# 4 tasks of econometrics

Sold: 0
Refunds: 0

Content: 30522155507720.zip (185,44 kB)

# Description

Objective 1.
In areas of the region display data for 199H of
Region Number Per capita cost of living in a working-day, rub., X Average daily wages, rbl., From
1 92 147
2 78133
3 79128
4 88152
5 87138
6 75 122
7 81145
8 96141
9 80127
10102151
11 83 129
12 94 147
Required:
1. A linear regression equation of the pair at x.
2. Calculate the linear correlation coefficient, determination coefficient and the average error of approximation.
3. To assess the statistical significance of the regression equation as a whole and the individual parameters of regression and correlation using the F-test and the Fisher Student t-test.
4. Run the forecast salary from the forecasted value of the average per capita subsistence level x, is 107% of the average.
5. Evaluate the accuracy of prediction by calculating prediction error and the confidence interval.
6. On the same graph to postpone the original data and the theoretical line.

Objective 2.
Keynes model (a simplified version).
where C - consumption; Y - income; I -investitsii; G - government spending; t - current period; t-1 - the previous period.
Required:
- By applying the necessary and sufficient condition for identification, determine whether each is identifiable from the equations of the model;
- Define the method for estimating model parameters;
- Record the shape of the model.

Objective 3
There are conditional data on volumes of electricity consumption (y) inhabitants of the region in 16 quarters.
Required:
1.Postroit autocorrelation function and infer the presence of seasonal variations.
2. Construct the multiplicative model of the time series.
3. Make a forecast for Q2 forward.
t yt t yt
1 5.3 9 8.2
2 4.7 10 5.5
3 5.2 11 6.5
4 9.1 12 11.0
5 7.0 13 8.9
6 5.0 14 6.5
7 6.0 15 7.3
8 10.1 16 11.2

20 companies in the region studied the dependence of production output per worker y (ths. Rub.) On the introduction of new fixed assets x1 (% of assets at the end of the year) and by the proportion of highly skilled workers in the labor x2 (%) .
Number enterprise y x1 x2 Number enterprise y x1 x2
1 7 3.6 9 11 10 6.3 21
2 7 3.6 11 12 11 6.9 23
3 7 3.7 12 13 11 7.2 24
4 8 4.1 16 14 12 7.8 25
5 8 4.3 19 15 13 8.1 27
June 8 4.5 8.2 19 16 13 29
7 9 5.4 20 17 13 8.4 31
8 9 5.5 20 18 14 8.8 33
9 10 5.8 21 19 14 9.5 35
October 10 6.1 21 20 9.7 14 34
Required:
1.Postroit linear multiple regression model. Record the standardized multiple regression equation. On the basis of standardized regression coefficients and average elasticities to rank the factors according to their impact on the result.
2. Find the coefficient of pair, partial and multiple correlation. Analyze them.
3. Find the adjusted coefficient of multiple determination. Compare it with the unadjusted (general) coefficient of determination.
4. Use the F - Fisher criterion to assess the statistical reliability of the regression equation and the coefficient of determination R2yx1x2.
5. With the help of the private F-Fisher criterion to evaluate the appropriateness of including in the multiple regression equation factors x1 and x2 after factor after x1, x2.