Review Problems for Midterm 1 Least Squares 1. In the linear regression model

Chapter 7 Functional Form and Structural Change 7.1 Using binary variable Example 1 Earnings equation for married women

Chapter 6 Large Sample Inference and Prediction 6.1 Large sample inference Consider the null hypothesis

Chapter 5 Large sample properties of the LSE 5.1 Stochastic convergence Suppose that Xn} is a sequence of random varia bles with a corresponding sequence of distribution functions{Fn} If Fn(x)(x) at every continuity point x of F, Fn is said to converge weakly to F, written FnF. In this case,{xn} is said to converge in distribution to where

Chapter 4 Finite-Sample properties of the LSE Finnite-sample the n is assumed to be fixed normal dist n assumed Large-sample theory n is sent to oo, general distn assumed

Chapter 3 Least Squares Methods for Estimating B Methods for estimat ing B Least squares estimation Maximum like lihood estimation Met hod of moments est imation Least a bsolute deviat ion est imation

Chapter 3 Least Squares Methods for Estimating B Methods for estimat ing B Least squares estimation Maximum like lihood estimation Met hod of moments est imation Least a bsolute deviat ion est imation 3.1 Least squares estimation The criterion of the least squares estimation is

Chapter 2 The Classical Multiple Linear Regression Model 2.1 Linear Regression Model Notation dependent variable, regressand

Chapter 12 Time Series Analysis 12.1 Stochastic processes A stochastic process is a family of random variables {Xt,t ET}. Example{St,t 0, 1,2,...} where St i=o X; and iid(0,2). St has a different distribution at each point t

Chapter 11 Heteroskedasticity 11.1 White's test for heteroskedasticity For a model with het eroskedasticity