Econometrics-I-1

What are the conclusions σ σ ? ? ? λ

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What are the assumptions?    What are the conclusions? Σ Σ = λ λ λ = = Λ - Λ Λ = + N i=1 i i N i=1 i i i 0.2 Kernel Regression w (z)y ˆ F(z)= w (z) x -z 1 w (z) K .9 / N K(t) (t)[1 (t)] exp(t) (t) 1 exp(t) s ™™ ™    6/21
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Part 1: Introduction Semiparametric Regression p Investmenti,t = a + b*Capitali,t + ui,t p Median[ui,t | Capitali,t]  =  0 = - N a,b i i i 1     Least Absolute Deviations ˆ ˆ ˆ             F(x)=a+bx ˆ ˆa,b=ArgMin |y a-bx | ™™ ™    7/21
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Part 1: Introduction Fully Parametric Regression p Investmenti,t = a + b*Capitali,t + ui,t p ui,t | Capitalj,s  ~ N[0, s 2] for all i,j,s,t p Ii,t|Ci,t  ~ N[a+bCit, s 2] = - - -  ÷ ÷ ÷  N 2 a,b i i i 1 1 N N i= 1 i= 1 i i i     Least Squares Regression ˆ ˆ ˆ               F(x)=a+bx ˆ ˆ   a,b=ArgMin (y a bx ) 1 1 1 = y x x x i ™™ ™    8/21
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Part 1: Introduction Estimation Platforms p The “best use” of a body of data n Sample data n Nonsample information p The accretion of knowledge p Model based n Kernels and smoothing methods (nonparametric) n Moments and quantiles (semiparametric) n Likelihood and M- estimators (parametric) p Methodology based (?) n Classical – parametric and semiparametric n Bayesian – strongly parametric ™™ ™    9/21
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Part 1: Introduction Classical Inference
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What are the conclusions Σ Σ Λ

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