Homework Answers
(a) HS, Expenditures and Age > 65 are statistically significant.
| coefficient | std. error | t-ratio | p-value | |
| cons | 439.285 | 172.698 | 2.54366 | 0.014559 |
| income | 0.005538 | 0.006808 | 0.813378 | 0.420381 |
| poverty | 185.02 | 652.611 | 0.283507 | 0.778118 |
| HS | -462.801 | 135.031 | -3.42737 | 0.001333 |
| College | -450.472 | 379.835 | -1.18597 | 0.242 |
| Expenditures | 0.100872 | 0.028404 | 3.551393 | 0.000927 |
| Age > 65 | 4447.17 | 390.724 | 11.38187 | 1.06E-14 |
(b) 89.439% of the variation in the model is explained.
(c) The hypothesis being tested is:
H0: β1 = β2 = β3 = β4 = β5 = β6 = 0
H1: At least one βi ≠ 0
The p-value is 0.000.
Since the p-value (0.000) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the model is significant.
(d) The F-test would be used here.
The hypothesis being tested is:
H0: β1 = β2 = 0
H1: At least one βi ≠ 0
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2. Multiple restriction test You have just been hired at the Center for Disease Control in...
1.Which variables are statistically significant at the 5% level? 2.Which variables are statistically significant at the...
1.Which variables are statistically significant at the 5%
level?
2.Which variables are statistically significant at the 10%
level?
3.Which variables are insignificant?
4.Please present the correlation matrix of the independent
variables.
5.Please run the White test for heteroskedasticity, with
cross-products AND PRESENT YOUR RESULTS. Please explain whether the
test is significant or not.
6.If the White test is significant, please present the
heteroskedasticity-consistent White regression results.
7.Can you test this model for autocorrelation? Why of why not?
If you do,...
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E,...
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E, and the estimated model: Model 1: OLS, using observations 1959-1995 (T = 37) Dependent variable: c t-ratio p-value const time Disposable Income Coefficient Std. Error 2707.84 385.254 80.9122 13.6539 0.508123 0.0460444 Mean dependent var Sum squared resid R-squared F(2, 34) Log-likelihood Schwarz criterion rho 11328.65 304975.4 0.998650 12577.63 -219.3165 449.4657 0.551018 S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson...
Attached are the results of a diagnostic test on an estimated model, autocorrelation, heteoskedasticity and non-normal...
Attached are the results of a diagnostic test on an estimated
model, autocorrelation, heteoskedasticity and non-normality
respectivey, can you please comment on the results and state the
conclusion for each test using a 5% significance level
Breusch-Godfrey Serial Correlation LM Test F-statistic Obs R-squared 0.7659 0.7612 0.458959 Prob. F(4,438) 1.861565 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 05/22/19 Time: 22:02 Sample: 1982M01 2019M02 Included observations: 446 Presample missing value lagged residuals set to zero. Coefficient Std....
I have a model from Gretl which I'm trying to copy the latex code for so l can paste in a web browser. However, when...
I have a model from Gretl which I'm trying to copy the latex
code for so l can paste in a web browser. However, when l submit
the code I'm getting weird results and the table is not coming out
how l would like. How do l get the latex code from the Gretl model
to format exactly the same on this web browser.
Latex Code from copying the output:
\begin{center}
Model 1: OLS, using observations 1--60\\
Dependent variable: l\_Y\\...
We want to look at potential predictors of movie revenues. Model 1: OLS, using observations l-609...
We want to look at potential predictors of movie revenues. Model 1: OLS, using observations l-609 Dependent variable: USGrossM coefficient std. error t-ratio p-value --------------------------------------- ------------------------ const -52.3692 15.4296 -3.394 0.0007 *** BudgetM 0.972348 0.0484576 20.07 4.89e-069 *** RunTimemin 0.387214 0.155146 2.496 0.0128 CriticScoreRotter 0.640257 0.0953758 6.713 4.40e-011 *** Mean dependent var Sum squared resid R-squared F(3, 605) Log-likelihood Schwarz criterion 75.81977 2004759 0.517227 216.0592 -3330.345 6686.337 S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion Hannan-Quinn...
