prelim-1-spring-2022

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Apr 3, 2024

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Name NetID Student ID 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 Cornell Sheet (3538) ZipGrade.com 1 A B C D E 2 A B C D E 3 A B C D E 4 A B C D E 5 A B C D E 6 A B C D E 7 A B C D E 8 A B C D E 9 A B C D E 10 A B C D E 11 A B C D E 12 A B C D E 13 A B C D E 14 A B C D E 15 A B C D E 16 A B C D E 17 A B C D E 18 A B C D E 19 A B C D E 20 A B C D E 21 A B C D E 22 A B C D E 23 A B C D E 24 A B C D E 25 A B C D E 26 A B C D E 27 A B C D E 28 A B C D E 29 A B C D E 30 A B C D E
Department of Economics Econ 3120 Cornell University Applied Econometrics Spring 2022 Prelim 1 — Spring 2022 GENERAL INSTRUCTIONS: Please write your name on the cover bubble sheet. Please fill in your Cornell student id on the bubble sheet under Student ID. Please answer the survey (Question 0) using the bubble sheet. You can use any notes you have written (or printed) on a single sheet of paper. You may use a calculator as long as you don’t use it to store additional notes or connect to the Internet. You may not use a cell phone, the internet, make phone calls, or send text messages during the exam. Show your work. Please write as neatly as you can. If you need to make assumptions, please clearly state them in your answer. 2
Question 0. Brief Survey on Studying (2 points) Your answers to these questions have absolutely no impact on your exam score, and I will remove all your names before I analyze this data. (1) How much of the material we’ve covered since the review module (i.e., experiments and bivariate regression) had you seen before? (a) none (b) some (c) almost all or all (2) We’ve had 8 classroom lectures so far. How many did you attend? (a) none (b) 1-3 (c) 4-5 (d) 6-7 (e) 8 (3) Not including attending lectures, how much time have you spent on this class each week doing things like reading or working on problem sets? (a) less than one hour (b) 1-2 hours (c) 3-4 hours (d) 5-6 hours (e) 7+ hours (4) How much time did you spend studying for this prelim? (a) less than one hour (b) 1-4 hours (c) 4-8 hours (d) 9+ hours (5) Did you mostly study for this prelim by yourself or with other students in the class? (a) mostly by yourself (b) mostly with other students in the class (c) about half by yourself and half with other students in the class 3
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(6) When did you do most of your studying for this prelim? (a) the day before and day of the exam (b) spread over multiple sessions over multiple days starting before the night before the exam To prepare for this prelim, did you (7) Review the lecture slides and/or your notes? (a) Yes (b) No (8) Read the book? (a) Yes (b) No (9) Identify areas or topics you are struggling with and get help (e.g., in o ffi ce hours or on the course discussion board)? (a) Yes (b) No (10) Study the problem sets and solutions? (a) Yes (b) No (11) Re-work problems from the problem sets? (a) Yes (b) No (12) Work new sample problems? (a) Yes (b) No (13) Explain or teach the material to someone else? (a) Yes (b) No 4
Question 1. Alcoholic Beverage Expenditures (20 points) Suppose you are interested in the relationship between age and consumption of alcoholic beverages. You decide to analyze all the single member households in the 2020 Consumer Expenditure Survey, and run the following regression. The dependent variable is the natural log of quarterly alcoholic beverage ex- penditures and the independent variable is the age (in years) of the household member. Age ranges from 16 to 87 in the sample. . reg ln_alcbevpq age_ref Source | SS df MS Number of obs = 689 -------------+---------------------------------- F(1, 687) = 38.11 Model | 49.9641117 1 49.9641117 Prob > F = 0.0000 Residual | 900.674363 687 1.31102527 R-squared = -------------+---------------------------------- Adj R-squared = Total | 950.638475 688 1.38174197 Root MSE = 1.145 ------------------------------------------------------------------------------ ln_alcbevpq | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age_ref | -.0135391 .0021931 -6.17 0.000 _cons | 5.07693 .1097273 46.27 0.000 4.861489 5.292371 ------------------------------------------------------------------------------ (a) (5 points) What do your results say is the e ect of one year increase in age on alcoholic beverage expenditures? 