#### SLP MOD 4 BUS306 ECON

**Paper , Order, or Assignment Requirements**

Module 4 – SLP

ASYMMETRIC INFORMATION AND MARKET OUTCOMES

Links to Estimation Techniques

Tim Shaughnessy, Chapter 7 — Demand Estimation and Forecasting, available from https:llwww. voutube.comlwatch?v=daiTisnznjM

Matt Kermode, Explanation of Regression Results, Available at

https:l/www. voutube. com/watch ?v=c5b/VUkkjTM

Jason Delaney, Introduction to Multiple Regression, Available at

https:llwww.voutube.com/watch?v=eLpfEm/4Vak

Session Long Project PART1

In 2006 the CEO of Bear Sterns, James Caynes, received a compensation package of $34 million. The following 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 Financial Crisis. In 2006, the CEO of Citigroup, Charles Prince, received a compensation package of $25 million. Since then the stock price has fallen from $50 a share to $3.5 a share. The CEO of Countrywide Financial, 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. According to the SEC, from 2005 through 2007, Countrywide Financial engaged in an unprecedented expansion of its underwriting guidelines and was writing riskier and riskier loans, which these senior executives were warned might ultimately curtail the company’s ability to sell them. Countrywide Financial was the third biggest originator of subprime mortgages and the nation’s leader in subprime mortgage- backed securities. The

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￼

￼tragedy is that these individuals did not make decisions that were in their companies’ best interest. Why? What went wrong? What caused the relation between the CEO and the stockholders to go so badly awry? Discuss.

PART2

An important component of this course is experience with analyzing economic data at the managerial level. The computer is a perfect tool for manipulating data and performing statistical analyses. While the focus of BUS 530 is not on learning statistics, this course will utilize and improve your computer skills with a computer assignment designed to illustrate the interconnections between data, information and managerial decisions.

The primary software will be Microsoft Excel and the Excel statistical add-in: Data Analysis. Microsoft Excel 2010 (and previous versions) provides a set of data analysis tools called Analysis TooiPak 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 an output table. The Analysis TooiPak is a Microsoft Office Excel add-in program that is available when you install Microsoft Office or Excel. To use the Analysis TooiPak 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

the Manage box, select Excel Add-ins. Click Go. In the Add-Ins available box, select the Analysis TooiPak check box, and then click OK. (If Analysis TooiPak is not listed in the Add-Ins

available box, click Browse to locate it.) If you get prompted that the Analysis TooiPak is not currently installed on your computer,

click Yes to install it. After you load the Analysis TooiPak, the Data Analysis command is available in the Analysis group on the

Data tab.

In the Module 4 SLP assignment you are also asked to estimate a market demand or a cost function (your choice) using the tools of regression analysis and the regression software outlined above.

The first data set (demand for housing) 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. Finally, 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.

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￼The estimation of demand follows two approaches:

- the classical approach, whereby the quantity demanded of a product is explained by its own price, the prices of related goods (complements and substitutes), income, tastes and preferences, and the size of the population, among others;
- the hedonic approach, whereby the price of an asset (car, house) is explained by the characteristics of the asset itself (i.e., the price of housing depends on the number of bedrooms, the number of bathroom, the view from the house (using 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 housing. The data are collected from the real estate pages of the Boston Globe during 1990. These are homes that sold in the Boston, MA area. The source of the data is Wooldridge

(2009) Introductory Econometrics: A Modern Approach, 4th Edition, Cengage

VARIABLES 1. price

- assess 3. bdrms

price, in dollars

assessed value, in dollars

- lotsize

number of bedrooms size of lot, square feet size of house, square feet

- sqrft

Cut and paste in Excel the data set. Then, in Excel, obtain the logarithmic transformation of the following variables using the Excel function =LOG( . )

- lprice 7. lassess 8. llotsize 9. lsqrft

log(price) :dependent variable log(assess): independent variable

DATASET 1

log(lotsize) : independent variable

log(sqrft) : independent variable

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￼OBSERV A TIONS PRICE SQRFT

ASSESS

BDRMS

LOTSIZE

300 2438 370 2076 191 1374 195 1448 373 2514 466 2754 332 2067 315 1731 206 1767 240 1890 285 2336 300 2634 405 3375 212 1899 265 2312 227 1760 240 2000 285 1774 268 1376 310 1835 266 2048 270 2124

