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2. (a) Let us consider a full model of a balanced (all t treatments have equal...
2. (a) Let us consider a full model of a balanced (all t treatments have equal...
2. (a) Let us consider a full model of a balanced (all t treatments have equal number of observations r) CRD design with t treatments and r replications of each treatment, hence having n-rt observations i. Minimizing sum of square error Δfull(μ, Tỉ)-Σι-12jai (Vij-l-ri)2 with respect to μ and Ti find the least square estimators of μ and Te as μ and Ti Hint: Take derivative of the objective function with respect to u and Ti and equate then to...
Please help! (a) Let us consider a full model of a balanced (all t treatments have...
Please help!
(a) Let us consider a full model of a balanced (all t treatments have equal number of observations r) CRD design with t treatments and r replications of each treatment, hence having n-rt observations. 2. i. Minimizing sum of square error Δfull (μ'Ti) -Σι-1 Σ-1 (Vi,- μ-Ti)2 with respect to μ and Ti find the least square estimators of μ and Ti as μ and Ti. Hint: Take derivative of the objective function with respect to μ and...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male workers was collected to estimate the following model Y; = Bo + B1X; + uj, for i = 1,..., 21. Here, Y; is the logarithm of hourly wage rate, log(wage), for the i-th worker. Xi is the education level, husedu, of the i-th worker, which is measured as the years of schooling, and uị is the error term for the i-th worker. The ordinary...
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....
3. (20 pts) Suppose that we have 4 observations for 3 variables y,I, 2 and consider...
3. (20 pts) Suppose that we have 4 observations for 3 variables y,I, 2 and consider a problem of regressing y on two (qualitative) variables r, 2. Data: 22 obs no. y (Income) 2 (Management Status) I (Gender) 1 None Female 2 None Male Yes Female Yes Male 4 To handle the qualitative variables r, 12, we define dummy variables 1, 22 as for 1, 22= Yes Male for 1, 219 22 -1. for 22= None for 1= Female -1,...
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:...
Which model is more appropriate for these data: the model in SAS Output 1 or the...
Which model is more appropriate for these data: the model in
SAS Output 1 or the model in SAS Output 2? Which test statistic and
p-value should you use to make this decision?
Output 1 because the interaction is not significant (F
= 0.92, p-value = 0.4594).
Output 1 because the interaction is not significant (F
= 6.25, p-value = 0.0003).
Output 1 because the interaction is significant (F =
6.25, p-value = 0.0003).
Output 2 because the interaction is...
Regression model>BEEF_CONSt - Bl B2INCOMEt+B3BEEF PRICEt B4PORK PRICEt+ et BEEF CONSIconsumption of beef per cap...
Regression model>BEEF_CONSt - Bl B2INCOMEt+B3BEEF PRICEt B4PORK PRICEt+ et BEEF CONSIconsumption of beef per capita in year t (kg), INCOMEt real income per capita in year t (thousands of dollars), BEEF PRICEt average real price of beef per kilogram in year t ($) PORK PRICEt= average real price of pork per kilogram in year t (S) Bk's regression cocfficients, and et is the random error term, which follows N(0, o2) Gretl Output for Section 2 Sunmary Stati stics Mean Median...
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question....
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question. (a) Estimate the model and report the results in the usual form, including the standard error of the regression. Obtain the predicted price when we plug in lotsize - 10, 000, sqrft - 2,300, and bdrms- 4; round this price to the nearest dollar. (b) Run a regression...
Consider the multiple regression model y = X3 + €, with E(€)=0 and var(€)=oʻI. Problem 1...
Consider the multiple regression model y = X3 + €, with E(€)=0 and var(€)=oʻI. Problem 1 Gauss-Mrkov theorem (revisited). We already know that E = B and var() = '(X'X)". Consider now another unbiased estimator of 3, say b = AY. Since we are assuming that b is unbiased we reach the conclusion that AX = I (why?). The Gauss-Markov theorem claims that var(b) - var() is positive semi-definite which asks that we investigate q' var(b) - var() q. Show...
2. (a) Let us consider a full model of a balanced (all t treatments have equal number of observations r) CRD design with t treatments and r replications of each treatment, hence having n-rt observations i. Minimizing sum of square error Δfull(μ, Tỉ)-Σι-12jai (Vij-l-ri)2 with respect to μ and Ti find the least square estimators of μ and Te as μ and Ti Hint: Take derivative of the objective function with respect to u and Ti and equate then to...
Please help!
(a) Let us consider a full model of a balanced (all t treatments have equal number of observations r) CRD design with t treatments and r replications of each treatment, hence having n-rt observations. 2. i. Minimizing sum of square error Δfull (μ'Ti) -Σι-1 Σ-1 (Vi,- μ-Ti)2 with respect to μ and Ti find the least square estimators of μ and Ti as μ and Ti. Hint: Take derivative of the objective function with respect to μ and...
Consider the relationship between hourly wage rate and education attainment. A random sample of 21 male workers was collected to estimate the following model Y; = Bo + B1X; + uj, for i = 1,..., 21. Here, Y; is the logarithm of hourly wage rate, log(wage), for the i-th worker. Xi is the education level, husedu, of the i-th worker, which is measured as the years of schooling, and uị is the error term for the i-th worker. The ordinary...
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....
3. (20 pts) Suppose that we have 4 observations for 3 variables y,I, 2 and consider a problem of regressing y on two (qualitative) variables r, 2. Data: 22 obs no. y (Income) 2 (Management Status) I (Gender) 1 None Female 2 None Male Yes Female Yes Male 4 To handle the qualitative variables r, 12, we define dummy variables 1, 22 as for 1, 22= Yes Male for 1, 219 22 -1. for 22= None for 1= Female -1,...
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:...
Which model is more appropriate for these data: the model in
SAS Output 1 or the model in SAS Output 2? Which test statistic and
p-value should you use to make this decision?
Output 1 because the interaction is not significant (F
= 0.92, p-value = 0.4594).
Output 1 because the interaction is not significant (F
= 6.25, p-value = 0.0003).
Output 1 because the interaction is significant (F =
6.25, p-value = 0.0003).
Output 2 because the interaction is...
Regression model>BEEF_CONSt - Bl B2INCOMEt+B3BEEF PRICEt B4PORK PRICEt+ et BEEF CONSIconsumption of beef per capita in year t (kg), INCOMEt real income per capita in year t (thousands of dollars), BEEF PRICEt average real price of beef per kilogram in year t ($) PORK PRICEt= average real price of pork per kilogram in year t (S) Bk's regression cocfficients, and et is the random error term, which follows N(0, o2) Gretl Output for Section 2 Sunmary Stati stics Mean Median...
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question. (a) Estimate the model and report the results in the usual form, including the standard error of the regression. Obtain the predicted price when we plug in lotsize - 10, 000, sqrft - 2,300, and bdrms- 4; round this price to the nearest dollar. (b) Run a regression...
Consider the multiple regression model y = X3 + €, with E(€)=0 and var(€)=oʻI. Problem 1 Gauss-Mrkov theorem (revisited). We already know that E = B and var() = '(X'X)". Consider now another unbiased estimator of 3, say b = AY. Since we are assuming that b is unbiased we reach the conclusion that AX = I (why?). The Gauss-Markov theorem claims that var(b) - var() is positive semi-definite which asks that we investigate q' var(b) - var() q. Show...
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