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Unformatted text preview: (2) = a random variable with a constant distribution with finite mean X' x = ε ε ε ∑ i i i i i i n i i 1 and variance and E[ ]= 0 (3) and statistically independent. Then, = = an observation in a random sample, with constant variance matrix and mean vector 0. 1 converges to its expectat n x z x z ion by the law of large numbers. Part 12: Asymptotics for the Regression Model Consistency of s2 ˜™™™™ ™ 7/38 = = → =  ÷ ÷ ÷ 2 2 1 1 n 1 s n K n K n K n n 1 n K 1 1 1 plims plim plim ( ) n n n 1 1 1 1 plim plim plim ( ) plim n n n n 1 plim n What must be a111 e'e 'M 'M 'M ' 'X X'X X' ' 'X X'X X' ' 0'Q 0 ε ε = ε ε ε ε = ε ε  ε ε = ε ε ε ε = ε ε  ε = σ 2 2 1 ssumed to claim plim = E[ ] ? n ' ε ε Part 12: Asymptotics for the Regression Model Asymptotic Distribution ˜™™™ ™ 8/38 = = + × ε ÷ ÷ ∑ 1 n i i i 1 1 1 n n The limiting behavior of is the same as that of the statistic that results when the moment matrix is replaced by its limit. We examine the behavior of the modified b X'X x b β = + × ε ÷ ∑ n 1 i i i 1 sum 1 n Q x β Part 12: Asymptotics for the Regression Model Asymptotics ˜˜™™™ ™ 9/38 = + × ε ÷ ∑ n 1 i i i 1 1 n What is the mean of this random vector? What is its variance? Do they 'converge' to something? We use this method to find the probability limit. What is the asymptotic distribu Q x β tion? Part 12: Asymptotics for the Regression Model Asymptotic Distributions p Finding the asymptotic distribution p b β in probability. How to describe the distribution? n Has no ‘limiting’ distribution p Variance 0; it is O(1/n) p Stabilize the variance? Var[n b ] ~ σ2Q1 is O(1) p But, E[n b ]= n β which diverges n n ( b β ) a random variable with finite mean and variance. (stabilizing transformation) n b apx. β +1/ n times that random variable ˜˜™™™ ™ 10/38 Part 12: Asymptotics for the Regression Model Limiting Distribution n ( b β ) = n ( X’X )1 X’ε = n ( X’X /n)1( X’ε /n) Limiting behavior is the same as that of n Q1( X’ε /n) Q is a fixed matrix. Behavior depends on the random vector n ( X’ε /n) ˜˜™™™ ™ 11/38 Part 12: Asymptotics for the Regression Model Limiting Normality ˜˜™™™ ™ 12/38 n n i i i i 1 i 1 n i i 1 1 1 1 n n n n n n n 1 Mean of a sample. n Independent observations. Mean converges to zero (plim (1/n) already assumed n a candidate for the LindbergFeller Central L = = = = ε = = = ∑ ∑ ∑ X' x w w ε w X' = 0 ε w = 2 2 i i i 2 2 imit Theorem....
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 Fall '10
 H.Bierens
 Econometrics, Least Squares, Regression Analysis, Variance, regression model, Wald

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