Question
In: Advanced Math
Consider the following regression results of B for time series data. The dependent variable is the...
Consider the following regression results of B for time series data. The dependent variable is the log of real consumption and the regressors are all lagged by one period. The coefficients are estimated by OLS method. *, **, and *** indicate that the coefficients are significant at the 10%, 5%, and 1% level, respectively. The F- test in the last row tests the joint hypothesis that all the coefficients except the constant are zero and their p-values are provided in the table. The empty space means that the variables are not included in the regression. (4) 3.1213*** 3.8876*** (1) Constant log(Real Income) 0.8683* Interest Rate Inflation Rate log(Stock Price) F-test 0.082 (2) (3) 3.3962*** 3.8115*** 0.6289*** 0.6341*** -0.2680** -0.2402** 0.5233 0.5230 -0.2504*** 0.6237 0.4775 0.064 0.095 0.044 (a) Why do we have to include lagg values of regressors in the equation? (b) Compare the results of equations (1) and (2) and make any comments. (c) Compare the results of equations (2) and (3) and make any comments. (d) Compare the results of equations (3) and (4) and make any comments.
Solutions
Colby Messinger answered 1 year agoRelated Solutions
Consider the following quarterly time series. The regression model developed for this data set that has...
Consider the following quarterly time series. The regression
model developed for this data set that has seasonality and trend is
as follows, yˆt = 864.08 + 87.8Qtr1t + 137.98Qtr2t + 106.16Qtr3t +
28.16t Compute the quarterly forecasts for next year based on the
regression model? Quarter Year 1 Year 2 Year 3 1 923 1112 1243 2
1056 1156 1301 3 1124 1124 1254 4 992 1078 1198
Consider the following time series data.
Consider the following time series data. Week123456Value181516131716a. Choose the correct time series plot.What type of pattern exists in the data? b. Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. Round your answers to two decimal places. WeekTime Series ValueForecast118215316413517616 MSE: The forecast for week 7: c. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. Round your answers to two decimal places. WeekTime Series ValueForecast118215316413517616MSE: The...
Consider the following time series data.
Consider the following time series data.
Quarter
Year 1
Year 2
Year 3
1
5
8
10
2
2
4
8
3
1
4
6
4
3
6
8
(b)
Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise.
If required,...
The following are regression results where Car Price is the dependent variable: Regression Statistics ?2=0.446R2=0.446 Adjusted...
The following are regression results where Car Price is the
dependent variable:
Regression Statistics
?2=0.446R2=0.446 Adjusted ?2=0.441R2=0.441 Observations =
804
Independent Variables
Coefficients
Standard Error
t Stat
P-value
Intercept
6758.76
1876.967
3.601
0.000
Mileage
-0.17
0.032
-5.326
0.000
Cylinder
3792.38
683.180
5.551
0.000
Liter
-787.22
867.062
-0.908
0.364
Doors
-1542.75
320.456
-4.814
0.000
Cruise
6289.00
657.992
9.558
0.000
Sound
-1993.80
571.776
-3.487
0.001
Leather
3349.36
597.681
5.604
0.000
Car Price is measured in dollars. The independent variables
are:
Mileage: number...
Below are results from a regression analysis. The dependent variable is the percent vote for Woodrow...
Below are results from a regression analysis. The dependent
variable is the percent vote for Woodrow Wilson in 1916, measured
from 0 to 100. Each observation is a New Jersey county, of which
there are 20. The independent variables are the county’s percentage
of Democrats (0 to 100), percentage working class (0 to 100), and
the number of shark attacks that occurred in it in 1916 (0, 1, 2, .
. . ). Shark attacks are rare: no county experienced...
PART B The following output was obtained from a regression analysis of the dependent variable Rating...
PART
B
The following output was obtained from a regression analysis of
the dependent
variable Rating and an independent
variable Price. (10 points)
ANOVA
df
SS
MS
F
Regression
1
372.707
372.707 42.927
Residual
15
130.234
8.682
Total
16
502.941
Coefficients
Standard
Error
t Stat
P-value
Intercept
45.623
3.630
12.569 0.000
Price
0.107
0.016
6.552 0.000
Use the critical value approach to perform an F test for the
significance of the linear relationship between Rating and Price at
the 0.05...
Using the following regression results Dependent Variable: Q for the monopolistically competitive chemical company Intercept &nbs
Using the following regression results
Dependent Variable: Q for the monopolistically competitive
chemical company
Intercept
-3.415
(0.2660)
Price
0.118
(0.029)
Price Related Chemicals 0.028
(0.003)
Local
Dummy
0.039
(0.033)
R^2
0.8022
ADJ.
R^2
0.7905
OBS 58
Note standard errors for each variable is in parenthesis below
the corresponding variable.
a. discuss the statistical significance of the parameters and
the equation as a whole.
b. what is the estimated value of the parameter?
c. what would be different...
Consider the following regression output with Sunday circulation of newspapers as dependent variable and Daily circulation...
Consider the following regression output with Sunday circulation
of newspapers as dependent variable and Daily circulation as
independent variable. Both Sunday and Daily circulation and
measured in thousands of copies.
Dependent Variable: Sunday
Variable:
Intercept
Daily
Coefficient
24.763
1.351
Std. Error
46.99
0.09
t Stat
0.527
14.532
P-value
0.602
0.000
Given the output above choose whether the following statement is
TRUE or FALSE.
Question 1
This regression is bad because we are only 39.8% confident that the
intercept coefficient is...
Consider the time series data in columns A and B. Month (A) Value (B) 1 6...
Consider the time series data in columns A and B.
Month (A)
Value (B)
1
6
2
11
3
9
4
14
5
15
a. Construct a time series plot.
b. Use simple linear regression analysis to find the parameters
for the line
c. For each new month, how much change is there in the value (on
average)?
d. Create a "Forecast" column and use the regression equation to
provide a forecast for each month. Include a forecats for month...
Consider a regression model of monthly time series data where we model the price of petrol...
Consider a regression model of monthly
time series data where we model the price of petrol which is
dependent on the Crude Oil price and Exchange rate (against US$).
Data for the three variables were collected over a 50 month period.
Suppose the estimation results showed that the Durbin-Watson (DW)
test value d is 1.38. Perform the DW test for first order positive
autocorrelation of the error terms at the 5% level of
significance.
Model: et = r...
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