviations correlate with each other
Random deviations are independent of each other
20. Indicate findings that fit the residuals plot
The OLS premise of the independence of the residuals from each other is violated
Autocorrelation of residuals takes place
There is no pattern in the behavior of the residuals
No autocorrelation of residuals
21. When the premises of the least squares method (OLS) are fulfilled, the residuals of the regression equation are usually characterized by ...
Zero average
Heteroskedstichnost
Random nature
High degree of autocorrelation
22. Methods for detecting heteroscedasticity of residues include
Darbin-Watson criterion
Goldfeld-Quandt test
Graphical analysis of residues
Least square method
23 The dummy variables in the multiple regression equation are ...
Qualitative variables converted to quantitative
Variables representing the simplest functions of variables already included in the model
Additional quantitative variables that improve the solution
Combinations of factors included in the regression equation that increase the adequacy of the model
24. To reflect the influence of a qualitative companion variable having m states, usually include in the model ... a dummy variable
m + 12
m-12
Nonlinear regression
25 Regressions that are nonlinear in the explanatory variables, but linear in the estimated parameters
y = a + b1x + b2x2 + ε
y = a ∙ xb ∙ ε
y = a + bx + ε
y = a + bx + ε
y = a ∙ bx ∙ ε
y = ea + bx ∙ ε
26 Regression, nonlinear in the estimated parameters
y = a + b1x + b2x2 + ε
y = a ∙ xb ∙ ε
y = a + bx + ε
y = a + bx + ε
y = a ∙ bx ∙ ε
y = ea + bx ∙ ε
27. Make the correct statements about the model.
y = fx, z ∙ ε = a ∙ bx ∙ cz ∙ ε
Refers to the type of models that are non-linear in explanatory variables, but linear in the estimated parameters
Refers to the type of models that are non-linear in the parameters being estimated.
Refers to the type of linear models
Cannot be reduced to linear form
Can be reduced to linear form
28. Make the correct statements about the model
Linear multiple regression model is linearized
Linearized pairwise regression model
Belongs to the class of nonlinear models in explanatory variables, but linear in the estimated parameters
Belongs to the class of linear models
29. The model y = a ∙ bx ∙ ε belongs to the class of ... econometric models of nonlinear regression
power-law
reverse
exemplary
linear
30. The model y = a ∙ xb ∙ ε belongs to the class of ... econometric models of nonlinear regression
power-law
reverse
exemplary
linear
31. The model y = a + bx + cx2 + ε belongs to the class ... econometric models of nonlinear regression
power-law
polynomial
exemplary
linear
32. It was noticed that with an increase in the amount of applied fertilizers, the yield also increases, however, upon reaching a certain value of the factor, the modeled indicator begins to decrease. To study this relationship, you can use the specification of the regression equation ...
y = a + bx + cx2 + ε
y = a + b1x1 + b2x2 + ε
y = a + bx + ε
y = a + xb + ε
33. To obtain estimates of the parameters of the power regression model y = a ∙ xb ...
Least squares not applicable
It is required to find the appropriate substitution
You need to perform a logarithmic transformation
Trigonometric transformation required
34. Using the least squares method, it is impossible to estimate the values of the parameters of the regression equation ...
y = a + bx + ε
y = a + bxc + ε
y = a + bx + cx2 + ε
y = a + b1x1 + b2x2 + ε
Time series analysis
35. Under the change that determines the general direction of development, the main trend of the time series, is understood ...
Trend
Seasonal component
Cyclic component
Random component
36.The regular components of the time series are
Trend
Seasonal component
Cyclic component
Random component
37. If the period of cyclical fluctuations in the levels of the time series does not exceed one year, then they are called ...
Yearly
Opportunistic
Seasonal
Perennial
38. Let Yt be a time series, Tt a trend component, St a seasonal component, Et a random component. The additive model of the time series looks like ...
Yt = Tt + St + Et
Yt = Tt ∙ St + Et
Yt = Tt + St ∙ Et
Yt = Tt ∙ St ∙ Et
39. Let Yt be a time series, Tt a trend component, St a seasonal component, Et a random component. The multiplicative model of the time series has the form ...
Yt = Tt + St + Et
Yt = Tt ∙ St + Et
Yt = Tt + St ∙ Et
Yt = Tt ∙ St ∙ Et
40. An additive model of a time series was built, where Yt is a time series, Tt is a trend component, St is a seasonal component, Et is a random component. If Yt = 15, then the values of the components of the series are correctly found ...
