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Econometrics 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
Econometrics Lecture Notes 1
Econometrics Lecture Notes 2
Econometrics Lecture Notes 3
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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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