Consider the following econometric modely=B₁ + B₁ Before + B₂ Treatment + B3AfterXTreatment + uwhere the variable names are self-explanatory. In which kind of estimation is such a model typically used?a. After Treatment Effect estimationb. Difference-in-Difference estimationC. Instrumental Variable estimationFirst Difference estimation

We have given a econometric model and also given that variable names are self explanatory. Then we…

Answered: Consider the following econometric…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Consider the model Y = ß0 + B₁X1 + ß₂X2 + B3X³ + B4X4 + €. If it is suggested to you that the two variables Z₁ = X₁ + X3 and Z2 = X₂ + X4 might be adequate to represent the data, what hypothesis, in the form C3 = 0, would you need to test? (Give the form of C.)
3. A Ross MAP team is trying to estimate the revenues of major-league baseball teams during the regular season using a regression model. Currently, the independent variables include stadium capacity, the number of weekend games, the number of night games, and the number of Wins (out of 162 regular season games). One of your team members suggests that the model also should include the number of losses as it provides additional explanatory power. Assume that ties are not possible; so every game results in exactly one team winning and the other team losing. Which of the following statements is the most likely conclusion of the new regression model? (a) R2 will increase, adjusted R2 will decrease, and Serror will decrease. (b) R2 and adjusted R2 will increase, and serror will decrease. (c) R, adjusted R2, and Serror will increase. (d) We cannot trust the regression output as some variables are highly correlated, resulting in multicollinearity. Answer to Question 3:
1. Let's say X is a main predictor (or exposure) of your interest and M is a potential mediator, and Y is an outcome Model 1: Y = Bo(1) + tX+ E(1) Model 2: Y= Bo(2) + t’X+ BM+ ɛ(2) Model 3: M Воз) + аХ + £(3) Indicate the p-value of each regression parameter in the table below. Please fill the p-value in the table whether it must be greater or less than 0.05 for confirming M as a mediator as per Baron- Kenny's Criteria. t' (full mediation model) t' (partial mediation тodel) p-value
17) Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 41 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.9, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 90000 and the sum of squared errors (SSE) is 10000. From this information, what is the number of degrees of freedom for the t-distribution used to compute critical values for hypothesis tests and confidence intervals for the individual…
ANSWER THE FOLLOWING QUESTION.
3. Consider the following regression model: Weekly Hours = Bo + B1 × Wage + uj Weekly Hours is the average number of hours the individual worked over the course of the year and Wage is the individual's average hourly wage over the course of the year. A researcher who collects data and regresses Weekly Hours against Wage finds that B1 > 0. The OLS estimator, B, however, likely suffers from omitted variable bias because those individuals who earn high wages may be driven personalities who would work long hours no matter the wage. Because of this omitted variable bias, it is likely the case that B1_B1. A) В)
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
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13. A researcher estimates the following regression model: Yt = a + ßxt + Ut where yt and xt are both non-stationary variables. Which of the following statements is correct? a) This necessarily leads to spurious results b) The Durbin-Watson d statistic will be larger than the R² c) Including a time trend in the regression specification will provide meaningful results d) Differencing both variables may provide meaningful results but could lose some useful information
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Consider the following econometric model y = Bo + B₁ Before + B₂ Treatment + B3AfterXTreatment + u where the variable names are self-explanatory. In which kind of estimation is such a model typically used?