BUS 530-Managerial Economics M4S-Asymmetric Information and Market Outcomes

BUS 530-Managerial Economics M4S-Asymmetric Information and Market Outcomes
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ASYMMETRIC INFORMATION AND MARKET OUTCOMES
Lin” rel=”nofollow”>inks to Estimation Techniques
Tim Shaughnessy, Chapter 7 — Demand Estimation and Forecastin” rel=”nofollow”>ing, available from https://www.youtube.com/watch?v=daiTjsnznjM
Matt Kermode, Explanation of Regression Results, Available at https://www.youtube.com/watch?v=c5blVUkkjTM
Jason Delaney, Introduction to Multiple Regression, Available at https://www.youtube.com/watch?v=eLpfEml4Vak
Session Long Project
PART 1
In 2006 the CEO of Bear Sterns, James Caynes, received a compensation package of $34 million. The followin” rel=”nofollow”>ing year Bear Sterns cost $2.7 billion to the taxpayers. In 2006, the CEO of Lehman Brothers received a compensation package of $27 million. On September 15, 2008, Lehman Brothers filed for bankruptcy. The collapse of Lehman Brothers is seen by many as the key event that sparked the Global Fin” rel=”nofollow”>inancial Crisis. In 2006, the CEO of Citigroup, Charles Prin” rel=”nofollow”>ince, received a compensation package of $25 million. Sin” rel=”nofollow”>ince then the stock price has fallen from $50 a share to $3.5 a share. The CEO of Countrywide Fin” rel=”nofollow”>inancial, Angelo Mozilo, did even better. His compensation package was $43 million. Angelo Mozilo and two other top executives were charged by the Security and Exchange Commission (SEC) with fraud. Accordin” rel=”nofollow”>ing to the SEC, from 2005 through 2007, Countrywide Fin” rel=”nofollow”>inancial engaged in” rel=”nofollow”>in an unprecedented expansion of its underwritin” rel=”nofollow”>ing guidelin” rel=”nofollow”>ines and was writin” rel=”nofollow”>ing riskier and riskier loans, which these senior executives were warned might ultimately curtail the company’s ability to sell them. Countrywide Fin” rel=”nofollow”>inancial was the third biggest origin” rel=”nofollow”>inator of subprime mortgages and the nation’s leader in” rel=”nofollow”>in subprime mortgage- backed securities. The tragedy is that these in” rel=”nofollow”>individuals did not make decisions that were in” rel=”nofollow”>in their companies’ best in” rel=”nofollow”>interest.
Why? What went wrong? What caused the relation between the CEO and the stockholders to go so badly awry? Discuss.
PART 2
An important component of this course is experience with analyzin” rel=”nofollow”>ing economic data at the managerial level. The computer is a perfect tool for manipulatin” rel=”nofollow”>ing data and performin” rel=”nofollow”>ing statistical analyses. While the focus of BUS 530 is not on learnin” rel=”nofollow”>ing statistics, this course will utilize and improve your computer skills with a computer assignment designed to illustrate the in” rel=”nofollow”>interconnections between data, in” rel=”nofollow”>information and managerial decisions.
The primary software will be Microsoft Excel and the Excel statistical add-in” rel=”nofollow”>in: Data Analysis. Microsoft Excel 2010 (and previous versions) provides a set of data analysis tools called Analysis ToolPak which you can use to save steps when you develop complex statistical analyses. You provide the data and parameters for each analysis; the tool uses the appropriate statistical macro functions and then displays the results in” rel=”nofollow”>in an output table. The Analysis ToolPak is a Microsoft Office Excel add-in” rel=”nofollow”>in program that is available when you in” rel=”nofollow”>install Microsoft Office or Excel. To use the Analysis ToolPak in” rel=”nofollow”>in Excel, however, you need to load it first. Click the Microsoft Office Button, and then click Excel Options. Click Add-Ins, and then in” rel=”nofollow”>in the Manage box, select Excel Add-in” rel=”nofollow”>ins. Click Go. In the Add-Ins available box, select the Analysis ToolPak check box, and then click OK. (If Analysis ToolPak is not listed in” rel=”nofollow”>in the Add-Ins available box, click Browse to locate it.) If you get prompted that the Analysis ToolPak is not currently in” rel=”nofollow”>installed on your computer, click Yes to in” rel=”nofollow”>install it. After you load the Analysis ToolPak, the Data Analysis command is available in” rel=”nofollow”>in the Analysis group on the Data tab.