Consider time series yt , defined as the daily percentage change in SP500 index. A researcher...
Consider time series yt , defined as the daily
percentage change in SP500 index. A researcher estimated the
following model:
(a) There is one partial
autocorrelation coefficient that you can find from the estimation
result. What is the value of it? What is order (k ) of
it?
(b) Test the null hypothesis that the partial autocorrelation
coefficient that you have is zero against the alternative that it
is not zero.
Dependent Variable: GROWTH Method: Least Squares Date: 03/08/15 Time:...
The information of data 1 Question Consider the following table that relates earning per hour (WAGE)...
The information of data 1
Question
Consider the following table that relates earning per hour (WAGE) to years of education (EDUC): Dependent Variable: WAGE Method Least Squares Date: 03/09/20 Time 1330 Sample: 11200 Included observations: 1200 Variable Coefficient Std. Error -Statistic tbl) 1770148 Prob. 0.0000 0.0000 1962400 se(b2) EDUC - 10 39996 2 396761 R-squared Adjusted R-squared SE of regression Sum squared resid Log likelihood F-statistic Prob(F statistic) 0 207327 Mean dependent var 0 206666 SD dependent var 13.55328 Akake...
just anw the c part thx Question 1 (100 Marks) The following table is the regression...
just anw the c part thx
Question 1 (100 Marks) The following table is the regression results from the econometric model: LOG(SALES) = B. + B2LOG (PRICE) + BzADVERT + e For a sample of 66 observations. SALES: Monthly Sales of product A ($1000) PRICE: A price Index of product A (SI) ADVERT: Adverting Expenditure on product A (S1000) Dependent Variable: LOGSALES Method: Least Squares Date:03/19/20 Time: 20:04 Included observations: 66 Variable Coefficient Std. Error -Statistic Prob. LOGPRICE ADVERT 5.325153...
One would expect that new home construction and sales depend on mortgage interest rates. If interest...
One would expect that new home construction and sales depend on mortgage interest rates. If interest rates are high, fewer people will be able to afford to borrow the funds necessary to finance the purchase of a new home. Builders are well aware of this fact and, when mortgage rates are high, they will be less inclined to build new homes. Based upon a sample of 184 monthly observations from January, 1990 to April, 2005, you have estimated the following...
gretl: model 1 File Edit Tests Save Graphs Analysis LaTeX Question 5 In your first year...
gretl: model 1 File Edit Tests Save Graphs Analysis LaTeX Question 5 In your first year microeconomics course you learned about differentiated products. As an econometrics student differentiated products are interesting because they are prime candidates for hedonic price modelling. As mentioned in class, a hedonic price model is a regression model that relates the price of a differentiated product (a residential house in this case) to its characteristics. For this assignment you will construct a simple hedonic model for...
1.Which variables are statistically significant at the 5%
level?
2.Which variables are statistically significant at the 10%
level?
3.Which variables are insignificant?
4.Please present the correlation matrix of the independent
variables.
5.Please run the White test for heteroskedasticity, with
cross-products AND PRESENT YOUR RESULTS. Please explain whether the
test is significant or not.
6.If the White test is significant, please present the
heteroskedasticity-consistent White regression results.
7.Can you test this model for autocorrelation? Why of why not?
If you do,...
1. Autocorrelation test Given the model Consumption, = a + B.Year + B Disposible Income, +E, and the estimated model: Model 1: OLS, using observations 1959-1995 (T = 37) Dependent variable: c t-ratio p-value const time Disposable Income Coefficient Std. Error 2707.84 385.254 80.9122 13.6539 0.508123 0.0460444 Mean dependent var Sum squared resid R-squared F(2, 34) Log-likelihood Schwarz criterion rho 11328.65 304975.4 0.998650 12577.63 -219.3165 449.4657 0.551018 S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson...
