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- Home
- How It Works
- Prices
- Samples
- More
- Resources
- A Comprehensive Guide on How to Write a Book Report
- A Simple Guide on How to Write a Lab Report
- A Simple Guide to Writing the Perfect PowerPoint Presentation at Assignment cafΓ©.com
- Assignmentcafe.com Sets the Pace in Academic Writing
- Beginners Guide to Writing a Good Thesis
- Creating a Flawless PowerPoint Presentation in A Few Simple Steps
- How to Excel in Your Coursework at the University
- How to Structure Your Argumentative Essays
- How to Write a Lab Report like An Expert
- How to Write a Lab Report That Will Impress Your Professor
- How to Write an Article Summary That Will Dazzle Your Professor
- How to Write Book Reviews
- Personal Statements That Will Impress the Faculty
- Professional Help for Students Writing Their Thesis
- Writing a Perfect Case Study as Part of Your Academic Work
- Writing a Perfect Case Study as Part of Your Academic Work
- Writing A Speech That Your Lecturers and Fellow Students Will Love and Remember

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Question 1

Consider the following model: π² = ππ + π, known as the Classical Linear Regression Model (CLRM), where y is the dependent variable, X is the set of independent variables, π is the vector of parameters to be estimated and π is the error term.

a) List and discuss the assumptions you need for the Ordinary Least Squares (OLS) to be a Best Linear Unbiased Estimator (BLUE). [25 marks]

b) Derive the OLS estimator and variance and discuss where each assumption is needed for the derivation of the two parameters. [25 marks]

c) Discuss the properties of linearity, unbiasedness, and efficiency, and what assumption you need for each of these properties to hold and show where each assumption is needed for the derivation. [25 marks]

d) Present and discuss the R2 and the adjusted R2. Discuss pros and cons of each of the two statistics. [25 marks]

Question 2

You estimate the following model: πΈπ₯πππ π _πππ‘π’ππΰ―§ = πΌ + π½ππ₯πππ π _πππ‘π’ππ_πΰ―§ + πΰ―§, where ππ₯πππ π _πππ‘π’ππΰ―§ is the difference between return on Asset A and the risk-free rate, ππ₯πππ π _πππ‘π’ππ_πΰ―§ is the difference between return on the market portfolio and the risk-free rate, and πΰ―§ is a random error term. Data are for 174 months. You estimate the model via OLS; results are reported below. OLS estimates using 174 observations Dependent variable: excess return on Asset A Variable Coefficient Std. Error t-Statistic Prob. Ξ± -0.850664 1.195838 -0.711354 0.4796 Excess_return_m 1.148796 0.339127 3.387506 0.0012 R-squared 0.158333 Mean dependent var -0.604042 Adjusted R-squared 0.144535 S.D. dependent var 10.24319 S.E. of regression 9.474063 Akaike info criterion 7.366224 Sum squared resid 5475.230 Schwarz criterion 7.434260 Log likelihood -230.0361 Hannan-Quinn criter. 7.392983 F-statistic 11.47520 Durbin-Watson stat 2.036330 Prob(F-statistic) 0.001240

a) What theory can be associated to the model to be estimated? Discuss. [25 marks]

b) How would you test the hypothesis that the market is in equilibrium? Perform the test at the 5% s.l. and present the logic of the test. Test the hypothesis that the stock is riskier than the market at the 5% s.l. Would you buy the stock if you aim at reducing risk in your portfolio? What issues can you see with this approach? [25 marks]

c) Consider the following augmented CAPM model πΈπ₯πππ π _πππ‘π’ππΰ―§ = πΌ + π½ππ₯πππ π _πππ‘π’ππ_πΰ―§ + πΎπ·πππ‘ΰ―§ + πΰ―§, where π·πππ‘ΰ―§ is the firmβs debt at time t. What is the sign you expect for this variable? Discuss. A regression on 300 observations gives you the value -0.022179 for the estimated parameter for the firmβs debt, and 0.011739 for its standard deviation. Is the variable statistically significant at the 10% s.l.? Are you going to adopt a passive or an active investment strategy based on your result? If your decision builds upon the OLS approach, is there a possibility that you could go for the wrong choice? Discuss. [25 marks]

d) Is the model statistically adequate? What type of analysis would you perform to check whether this is the case? Discuss. [25 marks]

Question 3

a) What are the advantages of estimating the panel data models, if one is available? Would it be reasonable to estimate it using the OLS approach? Discuss. [25 marks]

b) Consider the following fixed effects model: π¦ΰ―ΰ―§ = πΌ + π½π₯ΰ―ΰ―§ + πΰ― + πΰ―ΰ―§, where πΰ― is taken as a fixed effect. What approach can you adopt to estimate the parameters of interest? Discuss. [25 marks]

c) Describe the random effects model and discuss how this model captures the cross-sectional individual heterogeneity. [25 marks]

d) How do you choose between the fixed effects and random effects panel models? Discuss. [25 marks]

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