5 en yer dependent Y yo I 149 age is associated w a 1 410 decrease in consumption all
(b) (5 points) Compute the 75% confidence interval for the age coe ffi cient. (c) (5 points) Predict the log expenditures on alcoholic beverages by a single member household who is 30 years old. Assuming the average age in the sample is 40 years old, build a 95% interval around your prediction that accounts for both inherent noise in the process (i.e., the variance of the error term of your regression model) and uncertainty in your coe ffi cient estimates. (d) (5 points) Why might your prediction interval be too narrow? In other words, what else might be inducing error in your prediction? 6 B I 1.15 0021931 ESE bl 537 35 3 0 0 Bo B lx 2 forecast error 9510 Pred int 4.672 I predicted Inexpediture I 1.96 forecast error GE uncertainty in coefficients specification error assume relationship btwn en expenditure age is linear
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Question 2. Grapefruits (20 points) The following two acceptance rules are being considered for determining whether to take a delivery of a large shipment of grapefruits: Rule 1: A random sample of ten grapefruits is checked, and the shipment is accepted only if the average weight in the sample is between 5 and 7 ounces. Rule 2: A random sample of 100 grapefruits is checked, and the shipment is accepted only if the average weight in the sample is between 5.5 and 6.5 ounces. Suppose the shipment contains grapefruits whose weight is distributed normally with a mean of 6 ounces and a standard deviation of 2 ounces. That is, the weight of each grapefruit in the shipment can be considered a random variable N (6 , 2 2 ). Weights of grapefruits in both the shipment and the samples are independent of each other. What is the probability of rejecting the shipment under each rule? 7 O O 5 6 Vare Z Vardi o4 INN 61.04 rejected w Pr EL 5 Prix n 2 1 Prix 27 L I Prez LEE 211 Pr Z 21058 2 1 1058 11 42 5 6 Var I Hot'YvavGi von N 61.04 rejects Pr Ics 5 Prix 6.5 2 1 Pv 26.5 2 l Pr Z Larga 211 Pr 222.5 24 9938 0124 G range however we reject less bk sample size higher
Question 3. Bad Chicken (20 points) Bob’s Grill buys its chicken exclusively from Sally’s Cheap Meat Company and usually doesn’t have a problem. In the previous 5 years, they have only had 20 incidents of food poisoning due to bad chicken. Unfortunately, over the last 12 weeks, Bob’s had two di erent weeks where one customer got sick and one week where two customers got sick. Bob wants you to tell him if the weekly rate he’s observed over the last 12 weeks is significantly di erent from the weekly rate he calculated from the previous 5 years. (a) (4 points) What is the null hypothesis? What is the alternative hypothesis? (b) (4 points) What is the sample standard deviation of the weekly rate during the last 12 weeks? 8 historic weekly rate 575251 0 1 Ho 9 0078 Ha Mto 07g NYT software pure chance new accidents stat sig s I Z xi x S 9 0 33372 2 1 11 13372 12 033312 I 999 to 89 2.774 54.424 5 06511
(c) (4 points) Using a one sample t-test and treating the historic rate as a constant, what is the value of the test statistic? You may assume the number of incidents in a given week is approximately normally distributed. (d) (4 points) Under the null hypothesis, what is the distribution of this test statistic? (e) (4 points) At a significance level of 5%, can you reject the null hypothesis? 9 3 1 1 1.36 the Test stat should be t statistic w 11 d.fr CV w G G 2 tail test s t 2.201 Maker in magnitude cant reject to
