Self-Lab 2 - Use Your 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
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m Problem1: The demand for roses was estimated using quarterly figures for the period 1971 (3rd quarter) to 1975 (2nd quarter). Two models were estimated and the following results were obtained: Y = Quantity of roses sold (dozens) X2 = Average wholesale price of roses ($ per dozen) X3 = Average wholesale price of carnations ($ per dozen) X4 = Average weekly family disposable income ($ per week) X5 = Time (1971.3 = 1 and 1975.2 = 16) ln = natural logarithm The standard errors
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foresee? Worshiping the False Gods and Beliefs (Efficient Markets Hypothesis‚ Option Pricing Model‚ the Normal Distribution) that Market Prices are Always Right (Whether it is right or wrong‚ The Market is The Market and You cannot trade with computer models)‚ The Fallacy (misleading and erroneous beliefs) of Self-Regulating Markets that both are Self-Adjusting and in a state of Equilibrium (balance & stability) (reflecting back to Adam Smith’s Invisible Hand in the Wealth of Nations published
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Descriptive Statistics Mean Variance Standard Deviation Sample Covariance If it is greater than zero‚ upward sloping. This is scale dependent. Sample Correlation This is scale independent: between -1 and 1‚ close to 1 is upward‚ 0 is central‚ -1 is downward sloping. Finding the regression Regression formula with one regressor Slope Intercept Finding R2 TSS=ESS+SSR The Coefficient of Determination = R2 This gives the total fit of ‚ between 0 (chance) and
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Properties of OLS estimators Population regression line: E(y|x)=1+2x‚ Observation = systematic component + random error: yi = 1 +2 x + ui Sample regression line estimated using OLS estimators: = b1 + b2 x Observation = estimated relationship + residual: yi =+ ei => yi = b1 + b2 x + ei Assumptions underlying model: 1. Linear Model ui = yi - 1- 2xi 2. Error terms have mean = 0 E(ui|x)=0 => E(y|x) = 1 + 2xi 3. Error terms have constant variance (independent of x) Var(ui|x)
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Answers to Selected Exercises For Principles of Econometrics‚ Fourth Edition R. CARTER HILL Louisiana State University WILLIAM E. GRIFFITHS University of Melbourne GUAY C. LIM University of Melbourne JOHN WILEY & SONS‚ INC New York / Chichester / Weinheim / Brisbane / Singapore / Toronto CONTENTS Answers for Selected Exercises in: Probability Primer 1 Chapter 2 The Simple Linear Regression Model 3 Chapter 3 Interval Estimation and Hypothesis Testing
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Hieu Nguyen – FIN 5309 Section 1 Assignment 1 2.3 Table 2.2 X=0 X=1 Total Y=0 0.15 0.07 0.22 Y=1 0.15 0.63 0.78 Total 0.30 0.70 1.00 With W = 3+6X and V = 20-7Y‚ we have: (W|X=0)=3 (W|X=1)=9 Total (V|Y=0)=20 0.15 0.07 0.22 (V|Y=1)=13 0.15 0.63 0.78 Total 0.30 0.70 1.00 a. E(W) = 3 x 0.3 + 9 x 0.7 = 7.2 E(V) = 20 x 0.22 + 13 x 0.78 = 14.54 b. = (3 – 7.2)2 x 0.3 + (9 - 7.2)2 x 0.7 = 7.56 = (20 – 14.54)2 x 0.22 + (13 – 14.54)2 x 0.78 = 8.4084 c. cov
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1. a) reg sleep totwrk b) SLEEP = 3586.38 - 0.15*TOTWRK c) The estimate of B1‚ which is the average that people sleep who do not work in a week‚ is 3586.38 minutes . However‚ each minute that a person works per week‚ reduces sleeping in 0.15 minutes per week. 2. a) - average salary = 865.86 - average tenure = 7.95 b) 5 CEOs are in the first year as CEO c) shown in stata d) ln(SALARY) = 6.51 + 0.0097*CEOTEN e) Every additional year of a person as a CEO position improves the salary 0.97%
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eAUSTRALIAN SCHOOL OF BUSINESS SCHOOL OF ECONOMICS ECON2206 / ECON3290 (ARTS) Introductory Econometrics Course outline SESSION 2‚ 2011 Lecturer in Charge: Dr. Rachida Ouysse Room ASB441 Telephone: 9385 3321 Email: rouysse@unsw.edu.au Lectures: Fridays 9am-11am Venue: Law Theatre G04 Website: http://telt.unsw.edu.au/ TABLE OF CONTENTS 1 STAFF CONTACT DETAILS 1 1 1 1 1 1 2 2 2 2 3 3 3 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 7 9 9 9 10 10 10 12 13 13 13
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STUDENT SOLUTIONS MANUAL Jeffrey M. Wooldridge Introductory Econometrics: A Modern Approach‚ 4e CONTENTS Preface Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Introduction The Simple Regression Model Multiple Regression Analysis: Estimation Multiple Regression Analysis: Inference Multiple Regression Analysis: OLS Asymptotics Multiple Regression Analysis: Further Issues Multiple Regression Analysis With Qualitative Information: Binary (or Dummy) Variables Heteroskedasticity
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