Tt = 8, St = 5, Et = 0
Tt = 8, St = 5, Et = 2
Tt = 15, St = 5, Et = 0
Tt = 15, St = -5, Et = 2
41. You can determine the presence of a trend in a time series ...
Time series plot
By the volume of the time series
By the absence of a random component
Using a statistical test of the trend hypothesis
42. It is possible to determine the presence of cyclical (seasonal) fluctuations in the time series ...
As a result of the analysis of the autocorrelation function
Time series plot
By the volume of the time series
Using the Foster-Stewart criterion
43. Let Yt be a time series with quarterly observations and St an additive seasonal component. The estimates of the seasonal component for the first, second and fourth quarters are S1 = 5, S2 = -1, S4 = 2, respectively. The estimate of the seasonal component for the third quarter is ...
44. As a result of smoothing the time series 6, 2, 7, 5, 12 of a simple three-term moving average, the first smoothed value is equal to ...
45. As a result of smoothing the time series 6, 2, 7, 5, 12 of a simple four-term moving average, the first smoothed value is equal to ...
46. The growth curve with saturation is used to describe the trend of the time series ...
y = a + b1t + b2t2
y = a + b1t + b2t2 + b3t3
y = a ∙ bt, b> 1
y = k + a ∙ bt, a
47 First order autocorrelation coefficient
Partial correlation coefficient between adjacent levels of the time series
Linear coefficient of pair correlation between arbitrary levels of the time series
Linear coefficient of pair correlation between adjacent levels of the time series
Linear coefficient of pairwise correlation between the level of the time series and its number
48. Autocorrelation function ...
Dependence of the autocorrelation coefficient on the first differences in the levels of the time series
Dependence of the time series level on the correlation coefficient with its number
A sequence of autocorrelation coefficients arranged in ascending order of magnitude
A sequence of autocorrelation coefficients arranged in ascending order of their values
49. If the autocorrelation coefficient of the 4th order turned out to be the highest, then the time series has
linear trend
random component
4th order polynomial trend
cyclical fluctuations with a period of 4
50 The values of the autocorrelation coefficients are known r1 = 0.8, r2 = 0.2, r3 = 0.3, r4 = 0.9. Please state the correct statements ...
The time series contains a trend in the form of a polynomial of the 4th order
51. The values of the autocorrelation coefficients are known r1 = 0.1, r2 = 0.8, r3 = 0.3, r4 = 0.9. We can conclude ...
Time series contains a linear trend
Time series is random
The time series contains cyclical fluctuations with a period of 2
The time series contains cyclical fluctuations with a period of 4
52. A time series model is considered adequate if the residual values ...
have zero mathematical expectation
the actual value of the F-criterion is less than the table
obey the normal distribution law
obey a uniform distribution law
positive
are casual and independent
53. The independence of the residuals of the time series model can be checked using
Darbin-Watson criterion
Pearson criterion
Fisher's criterion
54 The randomness of the residuals of the time series model can be checked using
Analysis of the autocorrelation function of residuals
Pearson criterion
Testing the hypothesis about the presence of a trend
Calculation of asymmetry and kurtosis
55 For exponential smoothing, use the formula
St = αyt + 1-αyt-1
St = αyt + 1-αSt-1
yt = k + a ∙ bt, a
Yt = Tt + St + Et
56.The smoothing constant α in the exponential smoothing model St = αyt + 1-αSt-1 takes the values
0.2 or 0.3
from 0.7 to 0.9
arbitrary
57. The choice of the optimal value of the smoothing constant α in the exponential smoothing model St = αyt + 1-αSt-1 is carried out
The value α = 0.3 is always used
The value α = 0.7 is always used.
The optimal value of α is considered, at which the smallest variance of the error is obtained
The optimal value of α is considered, at which the largest error variance is obtained
58. Adaptation parameter α = 0.3, y5 = 8, y6 = 7, S4 = 6. The S6 value obtained as a result of exponential smoothing of the time series according to the formula St = αyt + 1-αSt-1 is ...
Answer: 6.72
59.The time series contains a trend and the Holt model is used to smooth it: St = αyt + 1-α (St-1-mt-1), mt = γSt-St-1 + 1-γmt-1. If α = γ = 0.3, y5 = 8, S4 = 5, m4 = 2. The m5 value is ...