349.1 351.5 217.7 231.8 319.1 414.5 367.8 300.2 236.1 256.3

4 6126 3 9903 3 5200 3 4600 4 6095 5 8566 3 9000 3 6210 3 6000 3 2892 4

314 416.5 434 279.3 287.5 232.9 303.8 305.6 266.7 326 294.3 318.8

6000 5 7047 3 12237 3 6460 3 6519 4 3597 4 5922 3 7123 3 5642 4 8602 3 5494 3 7800

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225 150 247

1768 1732 1440

294.2 208 239.7

3 4 3 3

6003 5218 9425

275 230 343

1932 1932 2106

294.1 267.4 359.9

3 3 7

6114

6710

8577

477 350 230

3529 2051 1573 2829

478.1 355.3 217.8

4 4 4

8400 9773 4806 15086

335 251 235

1630

385 224.3 251.9

361

1840 2066 1702

354.9 212.5 452.4

3 4 4

5763 6383 9000

190 360 575

2750 3880 1854

518.1 289.4 268.1

4 4 5

3500 10892 15634

209 225 246

1421 1662 3331

278.5 655.4 273.3

4 2 3

6400 8880 6314

713 248 230

1656 1171 2293 1764

212.1 354

5 4 3

28231 7050 5305

375 265 313

2768

252.1 324

5 3 3

6637

7834

1000

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￼

￼417

253

315

264

255

210

180

250 1722 268.4 250 1780 282.3 209 1674 230.7

3733 475.5 1536 256.8 1638 279.2 1972 313.9 1478 279.8 1408 198.7 1812 221.5

4 8112 3 5850 4 6660 3 6637 2 15267 3 5146 3 6017 3 8410

258 1850 287 289 1925 298.7 316 2343 314.6 225 1567 291 266 1664 286.4 310 1386 253.6 471 2617 482 335 2321 384.3

4 5625 4 5600 4 6525 3 6060 4 5539 3 7566 4 5484

495 2638 543.6

279 1915 336.5

380 2589 515.1

325 2709 437

220 1587 263.4

215 1694 300.4 3

11554

https://tlc.trident.edu/content/enforced/76908-BUS530-JUN2016FT-1/DW4Mod%20-%20… 7/17/2016

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6 5348 5 15834 4 8022 4 11966 4 8460 4 15105 4 10859 3 6300

￼240 1536 725 3662 230 1736 306 2205 425 1502 318 1696

250.7 708.6 276.3 388.6 252.5 295.2 359.5

3 6000 5 31000 3 4054 2 20700 3 5525 4 92681 3 8178

330 2186 246 1928 225 1294 111 1535 268 1980 244 2090 295 1837 236 1715 202 1574

276.2 249.8 202.4

4 5944 3 18838 4 4315 3 5167 4 7893 3 6056 3 5828

219 1185 242 1774

258.1 232 252

3 6341 2 6362 4 4950

254 306.8 318.3 259.4

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￼Ifyouchoosethehousingdataset(DATASET 1),runthe followinghedmc priceregression usmgthe Excel add-in Data Analysis:

log(price)=Po+Ptlog(asses.r)+P2 1ogq’atsizt9+P31og~qifl)+Pi>edraams+e

Ml.~J.~ e is an error term, and the variables andtheir logarithmic transforrn:tions are defined

above. Read the backgroundmaterial, run theiWltipleregression ottlinedabove :mdthen “‘rte a 3 to 4 page repart(and attaChthe Excel printout) answering the following questions:

- • • • • •

What is the R-square of the regression? What does it mean?

Ooi!s assessment matters? Test the significance of Pt

Ooi!s tile size of the lot matter? Test the significance of P1

Ooi!s the size of the house matter? Test the significance of P3

Ooi!s the number of bedrooms matter? Test the significance of P-.

Canyouforecast(predic.t)whathappenstotheaveragepriceofahouseifthenumberof bedrooms increases from3 to4 (keeping everything else constantl?

Looking at tile ANal/A table, can you conclude the independent variables jointly affect the average housing price?

- •

Do you find any anomaly in the results? That is, any result that may not make sense to you? Do you tnink that it would be interesting to matc,fl the information on houses witn other information, such as local crime rates, quality ofthe local schools, pollution levels, and so on. and estimate theeffectsofsuch variables on housing prices?