bus530-slp4

Links to Estimation Techniques 

Tim Shaughnessy, Chapter 7 — Demand Estimation and Forecasting, available fromhttps://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
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 correctly—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 [https://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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