Answer: 1.25
Systems of Simultaneous Equations
The agricultural enterprise is engaged in the cultivation of wheat, corn, barley, buckwheat. An econometric model has been built that describes the yield of each crop depending on the applied doses of fertilizers and the amount of moisture. This model belongs to the class of systems ... equations
simultaneous
independent
recursive
normal
The state of a closed economy is described by the following characteristics: Y is the gross domestic product (GDP), C is the level of consumption, I is the amount of investment, G is government spending, T is the amount of taxes, R is the real interest rate. The specification of the model is based on the following provisions of economic theory: 1) consumption is explained by the amount of disposable income (Y-T); 2) the level of investment is determined by the size of GDP and the interest rate; 3) consumption, investment and government spending in the amount equal to GDP. The corresponding system of interrelated equations will look like:
C = a0 + a1 ∙ Y + ε1, I = b0 + b1 ∙ Y + b2 ∙ R + ε2, Y = C + I + G
C = a0 + a1 ∙ Y-T + ε1, I = b0 + b1 ∙ Y + ε2, Y = C + I + G
C = a0 + a1 ∙ Y-T + ε1, I = b0 + b1 ∙ Y + b2 ∙ R + ε2, Y = c0 + c1 ∙ C + c2 ∙ I + c3 ∙ G + ε3
C = a0 + a1 ∙ Y-T + ε1, I = b0 + b1 ∙ Y + b2 ∙ R + ε2, Y = C + I + G
In the structural form of the model, built according to the indicated scheme of relationships between variables, the number of exogenous variables is equal to ...
Answer: 2
In the structural form of the model, built according to the indicated scheme of relationships between variables, the number of endogenous variables is equal to ...
Answer: 3
In the system of simultaneous equations, the endogenous variables are
In the system of simultaneous equations, the exogenous variables are
The number of equations of the system for the specified scheme of relationships between variables is equal to ...
Answer: 2
60. The number of equations of the system for the specified scheme of relationships between variables is equal to ...
Answer: 3
61. The number of equations of the system for the specified scheme of relationships between variables is ...
Answer: 3
Equations that need to be included in the system for the specified scheme of relationships between variables
Y1 = b12Y2 + a11X1 + a12X2 + ε1
Y2 = b21Y1 + a21X1 + a22X2 + ε2
Y1 = a11X1 + a12X2 + ε1
Y2 = a21X1 + a22X2 + ε2
Y1 = b12Y2 + a11X1 + ε1
Y2 = b21Y1 + a21X1 + ε2
The reduced form of the model corresponding to the structural form of the system of simultaneous equations
includes equations
y1 = a11x1 + ε1
y2 = a22x2 + ε2
y1 = δ11x1 + u1
y2 = δ22x2 + u2
y1 = δ11x1 + δ12x2 + u1
y2 = δ21x1 + δ22x2 + u2
The given form of the model is the result of transformation ...
Nonlinear Regression Equations
The structural form of the model
Systems of independent equations
Systems of recursive equations
62. The reduced form for the model of price dynamics and wages
y2 - rate of price change,
x1 - percentage of unemployed,
x3 is the rate of change in prices for imports of raw materials,
looks like ...
y1 = δ11x1 + ε1, y2 = δ22x2 + δ23x3 + ε2
y1 = δ12y2 + δ11x1 + ε1, y2 = δ21y1 + δ22x2 + δ23x3 + ε2
y1 = δ12y2 + ε1, y2 = δ21y1 + ε2
y1 = δ11x1 + δ12x2 + δ13x3 + ε1, y2 = δ21x1 + δ22x2 + δ23x3 + ε2
63. The uniqueness of the correspondence between the reduced and structural forms of the model of a system of simultaneous equations is a problem ...
multicollinearity factors
identification
heteroscedasticity of residues
heterogeneity of data
64. Establish a correspondence between the type of the structural model and the correspondence of the structural and reduced coefficients ...
