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

CHAPTER 10 GENERALIZED LEAST SQUARES ESTIMATION Chapter 10 Generalized Least Squares Estimation 10.1 Model

Chapter 1 Introduction 1.1 What is econometrics? Application of st atistical and mathematical met hods to the analysis of economic Purposes 1. Testing economic theories 2. Policy analysis(simultaneous equations model, vector autoregressive model