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Question 4. Three Observations (18 points) Following are the year-end closing values for the Nasdaq Composite and S&P 500 stock market indices for the last three years, as well as average closing for the three years: Year Nasdaq S&P 500 2019 8973 3231 2020 12888 3756 2021 15645 4766 Avg 12502 3918 (a) (4 points) What is the sample variance of the S&P 500 closing values in the sample above? (b) (4 points) What is the sample covariance between the Nasdaq index and S&P 500? 10 All Plugging into formulas Q S I x x Sx I 3231 3918 2 3756 391812 14766 39181 54 6081658 Say Z Xi F ly 5 3231 3918 8977 12502 3756 3918 12888 12502 4766 3918 15641 2513,57705
(c) (4 points) Suppose we regressed the Nasdaq index on the S&P 500 using the data above. (i.e., Nasdaq is the dependent variable.) What would the estimated slope coe ffi cient be? (d) (3 points) What would the estimated intercept be? (e) (3 points) What would the R 2 be? 11 b Sgt 4.13 bo y bit 12502 4413 3919 avg NAS b estimates avg SEP R ra Est JE RE
Question 5. Perceived Returns to Education (20 points) In 2010, Rob Jensen published a paper in the Quarterly Journal of Economics titled “The (Perceived) Returns to Education and the Demand for Schooling.” From the abstract: Economists emphasize the link between market returns to education and investments in schooling. Though many studies estimate these returns with earnings data, it is the perceived returns that a ect schooling decisions, and these perceptions may be inaccurate. Using survey data for eighth-grade boys in the Dominican Republic, we find that the perceived returns to secondary school are extremely low, despite high measured returns. Jensen goes on to conduct an experiment where he randomizes a sample of schools in the Dominican Republic to a treatment group where he informs stu- dents of the actual returns to schooling and a control group where he does not. Four years later, he follows up with both groups of students and finds that the 1,033 control group students went on to complete an average of 9.75 years of schooling. The 1,041 treatment group students completed an average of 9.93 years of schooling. The standard deviation in the control group was 2.25 years and in the treatment group it was 2.36. (a) (4 points) What does the observed 0.18 year di erence in outcomes between the two groups represent? Be specific about what the treatment is. 12 o o ATE if treatment student attending school when stroente i informed of actual return to schooling
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(b) (4 points) Assuming the variance in the two populations was equal, test whether the observed di erence in the years completed is statistically sig- nificant at the 5% and 10% levels. (c) (4 points) What would it mean if I told you the Average Treatment Ef- fect (ATE) was not the same as the Intent to Treat e ect (ITT) in this experiment? 13 8 Stalinist ha psi to I 77 can't reject 580 can at 101 students assigned treatment group didnt actually get treatment
(d) (4 points) What regression model would you estimate to get a b 1 that was exactly equal to the observed di erence in outcomes (i.e., 0.18)? Be specific about what your observations are and how you define your variables. (e) (4 points) Suppose instead of randomizing students to treatment and con- trol, that you put all the male students in the treatment group and all the female students in the control group. Give two reasons why the observed di erence in average outcomes is not the Average Treatment E ect (ATE). 14 Obs students dependent variable ly if yrs school complied explanatory variable di I in treatment group o in control group Yi Bot B dit Ei Selection bias Male female different IT bk male students may inherently get more edu than females bk of systemic issues