Attached are the results of a diagnostic test on an estimated
model, autocorrelation, heteoskedasticity and non-normality
respectivey, can you please comment on the results and state the
conclusion for each test using a 5% significance level
Breusch-Godfrey Serial Correlation LM Test F-statistic Obs R-squared 0.7659 0.7612 0.458959 Prob. F(4,438) 1.861565 Prob. Chi-Square(4) Test Equation: Dependent Variable: RESID Method: Least Squares Date: 05/22/19 Time: 22:02 Sample: 1982M01 2019M02 Included observations: 446 Presample missing value lagged residuals set to zero. Coefficient Std....
I have a model from Gretl which I'm trying to copy the latex
code for so l can paste in a web browser. However, when l submit
the code I'm getting weird results and the table is not coming out
how l would like. How do l get the latex code from the Gretl model
to format exactly the same on this web browser.
Latex Code from copying the output:
\begin{center}
Model 1: OLS, using observations 1--60\\
Dependent variable: l\_Y\\...
We want to look at potential predictors of movie revenues. Model 1: OLS, using observations l-609 Dependent variable: USGrossM coefficient std. error t-ratio p-value --------------------------------------- ------------------------ const -52.3692 15.4296 -3.394 0.0007 *** BudgetM 0.972348 0.0484576 20.07 4.89e-069 *** RunTimemin 0.387214 0.155146 2.496 0.0128 CriticScoreRotter 0.640257 0.0953758 6.713 4.40e-011 *** Mean dependent var Sum squared resid R-squared F(3, 605) Log-likelihood Schwarz criterion 75.81977 2004759 0.517227 216.0592 -3330.345 6686.337 S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion Hannan-Quinn...
Consider time series yt , defined as the daily
percentage change in SP500 index. A researcher estimated the
following model:
(a) There is one partial
autocorrelation coefficient that you can find from the estimation
result. What is the value of it? What is order (k ) of
it?
(b) Test the null hypothesis that the partial autocorrelation
coefficient that you have is zero against the alternative that it
is not zero.
Dependent Variable: GROWTH Method: Least Squares Date: 03/08/15 Time:...
The information of data 1
Question
Consider the following table that relates earning per hour (WAGE) to years of education (EDUC): Dependent Variable: WAGE Method Least Squares Date: 03/09/20 Time 1330 Sample: 11200 Included observations: 1200 Variable Coefficient Std. Error -Statistic tbl) 1770148 Prob. 0.0000 0.0000 1962400 se(b2) EDUC - 10 39996 2 396761 R-squared Adjusted R-squared SE of regression Sum squared resid Log likelihood F-statistic Prob(F statistic) 0 207327 Mean dependent var 0 206666 SD dependent var 13.55328 Akake...
just anw the c part thx
Question 1 (100 Marks) The following table is the regression results from the econometric model: LOG(SALES) = B. + B2LOG (PRICE) + BzADVERT + e For a sample of 66 observations. SALES: Monthly Sales of product A ($1000) PRICE: A price Index of product A (SI) ADVERT: Adverting Expenditure on product A (S1000) Dependent Variable: LOGSALES Method: Least Squares Date:03/19/20 Time: 20:04 Included observations: 66 Variable Coefficient Std. Error -Statistic Prob. LOGPRICE ADVERT 5.325153...
One would expect that new home construction and sales depend on mortgage interest rates. If interest rates are high, fewer people will be able to afford to borrow the funds necessary to finance the purchase of a new home. Builders are well aware of this fact and, when mortgage rates are high, they will be less inclined to build new homes. Based upon a sample of 184 monthly observations from January, 1990 to April, 2005, you have estimated the following...
gretl: model 1 File Edit Tests Save Graphs Analysis LaTeX Question 5 In your first year microeconomics course you learned about differentiated products. As an econometrics student differentiated products are interesting because they are prime candidates for hedonic price modelling. As mentioned in class, a hedonic price model is a regression model that relates the price of a differentiated product (a residential house in this case) to its characteristics. For this assignment you will construct a simple hedonic model for...
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