Answer: a-3, b-1, c-2
65. Using the necessary identification condition for the model of price and wage dynamics, indicate the correct statements ...
y1 = b12y2 + a11x1 + ε1, y2 = b21y1 + a22x2 + a23x3 + ε2,
where y1 is the rate of change in the monthly salary,
y2 - rate of price change,
x1 - percentage of unemployed,
x2 - rate of change of constant capital,
x3 - rate of change in prices for import of raw materials
both equations are exactly identifiable
both equations are not identifiable
both equations are overly identifiable
the first equation is overly identifiable
the second equation are exactly identifiable
66. Let D be the number of exogenous variables that are contained in the system, but are not contained in this equation. For the first equation of the model of price and wage dynamics, the value of D is equal to ...
y1 = b12y2 + a11x1 + ε1, y2 = b21y1 + a22x2 + a23x3 + ε2,
Answer: 2
67. Let D be the number of exogenous variables that are contained in the system, but are not contained in this equation. For the second equation of the model of price and wage dynamics, the value of D is equal to ...
y1 = b12y2 + a11x1 + ε1, y2 = b21y1 + a22x2 + a23x3 + ε2,
68. Let H be the number of endogenous variables in the system, D - the number of exogenous variables that are contained in the system, but are not contained in this equation. For the first equation of the model of price and wage dynamics, the value (H - D) is ...
y1 = b12y2 + a11x1 + ε1, y2 = b21y1 + a22x2 + a23x3 + ε2,
Answer: 0
69. Match the counting rule necessary condition identification, if H is the number of endogenous variables in the system, D is the number of exogenous variables that are contained in the system, but are not contained in this equation
|
a) the equation is identifiable |
1) D + 1 |
|
|
2) D + 1 = H |
|
3) D + 1> H |
Answer: a-2, b-3
70. Establish a correspondence for the counting rule of the necessary identification condition, if H is the number of endogenous variables in the system, D is the number of exogenous variables that are contained in the system, but are not contained in this equation
|
a) the equation is not identifiable |
1) D + 1 |
|
b) the equation is over identifiable |
2) D + 1 = H |
|
3) D + 1> H |
Answer: a-1, b-3
71. Ordinary OLS has been successfully applied to assess structural coefficients ...
Systems of unidentifiable equations
Systems of recursive equations (triangular models)
Systems of interrelated or simultaneous equations
Systems of Equations-Identities
Systems of independent equations
72. For an identifiable structural form of a system of simultaneous equations, when evaluating parameters, it is used ...
73. For an overidentifiable structural form of a system of simultaneous equations, when evaluating parameters, it is used ...
Ordinary least squares method
Indirect least squares
Two-step least squares method
Three-step least squares method
1. which of the regression equations is a power law
Y= A? A?? A
2.the estimates of the regression parameters are unbiased if
The mathematical expectation of the residuals is 0
3.The estimates of the regression parameters are effective if
Estimates have the least variance ………….
(4) the estimates of the regression parameters are consistent if
Increase. accuracy….
5. dummy variables are
Attributive signs….
6.if the quality factor has 3 grades, then the required number of dummy variables
7.correlation coefficient, equal to zero, means that between the variables
Situation not defined
8.correlation coefficient equal to -1 means that between the variables
Functional dependence
9.the econometric analysis of Xj considers
As random variables
10.the regression coefficient varies within
Accepts any value
11.Q = ……… ..min corresponds to
Least squares
12.In what limits does the coefficient of determination change?
13.In a well-fitted model, the residuals should
Have a normal law ... ..
14.the wrong choice of functional form or explanatory variables is called
Specification errors
15.the coefficient of determination is
Doubles square ...
16.value calculated by the formula r = ……………… is an estimate
Paired Correlation Coefficient
17.Sample correlation coefficient r in absolute value
Does not exceed one
18.components of the vector Ei
Have a normal law
19. can we apply the least squares method for calculating the parameters of non-linear models?
We will apply after it ... ..
20.Is the least squares method applicable for calculating the parameters of the exponential dependence?
We will apply after its reduction
21. what the absolute growth rate shows
How many units will y change if x has changed by one
22. if the correlation coefficient is positive, then in the linear model
As x grows, y increases
23. what function is used when modeling models with constant growth
If the relative value is …………………… unlimited
25. elasticity shows
How much% will change …………………………… .. by 1%
26. the student's table value depends on
And on the level of confidence, and on the number of factors included in the model and on the length of the original series
27. the table value of the phisher criterion depends on
Only on the level of confidence and on the number of factors included in the model
28. what is the statistical characteristic expressed by the formula
Rxy = …………
Correlation coefficient
29. formula t = rxy …………. Is used for
Materiality Checks Correlation Coefficient
30. What statistical characteristic is expressed by the formula R? = ……………
Determination coefficient
31. The correlation coefficient is used for
Determination of the tightness of communication .....................
