In Instrumental variable estimation we require that an instrument is valid and relevant. This means: that the instrument is part of the structural equation. that the instrument is a good predictor of the endogeneous regressor and is not part of the structural equation. that the instrument correlates with the error terms of both the structural and the reduced form equations. that the instrument has been approved by the ethics committee of the University. All of the above. None of the above.

Answered: In Instrumental variable estimation we…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter3: Functions And Graphs

Section3.3: Lines

Problem 76E

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
Show calculations or explanation for each question. a) Which of the following techniques is used to predict the value of one variable on thebasis of other variables?a. Correlation analysisb. Coefficient of correlationc. Covarianced. Regression analysis b) In the least squares regression line, y^=3-2x the predicted value of y equals:a. 1.0 when x = −1.0b. 2.0 when x = 1.0c. 2.0 when x = −1.0d. 1.0 when x = 1.0 c) In the simple linear regression model, the y-intercept represents the:a. change in y per unit change in x.b. change in x per unit change in y.c. value of y when x = 0.d. value of x when y = 0.
A statistical program is recommended. An automobile dealer conducted test to determine whether the time needed to complete a minor engine tune-up depends on whether a computerized engine analyzer or an electronic analyzer is used. Because tune-up time varies among compact, intermediate, and full-sized cars, the three types of cars were used as blocks in the experiment. The data (time in minutes) obtained follow. x₂ Analyzer 0 Computerized Electronic Define all variables. Let x₁ = 0 if a computerized analyzer is used, or let x₁ = X3 O Ho: B₁ * 0 H₂: B₁ = 0 0 Compact 1 52 Car 41 Compact Intermediate Full Size Intermediate O Ho: One or more of the parameters is not equal to zero. H₁: B₂ =B3 = 0 Car 57 Find the p-value. (Round your answer to three decimal places.) p-value = 43 Use α = 0.05 to test for any significant differences between the two analyzers. State the null and alternative hypotheses. O Ho: B₁ = 0 H₂: B₁ = 0 Full Size o Ho: Ba = By = 0 H₂: One or more of the parameters is not…
Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is: kids; = Bo + B1educ; + uż. 1. What are the parameters in the model? 2. What kinds of factors are contained in u? Are these likely to be cor- related with level of education? 3. Will a simple regression analysis uncover the ceteris paribus effect of education on fertility? Explain.
Assume we have data demonstrating a strong linear link between the amount of fertilizer applied to certain plants and their yield. Which is the independent variable in this research question?
The main regression specification of CG involves regressing firm investment rates on market Q, fundamental Q, cash flows, a bubble indicator and an interaction term between bubble and market Q. If you want to see the impact of bubbles on firm investment rates, you will examine significance of Select one: O a. coefficient of the Bubble indicator only O b. sum of the coefficients of market Q and the interaction term. O c. coefficient of the interaction term only O d. sum of the coefficients of Bubble indicator and the interaction term.
Consider the following econometric model y=B₁ + B₁ Before + B₂ Treatment + B3AfterXTreatment + u where the variable names are self-explanatory. In which kind of estimation is such a model typically used? a. After Treatment Effect estimation b. Difference-in-Difference estimation C. Instrumental Variable estimation First Difference estimation
We have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.
Which of the following is NOT a good reason for including a disturbance term in a regression equation? Select one: a. It captures omitted determinants of the dependent variable b. To allow for errors in the measurement of the dependent variable c. To allow for random influences on the dependent variable d. To allow for the non-zero mean of the dependent variable
ANSWER THE FOLLOWING QUESTION.
A residual plot is a plot in which the residuals are plot ted against the value of the explanatory variable x. When a residual plot exabits a noticeable pattern, the variables do not have a linear relationship, and the least square regression line should not be used. When a residual plot exabits no noticeable pattern, the least square regression line may be used to describe the relationship between the variables. For each of the following residual plots, determine whether a linear model is appropriate. a. Residual 0.5 2.0 1.5 1.0 0505 0 -0.5 -1.0 -1.5 -2.0 -2 The residual plot in (a) [Select] square regression line [Select] b. Residual 19 15 10 5 0 -5 -10- -15 -10 10 15 and thus the least- The residual plot in (b) does not exabit a noticeable pattern and thus the least -square regression line regression line may be used

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		that the instrument is part of the structural equation.
		that the instrument is a good predictor of the endogeneous regressor and is not part of the structural equation.
		that the instrument correlates with the error terms of both the structural and the reduced form equations.
		that the instrument has been approved by the ethics committee of the University.
		All of the above.
		None of the above.