AAEC 4302 Ag advanced research

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What values of the VIF are said to be problematic? (i.e., they indicate a multicollinearity problem)

10 and higher

For the problem related to the effect of math aptitude test score on a Statistics class grade: What are the number of observations used in the model?

5

Good luck!

:)

Suppose that x is the high school GPA and y is the college GPA, and we happen to know that E[collegeGAP]= 1.5+ 0.5highschoolGPA. This means that:

A 1 point increase in highschoolGPA will increase the average collegeGPA of all students by 0.5 points

For the estimated model: log(y)hat = 10 + 0.56log(x), what is the correct interpretation of the coefficient related to log(x)?

A 1% change in x changes y by 0.56%

Econometric models can be used to:

All of the above Answers: To estimate economic relationships To test economic theories To evaluate policies All of the above

What does BLUE stands for?

Best, Linear, Unbiased Estimators

An estimate is the formula used to estimate a coefficient in the regression model

False

For hypothesis testing, the Ha: Bj = (with line in =) 0 is the same as saying that xj does NOT have an effect on y.

False

For tests of hypotheses about individual Bj's, a p-value is the probability related to the t-critical value from the t Table.

False

For the model: log(y)hat = 10 + 0.56log(x), calculation of the predicted value of y at x=10 equals e^(10+0.56log(10)).[Note that e^() is the constant e to the value inside the parenthesis]

False

In a quadratic model, the marginal effects are constant for any value of x.

False

In multiple linear regression, we cannot include as explanatory variables (x's) those variables denoting qualitative information

False

In the MLR model, a VIF=0 for a variable xj indicates that there is no correlation between this variable and all other explanatory variables.

False

Large t-calculated values are associated with large p-values.

False

Log-log models are very popular in Economics because the intercept coefficient has a very meaningful and easy to understand interpretation

False

Most of the data used in Economics in general is "experimental" data (i.e., data from experiments)

False

Multiple linear regression assumption 6 (MLR6) is related to the normality of the explanatory variables

False

SSE stands for error sum squares and corresponds to a measure of the variability in y that is not explained by the model

False

Simple Linear Assumption 1 (Linear in Parameters) indicates that no matter what is the real relatiomship between E[y] and x, the linear regression model is always the correct model.

False

Tests of hypotheses are about the estimated Bj's

False

The 95% confidence interval for Bj in a MLR model is [10, 20]. Since the confidence interval does not contain zero, we CAN'T reject the Ho: Bj=0.

False

The R2 measures the percentage of total variability in the independent variable (x) that is explained by the model

False

The expression E[y] means the estimated value of y

False

The formulas used to estimate B0 hat and B1 hat ensure that the sum of squared residuals (SSR= N all sum of squares U Hat Ui2 ) is a large as possible:

False

The log function used in Econometric models in this class refers to logarithms with base = 10

False

The main objective of the econometric model is to analyze the effect of an outcome (or dependent variable) on a set of factors (or explanatory variables)

False

The total variability of y that is not explained by the model = total explained variability of y in the model + total observed variability in y.

False

What is NOT an advantage of the multiple linear regression model relative to the simple linear regression model?

It is easier to estimate as the formulas are simpler

What are the minimum set of assumptions required for the least square estimators in the MLR model to be unbiased estimators of the true population coefficients?

MLR1, MLR2, MLR3, MLR4

What is correct regarding marginal effects in linear and nonlinear models?

Marginal effects in linear models are constant whereas marginal effects in nonlinear models are non-constant

What are the maximum and minimum values for R2?

Minimum = 0; maximum=1

The least squares estimators and are unbiased estimators for and since:

On average, they are equal to the true population values (i.e., E[]= and E[]= ).

Two-sided confidence intervals about the Bj's:

Provide a range of potential values of the true (population) coefficients

What assumptions are needed for the Gauss Markov Theorem to work?