NORMAL DISTRIBUTION TABLE z 0 ±f f .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 .0 .5000 .5040 .5080 .5120 .5160 .5199 .5239 .5279 .5319 .5359 .1 .5398 .5438 .5478 .5517 .5557 .5596 .5636 .5675 .5714 .5753 .2 .5793 .5832 .5871 .5910 .5948 .5987 .6026 .6064 .6103 .6141 .3 .6179 .6217 .6255 .6293 .6331 .6368 .6406 .6443 .6480 .6517 .4 .6554 .6591 .6628 .6664 .6700 .6736 .6772 .6808 .6844 .6879 .5 .6915 .6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224 .6 .7257 .7291 .7324 .7357 .7389 .7422 .7454 .7486 .7517 .7549 .7 .7580 .7611 .7642 .7673 .7704 .7734 .7764 .7794 .7823 .7852 .8 .7881 .7910 .7939 .7967 .7995 .8023 .8051 .8078 .8106 .8133 .9 .8159 .8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389 1.0 .8413 .8438 .8461 .8485 .8508 .8531 .8554 .8577 .8599 .8621 1.1 .8643 .8665 .8686 .8708 .8729 .8749 .8770 .8790 .8810 .8830 1.2 .8849 .8869 .8888 .8907 .8925 .8944 .8962 .8980 .8997 .9015 1.3 .9032 .9049 .9066 .9082 .9099 .9115 .9131 .9147 .9162 .9177 1.4 .9192 .9207 .9222 .9236 .9251 .9265 .9279 .9292 .9306 .9319 1.5 .9332 .9345 .9357 .9370 .9382 .9394 .9406 .9418 .9429 .9441 1.6 .9452 .9463 .9474 .9484 .9495 .9505 .9515 .9525 .9535 .9545 1.7 .9554 .9564 .9573 .9582 .9591 .9599 .9608 .9616 .9625 .9633 1.8 .9641 .9649 .9656 .9664 .9671 .9678 .9686 .9693 .9699 .9706 1.9 .9713 .9719 .9726 .9732 .9738 .9744 .9750 .9756 .9761 .9767 2.0 .9772 .9778 .9783 .9788 .9793 .9798 .9803 .9808 .9812 .9817 2.1 .9821 .9826 .9830 .9834 .9838 .9842 .9846 .9850 .9854 .9857 2.2 .9861 .9864 .9868 .9871 .9875 .9878 .9881 .9884 .9887 .9890 2.3 .9893 .9896 .9898 .9901 .9904 .9906 .9909 .9911 .9913 .9916 2.4 .9918 .9920 .9922 .9925 .9927 .9929 .9931 .9932 .9934 .9936 2.5 .9938 .9940 .9941 .9943 .9945 .9946 .9948 .9949 .9951 .9952 2.6 .9953 .9955 .9956 .9957 .9959 .9960 .9961 .9962 .9963 .9964 2.7 .9965 .9966 .9967 .9968 .9969 .9970 .9971 .9972 .9973 .9974 2.8 .9974 .9975 .9976 .9977 .9977 .9978 .9979 .9979 .9980 .9981 2.9 .9981 .9982 .9982 .9983 .9984 .9984 .9985 .9985 .9986 .9986 3.0 .9987 .9987 .9987 .9988 .9988 .9989 .9989 .9989 .9990 .9990 3.1 .9990 .9991 .9991 .9991 .9992 .9992 .9992 .9992 .9993 .9993 3.2 .9993 .9993 .9994 .9994 .9994 .9994 .9994 .9995 .9995 .9995 3.3 .9995 .9995 .9995 .9996 .9996 .9996 .9996 .9996 .9996 .9997 3.4 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9997 .9998
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t-Distribution Table t The shaded area is equal to α for t = t α . df t . 100 t . 050 t . 025 t . 010 t . 005 1 3.078 6.314 12.706 31.821 63.657 2 1.886 2.920 4.303 6.965 9.925 3 1.638 2.353 3.182 4.541 5.841 4 1.533 2.132 2.776 3.747 4.604 5 1.476 2.015 2.571 3.365 4.032 6 1.440 1.943 2.447 3.143 3.707 7 1.415 1.895 2.365 2.998 3.499 8 1.397 1.860 2.306 2.896 3.355 9 1.383 1.833 2.262 2.821 3.250 10 1.372 1.812 2.228 2.764 3.169 11 1.363 1.796 2.201 2.718 3.106 12 1.356 1.782 2.179 2.681 3.055 13 1.350 1.771 2.160 2.650 3.012 14 1.345 1.761 2.145 2.624 2.977 15 1.341 1.753 2.131 2.602 2.947 16 1.337 1.746 2.120 2.583 2.921 17 1.333 1.740 2.110 2.567 2.898 18 1.330 1.734 2.101 2.552 2.878 19 1.328 1.729 2.093 2.539 2.861 20 1.325 1.725 2.086 2.528 2.845 21 1.323 1.721 2.080 2.518 2.831 22 1.321 1.717 2.074 2.508 2.819 23 1.319 1.714 2.069 2.500 2.807 24 1.318 1.711 2.064 2.492 2.797 25 1.316 1.708 2.060 2.485 2.787 26 1.315 1.706 2.056 2.479 2.779 27 1.314 1.703 2.052 2.473 2.771 28 1.313 1.701 2.048 2.467 2.763 29 1.311 1.699 2.045 2.462 2.756 30 1.310 1.697 2.042 2.457 2.750 32 1.309 1.694 2.037 2.449 2.738 34 1.307 1.691 2.032 2.441 2.728 36 1.306 1.688 2.028 2.434 2.719 38 1.304 1.686 2.024 2.429 2.712 1.282 1.645 1.960 2.326 2.576