taxed the same as zero coupons. The only difference is that the holder of a zero-coupon bond does not receive cash payment until maturity. In any case‚ every year the issuer will send a statement telling you how much interest accrued to the bond that year. In the case‚ the bond is amortized semiannually‚ accruing interest and taxes each year. 3. A- The company uses an investment banker as an intermediary to issue shares on the open market because bonds can only be issued to the public through
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EViews -- example 2: the CAPM Appendix: Mathematical derivations of CLRM results 65 67 69 71 74 75 77 81 3 Further development and analysis of the classical linear regression model 3.1 Generalising the simple model to multiple linear regression 3.2 The constant term 3.3 How are the parameters (the elements of the β vector) calculated in the generalised case? 3.4 Testing multiple hypotheses: the F -test 3.5 Sample EViews output for multiple hypothesis tests 3.6 Multiple regression
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extreme outlying circumstances. Question C Zero Conditional Mean Assumption is “a key assumption used in multiple regression analysis that states that‚ given any values of the explanatory variables‚ the expected value of the error equals zero” (Wooldridge‚ … ) . Within the context of the equation listed above‚ the error term[pic]has no relation with any of the explanatory variables “log(lotsize)” and “log(sqrft)‚ in other words [pic]is mean independent of “log(lotsize)” and “log(sqrft). This also
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Econometrics I Yarine Fawaz Exercises Econometrics: Set 1-Correction Computer exercises: 1) Use the data in SLEEP75.RAW from Biddle and Hamermesh (1990) to study whether there is a tradeoff between the time spent sleeping per week and the time spent in paid work. We could use either variable as the dependent variable. For concreteness‚ estimate the model sleep 0 1totwork u ‚ where sleep is minutes spent sleeping at night per week and totwrk is total minutes worked during the
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ECONOMETRICS Bruce E. Hansen c 2000‚ 20101 University of Wisconsin www.ssc.wisc.edu/~bhansen This Revision: January 10‚ 2010 Comments Welcome 1 This manuscript may be printed and reproduced for individual or instructional use‚ but may not be printed for commercial purposes. Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 1 Introduction 1.1 What is Econometrics? . . . . . . . . . . . . 1.2 The Probability
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INTRODUCTORY ECONOMETRICS Lectures 1 & 2 Statistics for Econometrics POPULATION AND SAMPLE Population – the group of ALL people or objects that are under study Sample – a sub-set of the population Parameter – a numerical characteristic of a population 1. Population & Sample Means 2. Expected Values 3. Population & Sample Variances 4. Population & Sample Covariances 5. Population & Sample Correlation Coefficients 6. Estimators Statistic – a numerical characteristic of a sample
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ECONOMETRIC ANALYSIS. INDEX: - Introduction..................................................................................3 -Background....................................................................................8 -Empirical Analysis.........................................................................9 -Conclusion.....................................................................................31 -Bibliography.............................................................
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ECONOMETRICS EXERCISE 3 TRAN THI ANH NGUYET 28 March‚ 2013 1. The data set CEOSAL2.DTA contains information on 177 CEOs. In this sample‚ the average annual salary is $865‚864‚400 with the smallest and largest being $100‚000 and $5‚299‚000‚000‚ respectively. Another most interesting variable is sales with the average being $3‚5329‚463‚000‚ and its the smallest and largest being $29‚000 and $51‚300‚000. Using the data set‚ the following OLS regression is obtained: (1) . ˆ lnsalary = 4.58 + 0
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Hand University of Gothenburg Department of Economics Applied Econometrics (MSc.)‚ Fall 2013 Alpaslan Akay University of Gothenburg This is your second homework. It is a lab that you are going to do it alone again. In the first lab you have learned how to operate Stata and calculate descriptive statistics. You also read a paper with an interesting research question. Self-Lab 2 covers some topics of Lecture 2 and 3. In this lab you are going to learn how to calculate OLS estimator with
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University of Hong Kong Introductory Econometrics (ECON0701)‚ Fall 2013 22 November 2013 Further Issues in Using OLS with Time Series Data • Last time‚ we discussed the assumptions necessary for OLS parameter estimates to be consistent in a time series context. • We also compared these assumptions with the assumptions necessary for OLS to be unbiased. • In general‚ the conditions are more restrictive for OLS to be unbiased than for OLS to be consistent‚ so in a time series context‚ it
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