SLR1 to SLR 5

One star (*) next to a coefficient in the Table form used to report regression results denote:

Statistical significance at the 10% level

Suppose that x is the high school GPA and y is the college GPA, and we happen to know that E[collegeGAP]= 1.5+ 0.5highschoolGPA. This means that:

The average college GPA for students whose high school GPA was 0 is 1.5.

What does endogeneity refers to?

The correlation between x and u

Which of the following is NOT an example of characteristics denoting qualitative information

The price of gasoline

The stars (*) used to report regression results denote information about:

The statistical significance of the individual explanatory variables

A dummy variable is a variable that takes only 2 values: zero and one

True

A log-level type of model indicates that the dependent variable is in log form, but the explanatory variable is not in log form

True

For hypothesis testing, the Ho: Bj=0 is the same as saying that xj does NOT have an effect on y

True

For tests of hypotheses about individual Bj's, a p-value is the probability related to the calculated t-statistic

True

For tests of hypotheses about individual Bj's, if the calculated t-statistic falls outside the rejection region we fail to reject the Ho.

True

In a MLR model, confidence intervals for can be used to 1) obtain a range of estimates of Bj, and 2) to conduct tests of hypothesis regarding Bj.

True

In the MLR model multicollinearity is a problem since it increases the variances of the estimated coefficients.

True

In the MLR model, the maximum value of the VIF is infinity ()

True

In the simple linear regression model , use of the least squares estimators for and , and , result in different estimated values when different samples are used:

True

In the simple linear regression model y = Bo + B1x + U , use of the least squares estimators for B0 and B1 , B0 hat and B1 hat , result in different estimated values when different samples are use

True

Log-log models are very popular in Economics because the coefficients can be interpreted as elasticities

True

Marginal effects and elasticities measure the effect of an explanatory variable on the dependent variable.

True

Most of the data used in Economics in general is "observational" data

True

Multiple linear regression assumption 6 (MLR6) is related to the normality of the error term

True

Random sampling means that each individual is the population has the same probability of being selected in the sample

True

SST stands for "Total Sum of Squares" and corresponds to a measure of the total variability of the dependent variable.

True

The "Best" property in the Gauss Markov theorem refers to the variance of the estimators (i.e., they have small relative variance).

True

The Rj^2 term to the R-square of a regression of xj as the dependent variable and all other x's as explanatory variables.

True

The expression E[y] means the expected value of y:

True

The formulas for the variances of B0 Hat and B1 Hat provided in class are only valid if the homoskedasticity assumption is satisfied. If it is not satisfied, the formulas are no longer valid.

True

The log-log type of model indicates that both the dependent variable and the explanatory variable are in log form

True

The main objective of the econometric model is to analyze the effect of a set of factors (or explanatory variables) on an outcome (or dependent variable)

True

The marginal effect is the same as the same effect of an additional unit of x on the average value of y.

True

The t distribution is an asymmetric distribution

True

Theoretically, the variance of the errors equals the variance of the dependent variable

True

What is the correct formula for the residuals or predicted errors?

Ui hat = yi - yi hat

What is the correct formula for the residuals or predicted errors?

Ui(hat) = Yi - Yi(hat)

What does VIF stands for?

Variance Inflation Factor

For a 2 tailed test of hypothesis about an individual coefficient, if the p-value is 0.09.

We reject the Ho at the 10%

For a 2 tailed test of hypothesis about an individual coefficient, if the p-value is 0.05

We reject the Ho at the 5% and 10% level

For the linear regression model y=bo +dx + u, where x is a dummy variable:

bo represents the average value of y when x=0

For a linear regression model y=bo +dx + u, where x is a dummy variable:

d represents the difference between the average value of y when x=1 and the average value of y when x=0

For the estimated quadratic model: Y hat = 10 + 12X - 3x^2 , what is the estimated effect of an additional unit of x when x=0

x = 12

The simple linear regression model: y = Bo + B1x +u implies a linear relationship between

x and the average value of y (which is E[y])

In the econometric model: y = Bo + B1x + u

y is the dependent variable, x is an explanatory variable or factor


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