32. elasticity measured
Unit of measurement of the factor ………………… indicator
33. Estimates of paired linear regression parameters are found by the formula
B = Cov (x; y) / Var (x); a = y? bx?
34. for the regression y = a + bx from n observations, the confidence interval (1-a)% for the coefficient b will be
35. suppose that the dependence of expenses on income is described by the function y = a + bx
Average value y = 2 ………………. Equals
36. for paired regression o? B equals
……. (Xi-x?)?)
37. The relationship between the coefficient of multiple determination (D) and correlation (R) is described by the following method
38. Confidence probability
The probability that ……………… ..predicted interval
39. to test the significance of a particular parameter, use
40. number of degrees of freedom for t statistics when checking the significance of regression parameters from 35 observations and 3 independent variables
41. number of degrees of freedom of denominators of f regression statistics from 50 observations and 4 independent variables
42. one of the problems of the cat. May occur in multivariate regression and never in paired regression, is
Correlation between explanatory variables
43 multicollinearity occurs when
Two or more independent ... ... ... ...
44.heteroscedaticity is present when
Dispersion of random….
45.The standardized coefficient of the Regression equation? K shows
How many% will the resulting indicator y change when xi changes by 1% with the same average level of other factors
46. relationship between multiple determination index R? and the corrected multiple determination index RC? (in the formula with R on top)
RC? = R? (n-1) / (n-m-1)
47. Suppose that for the description of one economic process 2 models are suitable. Both are adequate according to the Fisher's f criterion. which one to give an advantage to the one that has:
Greater value of F criterion
48. for a regression of n observations and m independent variables, is there such a relationship between R? and F
………… .. = [(n-m-1) / m] (R? / (1- R?)]
49. The significance of quotients and paired correlation coefficients is checked using
Student's T criterion
50. if there is an insignificant variable in the regression equation, then it reveals itself by a low value
T statistics
51.when is the model considered adequate?
Fcalc> Ftabl
52. What criterion is used to evaluate the significance of the Regression coefficient
Student's T
53. The value of the confidence interval allows you to establish how reliable the assumption that
The interval contains the parameters of the general population
54. the hypothesis of the absence of autocorrelation of residuals is proved if
Уt = a + b0x1 +? Yt-1 +? T
56. choose a model with lags
Уt = a + b0x1 ……. (The longest formula)
57. what points are excluded from the time series by the smoothing procedure
Standing at the beginning and end of the time series
58. What determines the number of points excluded as a result of smoothing
From the number of points ………………
59. Autocorrelation is available when
Each next value of residuals
60. as a result of autocorrelation we have
Ineffective parameter estimates
61. if we are interested in using attribute variables to display the effect of different months we should use
11 attributive methods
62. The additive time series model has the form
63. THE MULTIPLICATIVE MODEL HAS THE FORM
64. autocorrelation coefficient
Characterizes the tightness of the linear relationship between the current and previous levels of the series
65. Additive time series model is built
Seasonal Amplitude Increases and Decreases
66. On the basis of quarterly data ……… ..values of 7-1 quarter, 9-2 quarter and 11-3 quarter …………….
67. endogenous variables are
Dependent variables, the number of which is equal to the number of equations …… ..
68. Exogenous Variables
Predefined variables affecting ………… ..
69. lag variables are
The value of the dependent variables for the previous period of time
70. to determine the parameters, the structural form of the model must be transformed into
The reduced form of the model
71. an equation in which H is the number of endogenous variables, D is the number of missing exogenous variables, identifiable if
72. an equation in which H is the number of endogenous variables, D is the number of missing exogenous variables, Unidentifiable if
73. an equation in which H is the number of endogenous variables, D is the number of missing exogenous variables, is overidentifiable if
74. to determine the parameters of an accurately identifiable model
Indirect OLS is applied
75. to determine the parameters of the OVER-identifiable model
TWO-STEP OLS APPLIED
76. to determine the parameters of the Unidentified model
NOT ONE OF THE EXISTING METHODS CANNOT BE APPLIED
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