ASYMMETRIC INFORMATION AND MARKET OUTCOMES LINKS TO ESTIMATION TECHNIQUES

27 Jul ASYMMETRIC INFORMATION AND MARKET OUTCOMES LINKS TO ESTIMATION TECHNIQUES

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 companies’ 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