In this assignment you are also asked to estimate a market demand or a cost function (your choice) usin” rel=”nofollow”>ing the tools of regression analysis and the regression software outlin” rel=”nofollow”>ined above.
The first data set (demand for housin” rel=”nofollow”>ing) is used to apply the hedonic approach to demand estimation, while the second data set (demand for cigarettes) is used to apply the classical approach. Fin” rel=”nofollow”>inally, the third dataset (cost of electricity) uses a well known dataset to
estimate the cost of electricity production. In all cases the data is cross-sectional data.
The estimation of demand follows two approaches:
? the classical approach, whereby the quantity demanded of a product is explain” rel=”nofollow”>ined by its own price, the prices of related goods (complements and substitutes), in” rel=”nofollow”>income, tastes and preferences, and the size of the population, among others;
? the hedonic approach, whereby the price of an asset (car, house) is explain” rel=”nofollow”>ined by the characteristics of the asset itself (i.e., the price of housin” rel=”nofollow”>ing depends on the number of bedrooms, the number of bathroom, the view from the house (usin” rel=”nofollow”>ing a dummy variable: 1 = view, 0 = no view), the square footage of the house, the square footage of the lot, etc).
PART 2: Assignment
You are given the data on housin” rel=”nofollow”>ing. The data are collected from the real estate pages of the Boston Globe durin” rel=”nofollow”>ing 1990. These are homes that sold in” rel=”nofollow”>in the Boston, MA area. The source of the data is Wooldridge (2009) Introductory Econometrics: A Modern Approach, 4th Edition, Cengage
VARIABLES
1. price price, in” rel=”nofollow”>in dollars
2. assess assessed value, in” rel=”nofollow”>in dollars
3. bdrms number of bedrooms
4. lotsize size of lot, square feet
5. sqrft size of house, square feet
Cut and paste in” rel=”nofollow”>in Excel the data set. Then, in” rel=”nofollow”>in Excel, obtain” rel=”nofollow”>in the logarithmic transformation of the followin” rel=”nofollow”>ing variables usin” rel=”nofollow”>ing the Excel function =LOG( . )
6. lprice log(price) : dependent variable
7. lassess log(assess) : in” rel=”nofollow”>independent variable
8. llotsize log(lotsize) : in” rel=”nofollow”>independent variable
9. lsqrft log(sqrft) : in” rel=”nofollow”>independent variable
DATASET 1
OBSERVATIONS PRICE SQRFT ASSESS BDRMS LOTSIZE 300 2438 349.1 4 6126 370 2076 351.5 3 9903 191 1374 217.7 3 5200 195 1448 231.8 3 4600 373 2514 319.1 4 6095 466 2754 414.5 5 8566 332 2067 367.8 3 9000 315 1731 300.2 3 6210 206 1767 236.1 3 6000 240 1890 256.3 3 2892 285 2336 314 4 6000 300 2634 416.5 5 7047 405 3375 434 3 12237 212 1899 279.3 3 6460 265 2312 287.5 3 6519 227 1760 232.9 4 3597 240 2000 303.8 4 5922 285 1774 305.6 3 7123 268 1376 266.7 3 5642
310 1835 326 4 8602 266 2048 294.3 3 5494 270 2124 318.8 3 7800 225 1768 294.2 3 6003 150 1732 208 4 5218 247 1440 239.7 3 9425 275 1932 294.1 3 6114 230 1932 267.4 3 6710 343 2106 359.9 3 8577 477 3529 478.1 7 8400 350 2051 355.3 4 9773 230 1573 217.8 4 4806 335 2829 385 4 15086 251 1630 224.3 3 5763 235 1840 251.9 4 6383 361 2066 354.9 4 9000 190 1702 212.5 4 3500 360 2750 452.4 4 10892 575 3880 518.1 5 15634 209 1854 289.4 4 6400 225 1421 268.1 2 8880 246 1662 278.5 3 6314 713 3331 655.4 5 28231 248 1656 273.3 4 7050 230 1171 212.1 3 5305 375 2293 354 5 6637
