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View Full DocumentEconometrics 10 1.1. var( ) is inversely proportional to n 1.1.1. the spread (standard deviation) of the sampling distribution is proportional to 1/ 1.1.2. Thus the sampling uncertainty associated with is proportional to 1/ (larger samples, less uncertainty, but square- root law) The sampling distribution of when n is large (note 1-33) For small sample sizes, the distribution of will usually be complicated ( unless . . . what is true about the distribution of the Y i values in the population?) But if n is large, the sampling distribution is simple! 1.1. As n increases, the distribution of becomes more tightly centered around Y (the Law of Large Numbers ) 1.2. Moreover, the distribution of both become normal (the Central Limit Theorem ) 1.2.1. 1.2.2. The Law of Large Numbers : (note 1-34) An estimator is consistent if the probability that its falls within an interval of the true population value tends to one as the sample size increases.... View Full Document
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View Full DocumentEconometrics Lecture Notes 1
Econometrics Lecture Notes 2
Econometrics Lecture Notes 3
Econometrics Lecture Notes 9
Econometrics Lecture Notes 7
Econometrics Lecture Notes 8
Chapters 2 and 3 - review of probability and statistics
ps2_sol
Lecture3_SM
Day 3
Confidence Intervals
Lecture 4 2010
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