ec41lecture10 (1)

ec41lecture10 (1) - Statistics for Economists Lecture 10...

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Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Statistics for Economists Lecture 10 Kata Bognar UCLA October 28, 2010

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Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Announcements Homework 3 solutions will be posted this evening Homework 4 will be posted this evening Class feedback Practice problems are online
Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Midterm 2 - Announcements The midterm covers Chapter 2.2 - 2.5, 3.2, 3.4, 3.6 - 3.7 and the lecture notes. The midterm is on Thursday, November 4 at 11:00am in MOORE 100 and FRANZ 1178. Photo IDs will checked so please bring your ID . You will have 60 minutes to answer multiple choice and short answer questions. The graded midterms can be viewed during the TA’s oﬃce hours. No notes or books are permitted in the exam. You may bring a calculator but no cell phones, laptops, etc. is allowed. No own paper is needed. All answers need to be written on the handed out midterm forms.

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Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Last Lecture Estimation Maximum likelihood estimator
Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Today’s Outline 1 Continuous random variables 2 Normal distribution 3 Readings: TH, Chapter 3.2, 3.4 Suggested readings: WS, Chapter 6.1 - 6.2 W, Chapter 6.1 - 6.4 4 Readings for next class: TH, Chapter 3.6 - 3.7

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Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Properties of Point Estimators If the expected value of the estimator is equal to the parameter then the estimator is unbiased . The standard deviation of an estimator is called the standard error of the estimator. If the standard error of one unbiased estimator is smaller than the standard error of an other unbiased estimator then the ﬁrst estimator is more eﬃcient . If the standard deviation of the estimator approaches zero as the sample size increases then the estimator is consistent .
Statistics for Economists Lecture 10 Kata Bognar UCLA Continuous Random Variables Normal Distribution Standard Normal Distribution Properties of Point Estimators - Example Consider a sample of size 100 from a discrete distribution with an unknown mean and a known standard deviation, σ. Estimate the mean of the distribution using (i) the ﬁrst element of the sample, X 1 , (ii) the sample mean, ¯ X both X 1 and ¯ X are point estimators both estimators are unbiased ¯ X is more eﬃcient than X 1 ¯ X is consistent.

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Statistics for Economists Lecture 10
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This note was uploaded on 12/28/2010 for the course ECON 41 taught by Professor Guggenberger during the Fall '07 term at UCLA.

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ec41lecture10 (1) - Statistics for Economists Lecture 10...

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