12.54 John Swanson, president of Market Research Inc.,has asked you to estimate the coefficients of themodelY = Bo + B,X, + BX + BaXywhere Y is the expected sales of office supplies fora large retail distributor of office supplies, X¡ isthe total disposable income of residents within5 miles of the store, and X, is the total numberof persons employed in information-based busi-nesses within 5 miles of the store. Recent work bya national consulting firm has concluded that thecoefficients in the model must have the followingrestriction:Bi+ B - 2Describe how you would estimate the model coeffi-cients using least squares.

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Answered: 12.54 John Swanson, president of Market…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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In economics we are often faced with causal problems where the endogeneity arises because of simultaneity. A classic example is that if you are interested in estimating a demand curve, the issue is what you observe in the data are equli- birum and prices and quantity which is not only a function of demand but also a function of supply. In this excercise we will generalize the problem of simultaneity bias. Consider a situation where we are interested in the effect of Ti on Yi, i.e. obtaining a consistent estimate of α. But Yi also effects Ti. Let Xi be some exogenous covariates affecting both Yi and Ti. Let us imagine you have an exogenous variable Z which only effects Ti but does not directly effect Yi. In particular, consider the following structural equations: Yi = αTi+Xi′β+ui (1) Ti = ρYi+Xi′γ+Ziδ+νi (2) where E(ui | Xi,Zi) = 0 and E(vi | Xi,Zi) = 0 (a) Show why you cannot you use OLS to estimate α consistently in model 1? (b) Solve for the reduced form equation for Ti…
15. The following five questions are based on the paper "THE POWER OF TV: CABLE TELEVISION AND WOMEN'S STATUS IN INDIA" by Jesnsen and Oster that tries to causally explore how access to cable television in villages in India affects domestic violence, preference for a male child and the autonomy that women enjoy. The variable "Village has cable" is a dummy variable equal to 1 if the village has access to cable and 0 otherwise. The variable beating attitude is a continuous variable, where higher beating attitude indicates higher instance of domestic violence. The variable son preference is a dummy variable which is equal to 1 if the household expressed preference for a male child. The variable autonomy is also a continuous variable where a higher number indicates higher autonomy for women. Two asterisks (stars) indicate statistical significance at the 5% level of signifinance, whereas three asterisks (stars) indicate statistical significance at the 1% level of significance Based on the…
What does February 2020’s actual grocery store sales value tell us about the strength of this model to extend past the ten years covered in the scatterplot?
4. The following output from R presents the results from computing a linear model. In our data example we are interested to study the relationship between students' academic performance api00 with variable enroll which is the number of students in the school. Call: Im (formula = api00 enroll, data = d) Residuals: мin 10 Median 30 Маx -285.50 -112.55 -6.70 95.06 389.15 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 744.25141 15.93308 46.711 < 2e-16 *** enroll -0.19987 0.02985 -6.695 7.34e-11 *** --- Signif. codes: O ' *** ' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 135 on 398 degrees of freedom Multiple R-squared: 0.1012, Adjusted R-squared: F-statistic: 44.83 on 1 and 398 DF, p-value: 7.339e-11 0.09898
I2
Illustration. 21.22. Comment criticaly on the following state- ments: (a) Road accidents resulted in 4513 deaths in 1958 and 5250 in 1967 while the number of women drivers increased in the period. Hence women make bad drivers. (b) Of 300 persons inoculated against influenza during the epide- mic only 12% were affected. This shows a marked improvement com- paring with the remainder of the population for which the equivalent igure was 28%.
Imagine that a professor of psychology has two teaching assistants (TAs) who will help her grade assignments for the duration of the semester. The professor wants to make sure that she and the TAs are well calibrated with one another, so she has all three of them grade the first assignment independently. Because the professor grades every assignment on a curve, she first converts the students’ scores to z-scores for each grader. The following table shows the z-scores for a population of 10 students in her class for each grader. Professor Teaching Assistant #1 Teaching Assistant #2 Student 1 0.70 1.27 1.27 Student 2 0.83 -0.64 -1.55 Student 3 -0.98 0.38 -0.14 Student 4 2.21 0.80 0.92 Student 5 -0.54 -1.34 -1.06 Student 6 0.43 0.10 0.30 Student 7 -1.03 -2.09 -1.50 Student 8 -0.01 0.85 1.14 Student 9 -0.54 0.48 0.57 Student 10 -1.07 0.20 0.04 The professor is going to use the z-scores to calculate the correlation coefficient between her scores…
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This dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise! (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important. (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test. (please fully describe steps if you are using Excel) Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…
13. In the regression equation Log(Cars_Sold) = 1500 + 2.3*Advertisement, what is the most appropriate interpretation of the slope coefficient? OA. a. $1 increase in advertisement, increases cars sold by 230% O B. b. $1 increase in advertisement increases cars sold by 2.3 OC. c. 1% increase in advertisement increases cars sold by 0.023 OD. d. 1% increase in advertisement increases cars sold by 230%
Suppose we obtain data on prices of big-screen televisions and estimate the following model: In(Price) = 4.06 + 0.06 * Size +0.23 * Wide + 0.34 * Plasma + 0.21 * LCD+0.09 * Memory where the dependent variable has been transformed, Size is the screen size measured in inches, Wide is a dummy variable equal to one if the television is a widescreen, Plasma is a dummy variable equal to one if the television is a Plasma screen, LCD is equal to one if the television is an LCD screen, and Memory is a dummy variable equal to one if the television has any memory card slots. What is the estimated price of a 42" Widescreen Plasma television with 2 memory card slots? Select one: O a. 7.24 O b. 7.33 1525.38 O d. 1394.09 • e 1881.83 Clear my choice

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