FAC SAT 5 138 44 98 1651 26 35 1278 365 47 1650 39 69 57 896 125 135 653 114 11 346 25 21 44 18 99 223 384 141 2021 66 73 10 94 195 70 165 316 355 30 185 101 148 960 284 905 55 445 623 412 1607 26 48 281 195 275 867 37 28 2606 STU 850 954 874 941 1185 874 902 1048 960 930 1142 800 1060 1000 1150 1170 1100 1080 1026 873 1097 990 920 900 1176 930 1037 960 860 1086 1030 1000 1070 858 1180 910 1001 980 1060 930 1124 945 1190 1057 1310 1090 848 1060 1120 1230 883 800 1010 980 1070 1060 1260 843 980 1160 VOL 58 2454 573 2172 31123 295 1131 22571 6554 793 36330 522 1041 1059 16411 1678 2529 19082 3523 207 6781 147 214 764 176 3682 5665 4411 3341 41528 1251 1036 120 2344 2400 1416 4148 9738 5578 505 3724 2387 1900 16750 2833 15762 875 6603 14727 11179 9251 608 656 3892 2987 5148 11240 569 628 34055 11.5 200 70 100 7000 70 125 2200 400 110 6000 58.4 212 400 1888 486 439 1900 155 6.9 509 180 53 100 30 157 475 613.6 483.6 2500 142 210 20 150 300 233.5 235 460.7 1632 93 263 144.5 770 1700 1100 1900 60 1200 1600 1289 1666 15 160 200 263 487 3300 145 205 7377 quarter consumer_expenditure 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 money_stock 214.6 159.3 217.7 161.2 219.6 162.8 227.2 164.6 230.9 165.9 233.3 167.9 234.1 168.3 232.3 169.7 233.7 170.5 236.5 171.6 238.7 173.9 243.2 176.1 249.4 178 254.3 179.1 260.9 180.2 263.3 181.2 265.6 181.6 268.2 182.5 270.4 183.3 275.6 184.3 price lotsize sqrf 300 6126 370 9903 191 5200 195 4600 373 6095 466.275 8566 332.5 9000 315 6210 206 6000 240 2892 285 6000 300 7047 405 12237 212 6460 265 6519 227.4 3597 240 5922 285 7123 268 5642 310 8602 266 5494 270 7800 225 6003 150 5218 247 9425 275 6114 230 6710 343 8577 477.5 8400 350 9773 230 4806 335 15086 251 5763 235 6383 361 9000 190 3500 360 10892 575 15634 209.001 6400 225 8880 246 6314 713.5 28231 248 7050 230 5305 375 6637 265 7834 313 1000 2438 2076 1374 1448 2514 2754 2067 1731 1767 1890 2336 2634 3375 1899 2312 1760 2000 1774 1376 1835 2048 2124 1768 1732 1440 1932 1932 2106 3529 2051 1573 2829 1630 1840 2066 1702 2750 3880 1854 1421 1662 3331 1656 1171 2293 1764 2768 417.5 253 315 264 255 210 180 250 250 209 258 289 316 225 266 310 471.25 335 495 279.5 380 325 220 215 240 725 230 306 425 318 330 246 225 111 268.125 244 295 236 202.5 219 242 8112 5850 6660 6637 15267 5146 6017 8410 5625 5600 6525 6060 5539 7566 5484 5348 15834 8022 11966 8460 15105 10859 6300 11554 6000 31000 4054 20700 5525 92681 8178 5944 18838 4315 5167 7893 6056 5828 6341 6362 4950 3733 1536 1638 1972 1478 1408 1812 1722 1780 1674 1850 1925 2343 1567 1664 1386 2617 2321 2638 1915 2589 2709 1587 1694 1536 3662 1736 2205 1502 1696 2186 1928 1294 1535 1980 2090 1837 1715 1574 1185 1774 Name (Last, First) _________________________________________ Email ID: ____________________@psu.edu ECON 306 Homework 5 Due: Thursday, April 14, 2016 30 points Instructions: Please print out and complete the following assignment writing your answers clearly and showing your work directly on the assignment. Please keep a log of your work in STATA and print out and attach all of your results. Use a highlighter to highlight all of your commands in STATA (this will make it easier for the graders to see your work). Follow directions carefully (underlining or circling where indicated in your STATA output). Be sure to turn the assignment in at the beginning of class on Thursday, April 14. Late homeworks cannot be accepted. 1. We have quarterly data from 1952 to 1956 on consumer expenditure and the stock of money, both measured in billions of current dollars for the U.S. The data can be found in the Excel spreadsheet: Consumer Expenditure and Money Stock.xls. a. Run the following regression in STATA: (1 point) consumer_expenditure = 0 + 1*money_stock b. Report the coefficient and p-value on money_stock. (This coefficient is the multiplier) Is this statistically significant? (2 points) c. Using STATA, plot the residuals (attach this to the assignment). What does the plot of the residuals suggest? (3 points) d. Perform a Durbin-Watson test at the .05 level testing that there is positive serial correlation. Be sure to report the lower and upper limit critical values and test statistic. What is your decision, and what does this mean? (5 points) e. Why does your decision in part d seem reasonable? (2 points) 2. You are looking at data on determinants of housing prices. An example dataset can be found in the Excel spreadsheet: House_price.xls: price = house price, $1000s lotsize = size of lot in square feet sqrft = size of house in square feet We want \"price\" to be our dependent variable and the other variables to be our independent variables; however, we believe that heteroskedasticity may be a concern because of the large range in housing prices. Why would taking the natural log of our variables (creating a double log form) be a possible solution to our problem? Explain. [Hint: If you are not sure, try taking the natural log of the price column to see how it changes the values] (2 points) 3. Suppose we are looking at determinants of the number of books in a school's library. Use Books.xls which has data taken from 60 U.S. university and college libraries: where: VOLi = thousands of books in the ith school's library STUi = the number of students in the ith school FACi = the number of faculty members in the ith school SATi = the average SATs of students in the ith school a) Create a new variable that is a linear combination of students and faculty called TOT where TOT i = 10FACi + STUi (1 point) b) Now run the regression: VOLi = 0 + 1*TOTi + 2*SATi Report the values for the coefficients, standard errors, and p-value (2 points): Coefficients 0 = Standard error P-value 1 = 2 = c) If we want to test for heteroskedasticity using the Park test, which of the independent variables would best serve as the proportionality factor in this case? Explain briefly. (2 points) d) Apply the Park test for heteroskedasticity to the model. Write down the coefficient for the natural log of the proportionality factor and the p-value. (Attach your STATA work)What do you conclude from this test? (4 points) e) Now, apply the White test for heteroskedasticity to the model. Write down the chi-square statistic and the p-value. (Attach your STATA work) What do you conclude? (4 points) f) Re-run the regression from part b using heteroskedastic corrected standard errors. Report your results for the coefficients, standard errors, and p-values: (2 points) Coefficients 0 = 1 = 2 = Standard error P-value