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Unformatted text preview: Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Quantitative Economics and Econometrics Sorawoot Srisuma Lecture 2 Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Plan of Today&s Lecture 1. Quick review of distribution theory 2. Inference 3. Introduction to estimation by least square Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Normal Distribution Let X & N & X , 2 X Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Normal Distribution Let X & N & X , 2 X aX + c & N & a X + c , a 2 2 X Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Normal Distribution Let X & N & X , 2 X aX + c & N & a X + c , a 2 2 X Let there be another RV Y & N & Y , 2 Y Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Normal Distribution Let X & N & X , 2 X aX + c & N & a X + c , a 2 2 X Let there be another RV Y & N & Y , 2 Y If Cov ( X , Y ) = then X and Y are independent (generally only & ( is true) Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Related Distribution We will be concerned with 2 , t and F distributions Let Z = X & X X N ( , 1 ) : Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Related Distribution We will be concerned with 2 , t and F distributions Let Z = X & X X N ( , 1 ) : Z 2 2 1 and if you have many i (.i.d.) f Z i g p i = 1 then Z 2 1 + . . . + Z 2 p 2 p Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Related Distribution We will be concerned with 2 , t and F distributions Let Z = X & X X N ( , 1 ) : Z 2 2 1 and if you have many i (.i.d.) f Z i g p i = 1 then Z 2 1 + . . . + Z 2 p 2 p Let W 2 m then Z / q W m has a t m when Z and W are independent Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Related Distribution We will be concerned with 2 , t and F distributions Let Z = X & X X N ( , 1 ) : Z 2 2 1 and if you have many i (.i.d.) f Z i g p i = 1 then Z 2 1 + . . . + Z 2 p 2 p Let W 2 m then Z / q W m has a t m when Z and W are independent Let V 2 n then W m / V n has an F m , n when W and V are independent Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading From Last Week & We have a random sample f X i g N i = 1 (with some unknown distribution) & We are interested in learning about X = E ( X 1 ) & Our estimator is X = X = 1 N N i = 1 X i Recall that: & X is an unbiased estimator for X & Var & X = 2 X = 2 X / N Intro Review of Distribution Theory Inference Intro to Least Square Relevant Reading Hypothesis Testing (Normal)...
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This note was uploaded on 01/27/2011 for the course ECON 2007 taught by Professor Srisuma during the Spring '10 term at University of London.
 Spring '10
 Srisuma
 Economics, Econometrics

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