265 1764 252.1 3 7834 313 2768 324 3 1000 417 3733 475.5 4 8112 253 1536 256.8 3 5850 315 1638 279.2 4 6660 264 1972 313.9 3 6637 255 1478 279.8 2 15267 210 1408 198.7 3 5146 180 1812 221.5 3 6017 250 1722 268.4 3 8410 250 1780 282.3 4 5625 209 1674 230.7 4 5600 258 1850 287 4 6525 289 1925 298.7 3 6060 316 2343 314.6 4 5539 225 1567 291 3 7566 266 1664 286.4 4 5484 310 1386 253.6 6 5348 471 2617 482 5 15834 335 2321 384.3 4 8022 495 2638 543.6 4 11966 279 1915 336.5 4 8460 380 2589 515.1 4 15105 325 2709 437 4 10859 220 1587 263.4 3 6300 215 1694 300.4 3 11554
240 1536 250.7 3 6000 725 3662 708.6 5 31000 230 1736 276.3 3 4054 306 2205 388.6 2 20700 425 1502 252.5 3 5525 318 1696 295.2 4 92681 330 2186 359.5 3 8178 246 1928 276.2 4 5944 225 1294 249.8 3 18838 111 1535 202.4 4 4315 268 1980 254 3 5167 244 2090 306.8 4 7893 295 1837 318.3 3 6056 236 1715 259.4 3 5828 202 1574 258.1 3 6341 219 1185 232 2 6362 242 1774 252 4 4950
Please keep in” rel=”nofollow”>in min” rel=”nofollow”>ind that when you in” rel=”nofollow”>interpret a regression coefficient, you are assumin” rel=”nofollow”>ing that all the other variables remain” rel=”nofollow”>in constant.
A Note on ANOVA
The ANOVA table is used to test the null hypothesis that all regression coefficients (excludin” rel=”nofollow”>ing the in” rel=”nofollow”>intercept term) are equal to zero again” rel=”nofollow”>inst the alternative hypothesis that at least one is different from zero. This test is known as the F test for regression. The F test is computed as follows, under the assumption that the null hypothesis is true:
The F statistics has two sets of degrees of freedom: numerator (attached to the Regression SS) and denomin” rel=”nofollow”>inator degrees of freedom (attached to Residual SS).
Excel computes the F statistic for you in” rel=”nofollow”>in the ANOVA table, and computes in” rel=”nofollow”>in the last column the level of significance (p-value). If the level of significance of the test is less than 5%, you will reject at the 5% level the null hypothesis that all regression parameters are zero. On the other hand, if the level of significance is greater than 5%, you will accept (i.e., fail to reject) the null hypothesis that all regression parameters are zero. SLP Assignment Expectations
In this Assignment, you are expected to:
? Describe the purpose of the paper and provide a conclusion.
? Present in” rel=”nofollow”>information in” rel=”nofollow”>in a professional manner.
? Answer the Assignment question clearly and provide necessary details.
? Write clearly and correctly—that is, no poor sentence structure, no spellin” rel=”nofollow”>ing and grammar mistakes, and no run-on sentences.
? Provide citations to support your argument and place references on a separate page. (All the sources that you listed in” rel=”nofollow”>in the references section must be cited in” rel=”nofollow”>in the paper.) Use APA format to provide citations and references [https://owl.english.purdue.edu/owl/resource/560/01/].
? Type and double-space the paper.
? Whenever appropriate, please use Excel to show supportin” rel=”nofollow”>ing computations in” rel=”nofollow”>in an appendix, present economic in” rel=”nofollow”>information in” rel=”nofollow”>in tables, and use the data to answer follow-up questions.

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