Designing and Delivering Presentations

Module 4 – SLP

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 the Module 4 SLP 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 the Module 4 SLP 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 SLP 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 [http://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.
Privacy Policy | Contact

find the cost of your paper