Module 4 – SLP

Asymmetric Information and Market Outcomes

Links to Estimation Techniques

Tim Shaughnessy, Chapter 7 — Demand Estimation and Forecasting, 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 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 tragedy is that these individuals did not make decisions that were in their companiesâ€™ best 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 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 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 an output table. The Analysis ToolPak is a Microsoft Office Excel add-in program that is available when you install Microsoft Office or Excel. To use the Analysis ToolPak 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 ToolPak check box, and then click OK. (If Analysis ToolPak is not listed in the Add-Ins available box, click Browse to locate it.) If you get prompted that the Analysis ToolPak is not currently installed on your computer, click Yes to install it. After you load the Analysis ToolPak, 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.

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 price, in dollars

2. assess assessed value, 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 Excel the data set. Then, in Excel, obtain the logarithmic transformation of the following variables using the Excel function =LOG( . )

6. lprice log(price) : dependent variable

7. lassess log(assess) : independent variable

8. llotsize log(lotsize) : independent variable

9. lsqrft log(sqrft) : 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 mind that when you interpret a regression coefficient, you are assuming that all the other variables remain constant.

A Note on ANOVA

The ANOVA table is used to test the null hypothesis that all regression coefficients (excluding the intercept term) are equal to zero against 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 denominator degrees of freedom (attached to Residual SS).

Excel computes the F statistic for you in the ANOVA table, and computes 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 the Module 4 SLP Assignment, you are expected to:

Describe the purpose of the paper and provide a conclusion.

Present information in a professional manner.

Answer the SLP Assignment question clearly and provide necessary details.

Write clearly and correctlyâ€”that is, no poor sentence structure, no spelling 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 the references section must be cited in the paper.) Use APA format to provide citations and references [http://owl.english.purdue.edu/owl/resource/560/01/].

Type and double-space the paper.

Whenever appropriate, please use Excel to show supporting computations in an appendix, present economic information in tables, and use the data to answer follow-up questions.

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